Abstract:
An energy analysis system provides valuable input into building energy expenditures. The system assists with obtaining a detailed view of how energy consumption occurs in a building, what steps may be taken to lower the energy footprint, and executing detailed energy consumption analysis. The analysis may include, as examples, a balance point pair analysis to determine either or both of a heating balance point and a cooling balance point, an exception rank analysis to identify specific data (e.g., energy consumption data) in specific time intervals for further review, or other analysis. The system may display the analysis results on a user interface.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to U.S. provisional patent application Ser. Nos. 61/176,855 and 61/176,790, both filed 8 May 2009 and titled Building Sensor Operational Data Analysis for Energy Saving System Control. 
     BACKGROUND OF THE INVENTION 
     1. Technical Field 
     This disclosure relates to obtaining and analyzing building energy data. This disclosure also relates to engaging in an energy analysis to determine analysis results, and optionally responsively controlling building systems, such as lighting, heating, air-conditioning, and other energy consuming systems. 
     2. Related Art 
     Energy consumption, monitoring, and management are crucial components of sustainable, eco-friendly infrastructures now and into the future. In the past, energy monitoring systems, such as those available from Sensus Machine Intelligence, have obtained and analyzed energy data from individual pieces of equipment in a building. A need exists to provide energy data focused analysis results to accurately determine building energy expenditures, performance and costs. 
     SUMMARY 
     An energy analysis system provides energy analysis results. The energy analysis system may include a processor, a communication interface coupled to the processor, and a memory coupled to the processor. The memory may include energy analysis logic that, when executed by the processor, causes an energy analysis system to: establish a data connection from a network operations center through an energy data connectivity interface to an energy data source, obtain energy data at the network operations center through the data connection to the energy data source, and perform an energy analysis in the network operations center on the energy data to produce an analysis result. 
     The energy analysis may include: determining building comparison baseline data within the energy data, determining actual consumption data within the energy data, and determining, as the analysis result, an exception rank by determining a comparison standard deviation of the energy data within a time interval in the building comparison baseline data, and comparing the comparison standard deviation and the actual consumption data. The exception rank may identify specific data in the actual consumption data for further review. 
     As another example, the energy analysis may include: determining a balance point set from the energy data as the analysis result. The balance point set may include both a heating balance point and a cooling balance point. The system may display any analysis result in a user interface on a display. 
     Other systems, methods, features and advantages will be, or will become, apparent to one with skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the invention, and be protected by the following claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The systems and methods may be better understood with reference to the following drawings and description. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. Moreover, in the figures, like referenced numerals designate corresponding parts throughout the different views. 
         FIG. 1  shows an example of a development framework for a system for building energy analysis. 
         FIGS. 2A and 2B  show a system architecture for building energy analysis. 
         FIG. 3  shows an example of a particular machine for implementing the system for building energy analysis. 
         FIG. 4  shows balance point determination logic. 
         FIG. 5  shows a kilowatt hour consumption and exception ranks analysis. 
         FIG. 6  shows comparison logic for determining exception ranks. 
         FIG. 7  shows alert logic for determining building level alerts. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  shows an example of a development framework  1000  for a system for building energy analysis. The following may be undertaken with regard to developing the system: 
     Create the strategy and roadmap for co-developing the Energy Management Framework (EMF). 
     Internally pilot the SMART Buildings platform to be the proving grounds of the EMF and serve as the credential for the EMF and its premier energy management application. 
     Co-develop the EMF to lead the industry in defining a unique platform to develop, deployed, and manage energy related applications. 
     Deploy the EMF using, e.g., cloud services OS to manage advanced engineering calculations and data management requirements. 
     Investigate leading technologies for building system connectivity and integration and help scale additional drivers, scalability, and security with a platform for any company to leverage. 
     Further define the strategy of the EMF to leverage additional technologies as the market develops. 
     Integrate into existing Energy systems and technologies. 
     Align with utility companies to leverage specific solutions as part of the world&#39;s energy/carbon dashboards (SMART Grid, INDE, demand response programs, carbon tracking/management/trading, SMART City). 
       FIGS. 2A and 2B  show an architecture  1100  for the system. The system may provide an energy data focused approach based on data management and analytics of existing systems and equipment versus a capital intensive approach. The system may connect and integrate in a hardware and software agnostic way with multiple vendor solutions &amp; protocols. The system may provide an open IP-based two way (read/write) infrastructure connecting one or more buildings in a portfolio to a network operations center with web-based control capabilities. The system may, as examples: 
     1) deliver continuous re-commissioning through setpoint control and schedule optimization; 
     2) deliver 24 hours a day×7 days a week (24×7) automated equipment fault detection and diagnosis down to one-minute intervals and prioritized by user defined options: rank, severity, cost of fault, location, and others; 
     3) establish operational guidelines for setpoints and schedules across all equipment and BAS data for a portfolio of buildings, managed through a central command center; 
     4) deliver measurable calculated monthly results tracking energy reductions: (10% min in year 1); 
     5) integrate to existing Maintenance Repair &amp; Operations applications and Energy Management systems; 
     6) include the ability to install, connect, and integrate additional meters, sub-meters, sensors into the common platform as required; 
     7) be supported by world class project management, change management, and training organization; and 
     8) be delivered on a scalable architecture to easily and securely scale with the client&#39;s needs. 
     With reference still to  FIGS. 2A and 2B , the architecture  1000  includes: 
     At the building (e.g., company facility): middleware  1102  provides a connectivity interface for connecting to one or more meters and sub-systems in a facility. The meters may include utility meters as well as any Building Automation System (BAS), lighting or security control system, or other systems. 
     Site Web Services  1104 : The middleware  1102  communicates the data it collects to the System Network Operations Center (NOC)  1106 . The middleware  1102  may received the energy data from web services  1104  installed onsite at any desired facilities. In other words, the web services  1104  may establish a data connection to the middleware  1102  and send building energy data to the middleware  1102 . However, any other type of connectivity interface as a data transmission mechanism may be employed to communicate data to the middleware  1102 , including file transfer, message passing (whether web services based or not), shared memories, or other data transfer mechanisms. 
     System NOC  1106 : The NOC  1106  may use a Service-Oriented Architecture (SOA) to aggregate data across all facilities. Its core services provide analytics and other data management services to the user via a Web-based portal, or Rich Internet Application (RIA). Examples of the specific implementation of the NOC  1106  and the analysis results that the NOC  1106  may provide are discussed below, and in particular with regard to  FIGS. 3-6 . 
     External Data sources  1108 : In addition, the NOC collects relevant data from external data sources, such as the National Weather Service, and may also obtain reports on regional energy prices and 3rd-party or company systems. 
     Additional Services  1110 : Because of the versatility of the system  100  infrastructure, additional services may be layered onto the core. The middleware  1102  has 2-way communication that supports Demand Response programs. Additional services also include a collaborative social network in which company facility managers and operators can share information on operations. They may also drive LEED certification of company buildings, perform carbon tracking and mitigation services, and others. 
     The system energy management data services offer a unique approach at delivering a comprehensive view of a facility&#39;s operations. The system may implement continuous optimized control through real time/interval data acquisition and analysis of all relevant facility data. The System Enterprise Energy Management System (EEMS) may include or involve: (1) a physical site assessment, (2) historical utility bill analysis, (3) utility meter interval data analysis, (4) holistic facility controls analysis, (5) real-time automated equipment fault detection and (6) energy sourcing and demand-response energy management. From these inputs the System energy management system generates insight in the form of reports, dashboards, and alerts that provide actionable information that leads to realized energy reduction and cost savings. 
     The system may begin with a detailed audit of the facility premises. Facilities are surveyed to fully document machine type, layout and building structure, operating hours, building automation capabilities and potential need for additional metering and sub-metering. Specific care is given to observe the unique properties of each building. The walkthrough provides critical baseline information on the premises&#39; layout, engineering, and operational health. 
     Historical utility bill analysis is the next phase of the system implementation. The historical utility bill analysis provides an in-depth look at utility bill trends over time, including general or seasonal trends energy trends. This information is crucial to understanding the way a building has been operating so energy saving opportunities can be recognized. It also provides a benchmark against which later energy saving measures can be compared. Additionally, it is common to find billing errors during this phase, which are immediate opportunities for savings. 
     Meter interval data analysis is a third phase of the system solution. To obtain data about energy consumption, an energy data middleware  1102  is installed and connected to on-site utility meters. The middleware  1102  is used to collect utility data from each of the meters and sub-meters in a building, including the building automation system. This data is then cleansed and collected to get a consumption breakdown by building, section or floor and can be viewed in the robust EEMS tool. Much like the billing analysis, this information is used to spot trends and benchmark future energy saving strategies. 
     The middleware  1102  extracts data from each building system and piece of equipment in the building. This data is tracked over time to observe subtle features in the way the equipment works and the building operates as a whole. As months and even years of data are collected, macro-scale trends related to seasonality, occupancy, and utilization rates all emerge. These trends help contextualize power usage and other metrics, allowing for even greater insight into building operations and further opportunities for savings. 
     The system solution utilizes data captured by the middleware  1102  on a per-minute basis. This fine level of granularity facilitates the System solution to identify real-time trends and problems which were previously undetectable. Additionally, it provides real-time actionable reporting that prioritizes problems and suggests a tangible cost to their continued neglect or systematic inefficiencies. Automatic fault detection begins with as little as three weeks of collected data, and continues for the life of the System contract. 
     The middleware  1102  provides for bi-directional (read/write) capability with any integrated system. This facilitates for 24/7 continuous optimized control of the systems connected to it. The middleware  1102  has an intelligence layer allowing for full closed loop advanced math and logic between any of the previously disparate systems. The middleware  1102  also sends and consumes Web services. An example Web service would be an Automated Demand Response (ADR) notice and pricing level signal from Constellation New Energy (CNE) triggering the middleware  1102  on board logic and control to automatically shed electrical loads by turning off non essential lighting and changing set points on chilled water and HVAC zones. 
     Thus, the system and NOC  1106  may establish a data connection from the network operations center  1106  through an energy data middleware  1102  to an energy data source  1104 . They may then obtain energy data at the network operations center  1106  through the data connection to the energy data source  1104 . The system and NOC  1106  may also perform an energy analysis in the network operations center  1106  on the energy data to produce an analysis result, as described below, for example in connection with  FIGS. 3-6 . 
     Another component of the system solution is the strategic energy sourcing and demand-response energy planning. Energy sourcing experts will help each facility to find the lowest cost and/or greenest power available in that market. Green options include offsetting a facility&#39;s energy requirements with renewable sources of energy, including wind, small hydroelectric, landfill gas, and biomass, providing an electricity choice that can directly support the building of new renewable power plants. The system demand response solution is an additional service that can provide direct revenue to a facility. Facilities sign up with the regional ISO&#39;s demand response program and receive annual payment for participation, in exchange for agreeing to shed load an agreed number of times. System helps to engineer a demand response solution that minimizes impact on facility operations. Both energy sourcing and demand response are administered to meet regional regulations while delivering value to the customer. 
     The system solution achieves cost savings on several fronts. Most notably, savings are realized through reductions in total energy consumption and decreases in both scheduled and unscheduled maintenance. 
     Energy savings occur when building control sequences are optimized to run in the most economic fashion. Wasteful situations like running an unoccupied building at/near full capacity are easily eliminated. More subtle improvements can be identified through system analytic solutions which identify mis-configured and even machines needing repairs. The system solution&#39;s automatic fault detection and diagnosis tool not only provides a clear understanding of a problem, but suggests a financial consequence with letting the problem go unresolved. The EEMS&#39; reporting gives a level of detail which requires less man-hours to investigate—freeing the facility team to address the most promising energy saving opportunities rather than simply assessing the state of the building. 
     Efficient building operations leads to decreases in scheduled maintenance costs. This increases the lifespan of the equipment and decreasing the replacement frequency, thus allowing a facility to optimize its operations with the equipment it has, rather than recommending new capital purchases. Unscheduled maintenance costs also decrease based on detection of inefficient machines. Some machines may achieve the desired end result (say, cooling a room to 72 degrees), but do so in a highly inefficient manner which cuts its lifespan by as much as half. As an example, a machine which is operating in both heating and cooling mode may be able to achieve a final temperature of 72 degrees by mixing hot and cold air. However, this is highly inefficient and could cause the machine to break unexpectedly. 
     Altogether, the system solution provides direct, continuous, and reliable energy savings without requiring major capital purchases. The system solution helps a facility makes the best use of the equipment it already has, by optimizing energy consumption and maximizing the lifespan of the equipment. Through its data-driven solution, the system provides an approach that is unique in the marketplace for bringing an information-technology-driven business intelligence capability to the world of facility energy management. 
       FIG. 3  shows one example of a particular machine  2500  that implements a system for building energy analysis. The machine  2500  may implement all or part of the network operations center  1106 , for example. The machine  2500  includes a processor  2502 , a memory  2504 , and a display  2506 . A communication interface  2508  connects the machine  2500  to energy data sources  2510 , such as building sensors, utility company meters, weather centers, mediator devices (e.g., a Richards-Zeta Mediator), and other data sources. The display  2506  may present reports  2512 , such as those described above or below, either locally or remotely to an operator interacting with the machine  2500 . 
     The machine  2500  also includes an energy database  2514 . The energy database  2514  may store any data that the machine  2500  processes. As examples, the energy database  2514  may store sensor samples  2516  (e.g., samples of energy consumption or performance of building energy consuming devices), energy metrics  2518  (e.g., measured or computed metrics, optionally based on energy KPIs), utility bill data  2520  (e.g., cost per unit energy, energy consumed, total cost, and date), weather data  2522  (e.g., temperature ranges, dates, expected temperature or temperature variations at any desired interval), or other data that helps the machine  2500  analyze energy consumption, cost, or history. 
     The memory  2504  may store program instructions or other logic for execution by the processor  2502 . For example, the memory  2504  may store energy analysis programs  2524  and energy reporting programs  2526 . The energy analysis programs  2524  may gather, analyze, and otherwise process the sensor samples  2516  or other energy data (e.g., to produce control recommendations for building systems or identify data points for further study and analysis). The energy reporting programs  2526  may generate user interfaces including dashboards, charts, graphs, text displays, or other reporting information. 
     The machine  2500  may perform an energy analysis (e.g., as part of the network operations center) on the energy data in the energy database  2514  to produce an analysis result. To that end, as one example, the machine  2500  may include in the memory  2504  balance point determination logic  2528  (e.g., as one of the energy analysis programs  2524 ). The balance point determination logic  2528  may search for one balance point, or a set of multiple balance points (e.g., a pair of balance points) in the energy data. In one implementation, the energy data that is searched are triples of: date, temperature, and measured consumption data  2546 , obtained from one or more energy data sources over any desired date range, such as from a building under analysis and a weather information center. 
     The balance points may include a Heating Balance Point (HBP)  2530  and a Cooling Balance Point (CBP)  2532  to help identify the number of heating degree days (HDDs) and cooling degree days (CDDs). The HBP  2530  may be interpreted as the temperature above which the building is not heating, while the CBP  2532  may be interpreted as the temperature below which the building is not cooling. In one model, the building is neither heating nor cooling between the HBP  2530  and the CBP  2532 . Thus, identifying both the HBP  2530  and CBP  2532  may significantly increase the accuracy of the count of the number of HDDs and CDDs for the building, particularly as compared to finding a single balance point for both heating and cooling, or compared to assuming a standard and usually inaccurate balance point (e.g., 65 degrees F.). The increased accuracy in the determination of HDDs and CDDs has positive effects in downstream technical analyses, such as obtaining a more accurate determination of the weather load on the building, and therefore a more accurate regression model of how the building responds to weather load. The more accurate regression model means more accurate measurement and verification of savings obtained when energy management strategies are implemented based on the downstream analyses. 
     In particular, the balance point determination logic  2528  may implement the logic  2600  shown in  FIG. 4 . The logic  2600  may be implemented as executable instructions for the processor  2502 , to determine either or both of a HBP  2530  and a CBP  2532  (i.e., as a computer implemented method). The balance point determination logic  2528  may obtain ( 2602 ) balance point parameters  2534  including, as examples, those shown below in Table Balance Point Parameters. 
     The balance point determination logic  2528  may obtain the balance point parameters  2534  from operator input, from pre-defined parameters stored in the memory  2504 , or in other ways. With regard to the search window parameters, alternatively the balance point determination logic  2528  may search for the HBP  2530  and CBP  2532  over the entire temperature range (or some pre-defined portion of the whole range) represented in the energy data obtained for analysis. 
     
       
         
               
             
               
               
             
           
               
                 TABLE 
               
             
             
               
                   
               
               
                 Balance Point Parameters 
               
             
          
           
               
                 Parameter 
                 Comment 
               
               
                   
               
               
                 HBPMin 
                 The lower end of the search window for the heating balance 
               
               
                   
                 point, e.g., in degrees F. or C. 
               
               
                 HBPMax 
                 The upper end of the search window for the heating balance 
               
               
                   
                 point, e.g., in degrees F. or C. 
               
               
                 CBPMin 
                 The lower end of the search window for the cooling balance 
               
               
                   
                 point, e.g., in degrees F. or C. 
               
               
                 CBPMax 
                 The upper end of the search window for the cooling balance 
               
               
                   
                 point, e.g., in degrees F. or C. 
               
               
                 R{circumflex over ( )}2Min 
                 The minimum correlation coefficient value that the balance 
               
               
                   
                 point determination logic 2528 will consider in its search for 
               
               
                   
                 a HBP or CBP. Thus, R{circumflex over ( )}2 values below R{circumflex over ( )}2Min may be 
               
               
                   
                 discarded or not considered in the search for the best fit 
               
               
                   
                 R{circumflex over ( )}2 in the analysis discussed below. In other 
               
               
                   
                 implementations, the balance point determination logic 2528 
               
               
                   
                 may employ additional or different statistical tests or 
               
               
                   
                 variables to determine which regression models to consider 
               
               
                   
                 in its search for the HBP and CBP. Alternatively, all R{circumflex over ( )}2 
               
               
                   
                 values may be considered. 
               
               
                 Temperature  
                 A temperature increment (for the HBP) or decrement (for 
               
               
                 Delta 
                 the CBP) that defines the number or size of steps through 
               
               
                   
                 the heating balance point search window or cooling balance 
               
               
                   
                 point search window. For example: an increment of 2 
               
               
                   
                 degrees and a decrement of 3 degrees, or an increment 
               
               
                   
                 and a decrement of 1 degree. 
               
               
                 Data  
                 Parameters used to identify and remove outlier data points 
               
               
                 Cleansing 
                 in the energy data before searching for the HBP or CBP. 
               
               
                 Parameters 
                 For example, a number (e.g., 1.5) of standard deviations 
               
               
                   
                 from the mean temperature or mean energy consumption 
               
               
                   
                 that defines outlier data points in temperature or energy 
               
               
                   
                 consumption to be removed. 
               
               
                   
               
             
          
         
       
     
     The balance point determination logic  2528  obtains energy data from the building under analysis ( 2604 ). As an example, the energy data may include date, temperature, and energy consumption (e.g., kWh) data for each date. The energy data may instead be kBTU consumption, occupancy, wind speed, relative humidity, or other energy data. The energy data may extend over any desired time period, such as one year, one quarter, or one month, and may be collected at any desired interval (e.g., every 15 minutes, 30 minutes, 60 minutes, or other interval), with the data subjected to any desired mathematical treatment (e.g., averaging of the data, or discarding outlier data) to obtain the energy data for a given date. 
     The balance point determination logic  2528  optionally cleanses the energy data prior to analysis ( 2606 ). For example, the balance point determination logic  2528  may remove from consideration from the energy data: weekend data points, outlier data points, data points with errors in date or energy consumption data, or other non-representative data points. The outlier data points may be data points beyond a predefined outlier threshold (e.g., 2 standard deviations away from the mean) input as a balance point parameter. More specifically, the outlier data points may be determined by finding the average and standard deviation of the input data (e.g., the average of weekdays after data with invalid dates and energy data are discarded), then removing data points more than a pre-defined or operator specified multiple of the standard deviation away from the average). 
     Given the input energy data, the balance point determination logic  2528  performs an analysis (e.g., a regression analysis) to determine a balance point set. The balance point set may include the HBP  2530  and the CBP  2532 , as an example. One exemplary analysis is described below with continued reference to  FIG. 4 . In the example given below, the balance point determination logic  2528  uses regression analysis to determine R^2 values. However, it is noted that the balance point determination logic  2528  may apply additional or different statistical tests or variables in its search of the energy data to find the HBP  2530  and CBP  2532 . 
     In particular, the balance point determination logic  2528  sorts the energy data according to temperature (e.g., in ascending order) ( 2608 ). An analysis bound, for finding the HBP  2530 , is initially set to HBPMin ( 2610 ). For all the data points starting from the least temperature to the data point corresponding to HBPMin, the balance point determination logic  2528  determines the square of the correlation coefficient, R^2, between temperature and energy consumption for that set of data points ( 2612 ). The balance point determination logic  2528  may determine the correlation coefficient by, in general, performing a linear regression using the least squares method, with ‘n’ independent variables and one dependent variable. The balance point determination logic  2528  then increments the analysis bound (e.g., by 1 degree, or another pre-defined temperature delta) and if HBPMax has not yet been exceeded, the balance point determination logic  2528  determines the square of the correlation coefficient, R^2, between temperature and energy consumption for all data points from the least temperature to the incremented analysis bound. This way, the balance point determination logic  2528  returns to ( 2612 ) repeatedly to determine the next R^2 over the new set of data points extending to the incremented analysis bound until the incremented analysis bound reaches HBPMax. The balance point determination logic  2528  may save, display, analyze, plot or otherwise manipulate any or all of the R^2 values determined during the analysis. Once the balance point determination logic  2528  has determined the R^2 values at each increment delta over the window between HBPMin to HBPMax, the balance point determination logic  2528  determines the temperature at which the best R^2 fit is achieved ( 2616 ), e.g., as determined by the greatest R^2 value. That temperature is designated the HBP  2530 . 
     With regard to the CBP  2532 , the balance point determination logic sorts the energy data according to decreasing temperature ( 2617 ) and sets a new analysis bound equal to CBPMax ( 2618 ). For the data points starting from the highest temperature to the data point corresponding to CBPMax, the balance point determination logic  2528  determines the square of the correlation coefficient, R^2, between temperature and energy consumption, for that set of data points ( 2620 ). The balance point determination logic  2528  then decrements the analysis bound (e.g., by 1 degree, or another pre-defined temperature delta) and if CBPMin has not yet been reached, the balance point determination logic  2528  determines the square of the correlation coefficient, R^2, between temperature and energy consumption for all data points from the highest temperature to the decremented analysis bound. This way, the balance point determination logic  2528  returns to ( 2620 ) repeatedly to determine the next R^2 over the new set of data points extending to the decremented analysis bound until the decremented analysis bound reaches CBPMin. The balance point determination logic  2528  may save, display, analyze, plot or otherwise manipulate any or all of the R^2 values determined during the analysis. Once the balance point determination logic  2528  has determined the R^2 values at each decrement delta over the window between CBPMmax to CBPMin, the balance point determination logic  2528  determines the temperature at which the best R^2 fit is achieved ( 2624 ), e.g., as determined by the greatest R^2 value. That temperature is designated the CBP  2530 . The HBP and CBP may be displayed, saved, or otherwise manipulated ( 2626 ). 
     Obtaining the balance point parameters noted above helps to focus the search for the balance points in specific windows. The results are faster and more efficient searches for the balance points. An exhaustive search of all the data points may still be performed, however, and in that regard, the balance point determination logic  2528  need not obtain specific balance point search window parameters before performing its analysis 
     Once the balance point determination logic  2528  has obtained the HBP  2530  and CBP  2532 , the machine  2500  may apply the HBP  2530  and CBP  2532  in many different types of analyses and reporting. For example, the measurement and verification (M&amp;V) logic  2548  (or the balance point determination logic  2528 ) may calculate the number of HDD and CDD, present analysis plots on the display  2506 , or take other actions. For example, the M&amp;V logic  2548  may: plot the CBPMin to CBPMax with corresponding R^2; plot the HBPMin to HBPMax with corresponding R^2; determine whether any day was a CDD according to the CDD test: Min(Temperature recorded that day−CBP, 0) that returns non-zero for a CDD; determine whether any day was a HDD according to the HDD test: Min(HBP−Temperature recorded that day, 0) that returns non-zero for a HDD; extract the month from the date field for each data point (row) and sum the kWh consumption, kBTU consumption, CDD, HDD and the number of days in the data set belonging to each month and store or display the monthly figures; for monthly CDD and HDD, calculate Log CDD, Log HDD, CDD^2, HDD^2 and store or display the values for each month; for each month, calculate average Occupancy, Relative Humidity, Wind Speed, Global Solar Radiation and store or display these for each month; or output other analysis result. 
     The M&amp;V logic  2548  may define, execute, and display the results of regression analyses, given, as examples, the number of CDD or HDD, the HBP  2530  or CBP  2532 , or other parameters. To that end, the M&amp;V logic  2548  may obtain (e.g., from operator input or from pre-defined parameters in the memory  2504 ) M&amp;V parameters  2550 . The M&amp;V parameters  2550  may include, as examples, company name, building name, an (optionally) unique building identifier, analysis start date, analysis end date, or other parameters. Additional M&amp;V parameters  2550  may include: user-specified independent variables for use in regression analyses, such as CDD, HDD, number of days, CDD^2, HDD^2, Log CDD, Log HDD, occupancy, relative humidity, wind speed, and global solar radiation; and user-specified dependent variables for use in the regression analyses, such as kWh consumption, kBTU (i.e., natural gas) consumption. Using the M&amp;V parameters  2550 , the M&amp;V logic  2548  may calculate, store, display or otherwise perform a user-specified regression analysis. In particular, the M&amp;V logic  2548  may determine the ‘n’ independent variables specified in the M&amp;V parameters  2550 , and from ‘i’=1 to n, take ‘i’ of the variables at a time and create a regression model with the ‘i’ variables as independent variables and kWh (or kBTU, or other energy measurement chosen) as the dependent variable. Each of the regression models created may use a combination (subset) of the overall ‘n’ independent variables chosen. The M&amp;V logic  2548  runs the regression based on the data (e.g., monthly, weekly, or daily data) for the dependent and independent variables, and may determine and store the R^2 value, significance F (e.g., from an F-test), or any other variables or test results, and the corresponding intercept and coefficient values for each of the ‘i’ independent variables. 
     The M&amp;V logic  2548  may, if desired, for each independent variable chosen, disallow the certain pairs of variables in the regression analysis (e.g., transformations of the same variable may not be chosen together in the same regression model). As examples: 
     CDD and CDD^2 together may be disallowed; 
     CDD and Log CDD together may be disallowed; 
     Log CDD and CDD^2 together may be disallowed; 
     HDD and HDD^2 together may be disallowed; 
     HDD and Log HDD together may be disallowed; 
     Log HDD and HDD^2 together may be disallowed. 
     Once the combinations of regression outputs for ‘i’=1 to n have been generated and stored, the M&amp;V logic  2548  may sort the R^2 values for the regressions (e.g., by decreasing R^2) and output on the display  2506  the top results (e.g., the top 1, 2, or 3 results). Each result may, for example, report the R^2, Significance F, intercept and coefficients for each independent variable in the top regression results. Additionally, the M&amp;V logic  2548 , may use the intercept and the coefficients for each independent variable to create the regression equation for each of the top regression outputs. 
     In addition to those noted above, the M&amp;V logic  2548  may generate a wide variety of analysis results as outputs on the display  2506 . Examples are given below. With regard to the first two examples, the M&amp;V logic  2548  may generate the displays using information obtained from the balance point determination logic  2528 . Alternatively or additionally, the balance point determination logic  2528  may display the charts described using the information that it obtains in its analysis to find the balance points. 
     1) a line chart showing temperature varying from HBPMin to HBPMax on the X axis and R^2 (the square of correlation for temperature and kWh) corresponding to each temperature on the Y axis; 
     2) a line chart showing temperature varying from CBPMin to CBPMax on the X axis and R^2 (the square of correlation for temperature and kWh) corresponding to each temperature on the Y axis; 
     3) tables containing the underlying data used to generate above two charts; 
     4) a table showing monthly date in any desired (e.g., mm/yyyy) format, kWh consumption, kBTU consumption, monthly CDD, monthly HDD, monthly CDD^2, Log CDD, Log HDD, HDD^2, monthly average Occupancy, monthly average Relative Humidity, monthly average Wind Speed, monthly average Global Solar Radiation, or other variables; and 
     5) a table which shows a summary of the top regression outputs, including R^2, Significance F, Regression Equation, Intercept and coefficients for each independent variable for each regression output. 
     The machine  2500  may additionally or alternatively include comparison logic  2544  (e.g., as one of the energy analysis programs  2524 ). The comparison logic  2544  may include instructions that when executed by the processor  2502  cause the processor  2502  to perform a kilowatt hour consumption and exception rank analysis, for example. One example of a comparison analysis  2700  that results from the comparison logic  2544  is shown in  FIG. 5 . The comparison analysis  2700  extends in 30 minute intervals over an entire day for a particular building under analysis, but the comparison logic  2544  may perform analyses over shorter or longer time periods at different intervals. Furthermore, the comparison may be done with respect to a single building (e.g., to compare energy consumption data historically for the building), or with respect to multiple buildings (e.g., to compare a building under analysis to a different control building). In addition, there may be multiple buildings in a control building group that each contribute energy data for defining (e.g., by averaging or according to another statistical treatment) the control building data described below. 
     The comparison logic  2544  may compare control building data  2702  to a user-defined standard  2704 . In the example shown in  FIG. 5 , the control building data  2702  is daily average kWh consumption measured at 30 minute intervals over 15 different days in July, and the user-defined data is  2704  is daily average kWh consumption at 30 minute intervals over the month of January 2009 for a particular building under analysis. The control building data  2702  and user-defined data  2704  may be data points over any other time intervals, time spans, or obtained from any number of different buildings, or from the same building. Further, either the control building data  2702 , the user-defined data  2704 , or both, may capture desired or expected levels of building performance, may represent actually measured and optionally statistically treated (e.g., averaged) consumption data from one or more buildings, or may represent other data automatically obtained or input by an operator. Thus, as another example, the control building data  2702  may be individual operator specified data points at 30 minute intervals that provide a baseline for comparison. The constitution of the control building data  2702  and the user-defined data  2704  may vary widely, as examples: average consumption at any desired interval on weekdays over a pre-defined time period (e.g., one week, three months, or one year), optionally excluding holidays, and/or outlier data points; average consumption at any desired interval on weekends over a predefined time period; average consumption at any desired interval on holidays over a predefined time period; or other consumption data. 
     For the example shown in  FIG. 5 , the comparison logic  2544  performs a comparison respect to energy consumption measured in kWh. However, other energy measurements may be used, such as cubic feet of natural gas consumption, gallons of water used, or measurements of other types of energy or resources. 
     The comparison logic  2544  may determine and display exceptions  2706 . The exceptions  2706  may identify noteworthy variations in consumption data for further review or analysis.  FIG. 5  illustrates an example in which 16 exception ranks  2708  are defined. As specific examples, the exception  2710  indicates a rank  3  exception and the exception  2712  indicates a rank  4  exception. The comparison logic  2544  may analyze the building control data  2702 , the user-defined data  2704 , or both to determine exceptions at any desired time interval, such as every second, every minute, every 15 minutes or every 30 minutes. 
     The comparison logic  2544  may determine an exception rank for any interval in many different ways. For example, the exception rank may apply to the user-defined data  2704 , with exception rank assigned based on the standard deviation of the user defined data  2704  (e.g., determined at each interval). Table Exception Ranks, below, shows one example definition of the exception ranks according to windows of standard deviations of the user-defined data  2704 . The comparison logic  2544  may determine additional, fewer, or different exception ranks according to additional, fewer, or different standard deviation windows, or using any other statistical criteria of interest that map the statistical criteria to one or more exception ranks. 
     
       
         
               
             
               
               
               
               
               
               
             
           
               
                 TABLE 
               
             
             
               
                   
               
               
                 Exception Ranks 
               
             
          
           
               
                 Rank 
                 1 
                 2 
                 3 
                 4 
                 5 
               
               
                   
               
               
                 Standard 
                 −1 to +1 
                 from: 
                 from: 
                 from: 
                 from: 
               
               
                 Deviations 
                   
                 −1 to −1.5 
                 −1.5 to −2 
                 −2 to −2.5 
                 −2.5 to −3 
               
               
                 from the 
                   
                 and 
                 and 
                 and 
                 and 
               
               
                 mean 
                   
                 from: 
                 from: 
                 from: 
                 from: 
               
               
                   
                   
                 1 to 1.5 
                 1.5 to 2 
                 2 to 2.5 
                 2.5 to 3 
               
               
                   
               
             
          
         
       
     
     More specifically, the comparison logic  2544  may determine the average (the control average) and standard deviation (the control standard deviation) of the energy data for each interval (e.g., the interval between 3 am and 3:30 am on a given day) in the building control data  2702 . The comparison logic  2544  further determines the average (the user-defined average) of the data for each similar interval in the user-defined data  2704 . The comparison logic  2544  then assigns an exception rank to the user-defined average for each interval in the user-defined data  2704  according to where the user-defined average falls within the control standard deviation determined from the building control data  2702  for the same interval. For example, in  FIG. 5 , at 3:30 am the comparison logic  2544  determines an exception rank of 4 because the average consumption (7082.58 kWh) is between −2 and −2.5 standard deviations of the average consumption (7267.40) in the building control data  2702  in the same interval. At 3:30 pm, an exception rank of 3 applies because the average consumption (7526.81 kWh) of the user-defined data  2704  is between −1.5 and −2 standard deviations of the average consumption (8064.87) in the building control data  2702  in the same interval. 
     The Table Comparison Data, below, shows for the comparison analysis  2700  the analysis data at 30 minute intervals for the control building data  2702  and the user-specified data  2704 , including the determined exception rank determined according to Table Exception Ranks. 
     
       
         
               
             
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                 TABLE 
               
             
             
               
                   
               
               
                 Comparison Data 
               
             
          
           
               
                   
                 Control Range 
                 User Defined  
                 Exception 
                   
               
               
                   
                 average  
                 Range average 
                 Rank by 
                   
               
               
                   
                 by time slot 
                 by time slot 
                 time slot 
                 Difference 
               
               
                   
               
             
          
           
               
                 12:30 AM 
                 7348.93 
                 7213.00 
                 2.00 
                 135.93 
               
               
                  1:00 AM 
                 7331.47 
                 7218.42 
                 1.00 
                 113.05 
               
               
                  1:30 AM 
                 7310.67 
                 7210.65 
                 1.00 
                 100.02 
               
               
                  2:00 AM 
                 7281.27 
                 7204.10 
                 0.00 
                 77.17 
               
               
                  2:30 AM 
                 7285.67 
                 7202.52 
                 1.00 
                 83.15 
               
               
                  3:00 AM 
                 7275.53 
                 7194.45 
                 1.00 
                 81.08 
               
               
                  3:30 AM 
                 7267.40 
                 7082.58 
                 4.00 
                 184.82 
               
               
                  4:00 AM 
                 7250.07 
                 7086.48 
                 4.00 
                 163.58 
               
               
                  4:30 AM 
                 7248.60 
                 7198.90 
                 0.00 
                 49.70 
               
               
                  5:00 AM  
                 7245.80 
                 7206.10 
                 0.00 
                 39.70 
               
               
                  5:30 AM  
                 7323.53 
                 7169.90 
                 2.00 
                 153.63 
               
               
                  6:00 AM  
                 7265.13 
                 7259.00 
                 0.00 
                 6.13 
               
               
                  6:30 AM  
                 7285.67 
                 7291.13 
                 0.00 
                 −5.46 
               
               
                  7:00 AM  
                 7413.67 
                 7419.35 
                 0.00 
                 −5.69 
               
               
                  7:30 AM  
                 7448.87 
                 7453.87 
                 0.00 
                 −5.00 
               
               
                  8:00 AM  
                 7526.47 
                 7419.16 
                 0.00 
                 107.31 
               
               
                  8:30 AM  
                 7583.80 
                 7443.19 
                 0.00 
                 140.61 
               
               
                  9:00 AM  
                 7673.47 
                 7465.71 
                 1.00 
                 207.76 
               
               
                  9:30 AM  
                 7751.73 
                 7499.58 
                 1.00 
                 252.15 
               
               
                 10:00 AM 
                 7835.53 
                 7522.03 
                 1.00 
                 313.50 
               
               
                 10:30 AM 
                 7923.40 
                 7550.65 
                 2.00 
                 372.75 
               
               
                 11:00 AM 
                 7952.73 
                 7556.00 
                 2.00 
                 396.73 
               
               
                 11:30 AM 
                 7975.87 
                 7537.77 
                 2.00 
                 438.09 
               
               
                 12:00 PM 
                 7994.87 
                 7545.10 
                 3.00 
                 449.77 
               
               
                 12:30 PM 
                 8015.13 
                 7557.58 
                 2.00 
                 457.55 
               
               
                  1:00 PM  
                 8016.80 
                 7550.87 
                 3.00 
                 465.93 
               
               
                  1:30 PM  
                 8041.73 
                 7556.03 
                 3.00 
                 485.70 
               
               
                  2:00 PM  
                 8057.87 
                 7556.29 
                 3.00 
                 501.58 
               
               
                  2:30 PM  
                 8052.53 
                 7600.32 
                 2.00 
                 452.21 
               
               
                  3:00 PM  
                 8043.93 
                 7568.87 
                 3.00 
                 475.06 
               
               
                  3:30 PM  
                 8064.87 
                 7526.81 
                 3.00 
                 538.06 
               
               
                  4:00 PM  
                 8050.93 
                 7492.26 
                 3.00 
                 558.68 
               
               
                  4:30 PM  
                 8041.73 
                 7506.42 
                 4.00 
                 535.31 
               
               
                  5:00 PM  
                 8010.40 
                 7509.71 
                 4.00 
                 500.69 
               
               
                  5:30 PM  
                 7971.53 
                 7496.00 
                 3.00 
                 475.53 
               
               
                  6:00 PM  
                 7935.40 
                 7487.13 
                 3.00 
                 448.27 
               
               
                  6:30 PM  
                 7891.13 
                 7462.32 
                 3.00 
                 428.81 
               
               
                  7:00 PM  
                 7859.20 
                 7441.35 
                 3.00 
                 417.85 
               
               
                  7:30 PM  
                 7815.40 
                 7436.06 
                 3.00 
                 379.34 
               
               
                  8:00 PM  
                 7783.07 
                 7426.45 
                 3.00 
                 356.62 
               
               
                  8:30 PM  
                 7720.87 
                 7415.61 
                 2.00 
                 305.25 
               
               
                  9:00 PM  
                 7703.20 
                 7405.29 
                 2.00 
                 297.91 
               
               
                  9:30 PM  
                 7628.07 
                 7392.23 
                 1.00 
                 235.84 
               
               
                 10:00 PM 
                 7492.53 
                 7280.90 
                 3.00 
                 211.63 
               
               
                 10:30 PM 
                 7442.33 
                 7267.32 
                 2.00 
                 175.01 
               
               
                 11:00 PM 
                 7423.47 
                 7257.65 
                 2.00 
                 165.82 
               
               
                 11:30 PM 
                 7371.53 
                 7224.42 
                 2.00 
                 147.11 
               
               
                 12:00 AM 
                 7349.53 
                 7219.74 
                 2.00 
                 129.79 
               
               
                 Total KWH 
                 367583.33  
                 354587.29  
                 3.00 
                 12996.04 
               
               
                   
               
             
          
         
       
     
       FIG. 6  shows the logic that the comparison logic  2544  may implement, e.g., as processor executable instructions (i.e., as a computer implemented method). The comparison logic  2544  obtains background information, such as company name, building and company location, an (optionally) unique building identifier, or other data ( 2802 ). The comparison logic  2544  gathers overall time of use consumption data at any desired interval (e.g., every 30 minutes) ( 2804 ). The operator may specify a date range for exception rank analysis ( 2806 ) and for the control range ( 2808 ). For example, the operator may specify a particular month in 2006 as the control range, and a particular month in 2010 as the date range for exception rank analysis. Optionally, the operator may directly specify the consumption data for either the control range or exception rank analysis range. 
     The comparison logic  2544  obtains or establishes exception rank definitions ( 2810 ). Examples of exception rank definitions are shown in Table: Exception Ranks, but the definitions may vary widely in implementation to take into consideration any statistical parameter desired. The comparison logic  2544  optionally cleanses any of the input consumption data ( 2812 ). To that end, the comparison logic  2544  may remove data rows for which no consumption data is available, for which erroneous data is present, or for which the consumption data is anomalous (e.g., exceeding the mean consumption data by more than a pre-defined threshold). 
     The exception analysis may be run against all the data, weekdays, weekends, holidays, or any other subset of the overall data. Thus, the comparison logic  2544  identifies and determines the number of weekdays, weekends, holidays, or other particular days in the overall data ( 2814 ). Similarly, the comparison logic  2544  determines overall and monthly average consumption data at each interval for weekdays, weekends, holiday, and all days in each month ( 2816 ). 
     For each interval in the control range (e.g., daily, every 30 minutes), the comparison logic  2544  determines the average and standard deviation of the data for that interval ( 2818 ). Similarly, the comparison logic  2544  determines the average consumption in each interval in the user-defined date range for the exception rank analysis ( 2820 ). The comparison logic  2544 , with reference to the exception rank definitions, assigns an exception rank for each interval, using the average consumption from the user-defined date range with reference to the average and standard deviation determined form the corresponding interval in the control range ( 2822 ). The comparison logic  2544  may generate and display the resulting comparison analysis  2700  ( 2824 ). While  FIG. 5  shows an analysis of daily consumption at 30 minute intervals, the comparison logic  2544  may analyze other time windows at other intervals (e.g., one day intervals over one month, or one day intervals over one year). 
     Exception ranks may be determined in ways other than that described above, however. As another example, the comparison logic  2544  may determine the exception rank according to the magnitude of the difference in consumption between the control building data  2702  and the user-defined data  2704 , with individual thresholds or ranges defined to determine which difference magnitude maps to which exception rank. Furthermore, the difference may be shaded or otherwise highlighted in the comparison analysis  2700  to help visualize the difference. 
     The machine  2500  may employ the comparison analysis  2700  to rank or gauge how well a building is performing. The ranking may be output on the display  2506  as an analysis result. For example, a building with more than a threshold number of exceptions of greater than a pre-defined exception rank (or some other function of the exception ranks) may be flagged and displayed as a building that needs special attention with regard to its energy consumption. To that end, the comparison logic  2544  may implement any desired ranking rules based on the comparison analysis  2700  to determine how well a building is performing, and responsively take action, e.g., by notifying a building supervisor, outputting notification or warning messages on the display  2506 , or taking other action when the rule fires. 
     In addition, the machine  2500  may implement building level alerts. The alerts may be defined in the memory  2504  using alert rules  2536 . Alert logic  2538 , shown in  FIG. 7 , may run at any desired interval. The alert logic  2538  may include instructions that when executed by the processor  2502 , read the alert rules  2536  ( 2902 ) and energy data from the energy database  2514  ( 2904 ). The instructions may further processes the alert rules  2536  to determine based on any of the energy data whether any of the alert rules  2536  should fire ( 2906 ). If so, the alert logic  2538  determines the analysis result ( 2908 ) based on the alert rule and displays the analysis result ( 2910 ). In that regard, the machine  2500  may output a message, display any desired indicia, or take any other pre-defined action when the alert rule fires, as defined in each alert rule  2536 . Further, the alert rules  2536  may specify the analysis result (e.g., display a warning message), the analysis results may be pre-determined (e.g., for any rule that fires, send a message to an operator), or the analysis results may be determined in other ways. The alert rules  2536  may vary widely in implementation, and may take into consideration any of the variables obtained from the building or any other source, such as kWh consumption, temperature, time, date, balance points, or other variables. Examples of alert rules  2536  are shown below in Table: Alerts. 
     
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 
               
             
             
               
                   
               
               
                 Alerts 
               
             
          
           
               
                 Alert 
                   
                   
                   
               
               
                 Category 
                 Alert Name 
                 Alert Rule 
                 Notes 
               
               
                   
               
               
                 Demand 
                 1. Demand 
                 Building kw 
                 Example: If kw  
               
               
                 kilowatts  
                 Exceeded Peak 
                 demand compared 
                 demand reaches 
               
               
                 (kw) 
                 Threshold 
                 at any desired 
                 over 500 kw, 
               
               
                   
                   
                 interval against a 
                 then issue alert. 
               
               
                   
                   
                 user defined kw 
                   
               
               
                   
                   
                 threshold. 
                   
               
               
                   
                 2. Demand 
                 Building kw 
                 Example: Daily kw 
               
               
                   
                 Exceeded 
                 demand 
                 demand is  
               
               
                   
                 Historic 
                 continuously or at  
                 compared to the 
               
               
                   
                 Threshold (not 
                 any desired 
                 same day in 
               
               
                   
                 normalized) 
                 interval (e.g.,  
                 previous years. 
               
               
                   
                   
                 daily, monthly) 
                 Issue alert if 
               
               
                   
                   
                 compared against 
                 demand is greater 
               
               
                   
                   
                 historical demand 
                 or less than 
               
               
                   
                   
                 for the building. 
                 historical demand 
               
               
                   
                   
                 Analysis Result: 
                 by a pre-selected 
               
               
                   
                   
                 Send warning 
                 threshold. 
               
               
                   
                   
                 message to 
                   
               
               
                   
                   
                 building operator. 
                   
               
               
                   
                 3. Demand 
                 Building kw 
                 Example:  
               
               
                   
                 Exceeded 
                 demand is 
                 Daily kw 
               
               
                   
                 Baseline 
                 continuously or at 
                 demand is  
               
               
                   
                 Threshold 
                 any desired 
                 compared to 
               
               
                   
                 (normalized) 
                 interval (e.g., daily,  
                 the same or a 
               
               
                   
                   
                 monthly) 
                 similar day 
               
               
                   
                   
                 compared against 
                 (as defined by 
               
               
                   
                   
                 historical baseline 
                 any desired  
               
               
                   
                   
                 regression model 
                 regression 
               
               
                   
                   
                   
                 on variables such  
               
               
                   
                   
                   
                 as HDD, CDD,  
               
               
                   
                   
                   
                 Humidity, 
               
               
                   
                   
                   
                 Occupancy,  
               
               
                   
                   
                   
                 or other 
               
               
                   
                   
                   
                 variables). 
               
               
                   
                 4. Significant 
                 Building kw 
                 Example: Demand  
               
               
                   
                 Change 
                 demand is 
                 goes from 700 kw 
               
               
                   
                 (Increase or 
                 monitored 
                 at 10 am to 500 kw 
               
               
                   
                 Decrease) in 
                 continuously or at 
                 10:15 (the next 
               
               
                   
                 Demand 
                 any desired 
                 interval). This  
               
               
                   
                   
                 interval for 
                 could either be a  
               
               
                   
                   
                 changes up or 
                 result of a demand/ 
               
               
                   
                   
                 down that exceed 
                 response event 
               
               
                   
                   
                 a pre-defined 
                 or some problem  
               
               
                   
                   
                 threshold. 
                 with equipment or 
               
               
                   
                   
                   
                 behavior changes. 
               
               
                   
                 5. Demand 
                 Building kw 
                 Example:  
               
               
                   
                 Exceeded 
                 demand is 
                 Building 1 
               
               
                   
                 Comparable 
                 continuously or at 
                 demand exceeds 
               
               
                   
                 Building 
                 any desired 
                 Building 7 kw 
               
               
                   
                 Threshold 
                 interval compared 
                 demand 
               
               
                   
                   
                 against a user- 
                 (Building 7 is the 
               
               
                   
                   
                 defined 
                 baseline or control 
               
               
                   
                   
                 comparable/similar 
                 building against 
               
               
                   
                   
                 building. 
                 which other  
               
               
                   
                   
                   
                 buildings are 
               
               
                   
                   
                   
                 compared). 
               
               
                 Consumption 
                 1. Consumption 
                 Building kwh 
                 Example:  
               
               
                 (kwh) 
                 Exceeded 
                 consumption at 
                 Daily kwh 
               
               
                   
                 Historic 
                 any desired 
                 consumption is 
               
               
                   
                 Threshold (not 
                 interval (e.g., 
                 compared to the 
               
               
                   
                 normalized) 
                 hourly, daily, 
                 same day in  
               
               
                   
                   
                 monthly) 
                 previous years. 
               
               
                   
                   
                 compared against 
                 Issue alert if 
               
               
                   
                   
                 historical 
                 consumption  
               
               
                   
                   
                 consumption for 
                 is greater or 
               
               
                   
                   
                 the building. 
                 less than 
               
               
                   
                   
                   
                 historical  
               
               
                   
                   
                   
                 consumption by 
               
               
                   
                   
                   
                 a pre-selected 
               
               
                   
                   
                   
                 threshold. 
               
               
                   
                 2. Consumption 
                 Building kwh 
                 Example: Daily  
               
               
                   
                 Exceeded 
                 consumption is 
                 kwh consumption 
               
               
                   
                 Baseline 
                 compared at any 
                 is compared to 
               
               
                   
                 Threshold 
                 desired interval 
                 the same or 
               
               
                   
                 (normalized) 
                 (e.g., daily, 
                 a similar day 
               
               
                   
                   
                 monthly) against 
                 (as defined 
               
               
                   
                   
                 historical baseline 
                 by regression  
               
               
                   
                   
                 regression model 
                 variables such as 
               
               
                   
                   
                   
                 HDD, CDD, 
               
               
                   
                   
                   
                 Humidity,  
               
               
                   
                   
                   
                 Occupancy, or 
               
               
                   
                   
                   
                 other variables). 
               
               
                   
                 3. Significant 
                 Building kwh 
                 Example:  
               
               
                   
                 Change 
                 consumption is 
                 Consumption 
               
               
                   
                 (Increase or 
                 monitored at any 
                 goes from 
               
               
                   
                 Decrease) in 
                 desired interval for  
                 900 kwh between 
               
               
                   
                 Consumption 
                 changes up or 
                 10 am and 11 am 
               
               
                   
                   
                 down that exceed 
                 to 500 kwh 11 am 
               
               
                   
                   
                 a pre-defined 
                 between and 
               
               
                   
                   
                 threshold. 
                 12 noon. 
               
               
                   
                   
                   
                 This could either  
               
               
                   
                   
                   
                 be a result of a 
               
               
                   
                   
                   
                 demand /response  
               
               
                   
                   
                   
                 event or some 
               
               
                   
                   
                   
                 problem with 
               
               
                   
                   
                   
                 equipment or 
               
               
                   
                   
                   
                 behavior changes. 
               
               
                   
                 4. Consumption 
                 Building kwh 
                 Example:  
               
               
                   
                 Exceeded 
                 consumption is 
                 Building 1 
               
               
                   
                 Comparable 
                 compared at any 
                 consumption  
               
               
                   
                 Building 
                 desired interval 
                 exceeds Building  
               
               
                   
                 Threshold 
                 against a user- 
                 7 kwh consumption 
               
               
                   
                   
                 defined 
                 (Building 7 
               
               
                   
                   
                 comparable/similar  
                 is the baseline 
               
               
                   
                   
                 building. 
                 or control 
               
               
                   
                   
                   
                 building against 
               
               
                   
                   
                   
                 which other  
               
               
                   
                   
                   
                 buildings are 
               
               
                   
                   
                   
                 compared). 
               
               
                   
               
             
          
         
       
     
     The alerts discussed above are building level alerts with regard to consumption and demand. However, the alert rules may define alerts based on any energy consumption parameters that are directly measured, statistically derived, or otherwise obtained. The machine  2500  may also implement more complex analyses. As one example, the machine  2500  may include event logic  2542  that analyzes equipment data to detect or infer events of interest based on event rules  2540 . Such event rules  2540  may help define when demand/response events occur, when equipment has failed or has been fixed, or other events. When the event logic  2542  identifies an event, the event logic  2542  may responsively execute a pre-defined action, for example as specified in the event rules  2540 . 
     The machine  2500  or any of the systems described above may be implemented with additional, different, or fewer components. As one example, a processor may be implemented as a microprocessor, a microcontroller, a DSP, an application specific integrated circuit (ASIC), discrete logic, or a combination of other types of circuits or logic. As another example, memories may be DRAM, SRAM, Flash or any other type of memory. The processing capability of the system may be distributed among multiple components, such as among multiple processors and memories, optionally including multiple distributed processing systems. Parameters, databases, and other data structures may be separately stored and managed, may be incorporated into a single memory or database, may be logically and physically organized in many different ways, and may implemented with different types of data structures such as linked lists, hash tables, or implicit storage mechanisms. 
     Logic, such as programs or circuitry, may be combined or split among multiple programs, distributed across several memories and processors, and may be implemented in a library, such as a shared library (e.g., a dynamic link library (DLL)). The DLL, for example, may store code that analyzes energy expenditure or that prepares energy reports. As another example, the DLL may itself provide all or some of the functionality of the machine  2500 . The programs may be stored on a computer readable medium, such as a CDROM, hard drive, floppy disk, flash memory, or other computer readable medium. Thus, a computer program product may include computer readable instructions, which when loaded and run in a computer and/or computer network system, cause the computer system and/or the computer network system to perform operations according to any of the claims below, and in particular to perform any of the logic and methods illustrated in  FIGS. 4 ,  6 , and  7 , as examples. 
     While various embodiments of the invention have been described, it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the invention. Accordingly, the invention is not to be restricted except in light of the attached claims and their equivalents.