Patent Publication Number: US-2023138551-A1

Title: Green building system and method

Description:
PRIORITY CLAIMS/RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 13/678,456, filed on Nov. 15, 2012, which claims the benefits under 35 U.S.C. 119(e) and 120 to U.S. Provisional Patent Application Ser. No. 61/560,284 filed on Nov. 15, 2011 and entitled “Green Building System and Method”, the entirety of which is incorporated herein by reference. 
    
    
     FIELD 
     The disclosure relates generally to a system and method for determining building components. 
     BACKGROUND 
     The building of energy efficient buildings (known as green building) has become a very popular task. The demand for building of energy efficient buildings has accelerated recently due to various factors including widespread regulations, tax and cash incentives, availability of cost-effective energy-efficient solutions, expected energy cost growth, an overall desire to be more environmentally responsible and/or energy related comfort that is important to people with low price sensitivity. 
     Meeting environmental construction goals (for example—reducing home energy consumption by 25%) requires finding an optimal combination of house shell components like windows, walls, roofs, insulation and mechanical equipment. There are millions of possible ways to design and build each house, and each can greatly affect cost, energy consumption and comfort. Unfortunately, architects and builders are not aware of all these combinations and don&#39;t have the tools and skills to find the best one. Thus, their selection is based on past experience and preference and usually yields suboptimal results. In most cases, homeowners can achieve better energy results for their investment or reach their energy-related goals for a much lower cost. 
     Systems and methods exist in which a user can try to identify the best building materials for green building. The existing solutions to try to build energy efficient buildings are too expensive and give only partial support. The existing solutions may include an architect&#39;s experience, an architect hiring an energy analysis using energy analysis software, an architect using third party energy analysis and/or a homeowner using an on-line retrofit analysis software. Each of those existing solutions, the cost can be as much as $50,000 and has many limitations. For example, none of these tools offers quick design data capture, automatic optimization capabilities, full cost/benefit analysis, early design optimization (such as, house orientation and shape) or easy visualization, and they all have a very steep learning curve. Thus, architects and builders usually use a combination of in-house developed spreadsheets and gut feelings to identify and suggest a possible design to their clients and then hire an expert to validate their findings. This process is time consuming and does not provide the optimization analysis for finding best designs. The existing solutions also usually cannot answer the questions:
         If I had $1,000 more to invest in energy systems, what would I do?   What is the most cost effective way for me to meet energy codes?   How can I best protect myself from future energy cost spikes?       

     Thus, it is desirable to provide a green building system and method that overcomes the above limitations of the existing solutions and it is to this end that the disclosure is directed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates an implementation of a client/server architecture of a green building system; 
         FIG.  2    illustrates an example of the interactions between the users and the system; 
         FIGS.  3 A and  3 B  are diagrams of a plot chart and a table, respectively of a set of several thousand design choices for the same house generated by the decision engine; 
         FIG.  4    illustrates a goal seek and design comparison user interface of the system; 
         FIG.  5    illustrates more details of the decision engine; 
         FIGS.  6 A- 6 E  illustrate examples of building specific dimension information user interfaces of the system; 
         FIG.  7    illustrates an example of the window choice user interface; 
         FIG.  8    illustrates an example of the user interface for an architect; 
         FIG.  9    illustrates low level details of the decision engine; 
         FIG.  10    illustrates an example of the database schema of the system; 
         FIG.  11    is an example of a user interface of a incentives feature; and 
         FIGS.  12 A- 12 B  are examples of a user interface of the incentive feature. 
     
    
    
     DETAILED DESCRIPTION OF ONE OR MORE EMBODIMENTS 
     The disclosure is particularly applicable to a client/server based building system design, construction and maintenance and method and it is in this context that the disclosure will be described. It will be appreciated, however, that the system and method has greater utility, such as to other architectures of a building system and method and to other implementations of the building system design, construction and maintenance and method. 
       FIG.  1    illustrates an implementation of a client/server architecture of a green building system  100 . The system has one or more client computing devices  102  (such as client computing device  102   a , . . . , client computing device  102   n ) that can communicate and connect through a link  104  to a green building unit  106 . Each client computing device may be a processing unit based device with sufficient memory, storage capacity, processing power, display capability and connectivity to connect to and interact with the green building unit  106 . For example, each client computing device  102  may be SmartPhone device (Apple® product (iPhone, iPad, etc.), RIM® product (Blackberry), Android® OS based devices, etc.), a cellular phone device, a personal computer, a tablet computer and the like. In one implementation, each client computing device  102  may have a typical browser application ( 102   a   1 , . . . ,  102   n   1  for example for the n client computing devices) that can connect to the green building unit  106  and communicate data and web pages with the green building unit  106 . The link  104  may be a wireless or wired link that allows the one or more client computing devices  102  to connect to and interact with the green building unit  106 , such as the Internet, a cellular data network, a computer data network and the like. The green building unit  106 , in one implementation, may be one or more server computers that execute a plurality of lines of computer code that implement the functions and operations of the green building unit  106 . In the client/server architecture implementation, the green building unit  106  may have a web server  106   a  that interacts with the browser application in each client computing device to exchange data, generate and deliver web pages, generate and deliver web pages with forms, etc., a decision engine  106   b  and one or more stores  106   c  that contain the data that is used by the decision engine and the rest of the system to perform the functions and operations described below. In one implementation, the code executed by the green building unit  106  is written in Java and Java Script and each client computing device interacts with the program through a web browser (Firefox, Chrome, IE, Safari). In this implementation, the program is downloaded from the green building unit  106  to the client computing device  102  and runs as a “rich internet application” on the web browser in Java Script and the client computing device communicates with the remote green building unit  106  using standard communication protocols (REST, HTTP, JSON, HTML.) The client initiates the green building process as described below on the one or more server computers and the code for the green building unit  106  is written in Java and runs on Windows, Linux and Unix. In one implementation, the green building process may be written for distributed system allowing to compute millions of permutations on many servers in parallel. By parallel execution, the system allows near-instant computation of different alternatives which is not done today. The green building unit may also have a user interface unit connected to the decision engine that generates the user interfaces of the green building unit as described below. 
     The system above is a software as a service (SaaS) solution since there is no installation on the client side and that upgrades are handled by the green building unit  106 . This allows the system to make easy updates, for example in case we learn that a cost of a window changes. It also allows us to do statistics on our data. For example—In a specific project, the homeowner is charged X for a sqft of wall. Using the system, she can check whether this is the normal price for that type of wall using the summarized analysis of the data in the database. There are other ways to implement the system that may include: 1) a full/partial installation on the client side to give full control of data; 2) a semi manual process—where the optimization is given as a service. The user sends the inputs and someone else running the system is doing the analysis; and 3) a full manual process—User sends one house design and gets back the utility value for that design. If it does not pass the threshold—the user updates the design and send the updated design for evaluation. The green building system may also be implemented with a piece of software downloaded to each client computer (or delivered to each client computer on a computer readable medium), in a client/server system and in a cloud system in which the one or more server computers are cloud resources. 
       FIG.  2    illustrates an example of the interactions between the users and the system. The decision engine  106   b  performs an analysis to suggest a best set of building components (for residential, commercial, new or retrofit) to answer the energy needs of the homeowner and the following pieces of data are input to the decision engine  106   b:    
     (a) External data sources:
         1. Building component cost data ( 108   a ) (for example the cost for different types of windows, walls etc).   2. Weather and climate data ( 108   b ) to project the heating/cooling needs at the house location.   3. Building material system ( 108   c )—to verify that we follow the correct building practices.   4. Building code data ( 108   d )—which codes are needed, where and how to check whether a design meets code.   5. Government &amp; utility incentives ( 108   e ) and tax breaks (some is location based).   6. Utility payment (cost) ( 108   f )—location based.       

     (b) Internal data (most data is obtained from the homeowner):
         1. Building Specific dimension information—sqft, number of floors, size of windows etc.   2. List of potential components that the client is considering for the house. For example windows types etc., walls, insulation, roof etc. Each input contains the thermal performance of the element and the element cost.   3. Client&#39;s special constraints and preferences: Components already chosen, financial constraints, desired payback period etc.   4. Other related information about weather, energy cost etc. needed for estimating the energy needs and costs.       

     The decision engine  106   b  establishes a utility function per client which is a combination of desires, financials, environmental awareness and code requirements, calculates all possible design permutations for the house based on a set of design components defined by the client (for example—4 types of potential windows, 5 types of potential walls . . . ); and/or finds the designs that best comply with the utility function. 
     An Architect/builder  120   a ,  120   b  uses the analysis from the decision engine  106   b  to compare and choose a design for the house (windows, walls, roof etc.), communicate the different design options as well as their utility (cost, benefit) and tradeoff to the home owner  120   d  (called client on the diagram), provide the needed “proof” to inspector  120   e  (for getting building, occupancy permit in case proof of environmental analysis is needed), and incentive providers  120   f  and compare design tradeoffs during construction (for example if a certain insulation is not available). 
     The system may have an input for the parts provider  120   g  who can enter information about new components available (for example new type of window) into the system. This will allow homeowners (clients) wider variety to choose from and will increase exposure for the parts provider. Future buyers  120   c  get information about energy consumption of a house (e.g., energy report) they are considering buying and in return willing to pay more for the house. Mortgage providers get information about energy consumption of a future house and, in return, they give a better mortgage terms (fewer risk of default due to smaller utility bills). 
       FIGS.  3 A and  3 B  are illustrations of a plot chart and a table of the design choice generated by the decision engine  106   b  in which each design is a point in the chart in  FIG.  3 A . In these figures that trade-off between annual energy bills and cost are shown for different design choices.  FIG.  4    illustrates a goal seek user interface  140  of the system in which goal seeks—design tradeoffs between several designs are illustrated to the user. For example, as shown in  FIG.  4   , a first design solution  141   a  and a second design solution  141   n  that match the various inputs and filters are displayed to the user. Each design solution  141  may include a calculated design results portion  142  that shows calculated values for the particular design solution and a design parameters portion  144  that lists the various design choices (lighting, air conditioner, etc.) that are part of each design solution. The calculated design results portion  142  may further include an HERS value for the design solution, a capital cost of the design solution, an estimated annual mortgage payment for the design solution, an estimated annual energy bill for the design solution, an estimated annual energy consumption for the design solution, an estimated annual CO2 emissions of the design solution, an estimated number of trees planted based on the reduced CO2 emissions and/or an estimated number of cars converted into hybrid cars that would correspond to the CO2 reduced emissions ( 142   a - 142   i ). 
       FIG.  5    illustrates more details of the decision engine  106   b . The inputs to the decision engine  106   b  may include Building Specific Dimension information  150  (an example user interface of which is shown in  FIG.  6 A ) which is the information needed about the size, orientation and type of material and components that the architect/builder plans to use for the house and are needed for the energy analysis. 
     Another input to the decision engine  106   b  may be other related information  152  which are other inputs needed for running the analysis that may include: building component cost data; Weather and climate data to project the heating/cooling needs at the house location; Building material; Building code data; Government &amp; utility incentives and tax breaks (some are location based); and Utility payment (cost) which can be location based. 
     The inputs may also include a list of potential components  154  which includes user input of possible selection of enclosure/wall components (see  FIG.  6 B  that has an example of the user interface for the enclosure/wall components), mechanical components (see  FIG.  6 C  that has an example of the user interface for the mechanical components), windows, heating equipment, air conditioners, ceiling insulation, floor insulation, basement wall insulation, lighting scheme (see  FIG.  6 D  that has an example of the user interface for the lighting components), and infiltration components (see  FIG.  6 E  that has an example of the user interface for the infiltration components.) For example, the user can indicate that she is considering 4 types of windows for the house as shown in  FIG.  7   . 
     The decision engine may also receive constraints &amp; Incentives  156  which are a list of filters and financial inputs. This list might be location, house size and geometry or time based. For example—a certain building code mandated in a certain town or the potential to get a tax break if meeting a certain energy standard. An example of the user interface for this feature is shown in  FIGS.  11 - 12 B . In particular,  FIG.  11    is an example of a first user interface screen for the constraints and incentives feature.  FIG.  12 A  illustrates an example of the user interface with some constraints and incentives used by the system and  FIG.  12 B  illustrates an example of a graph that compares HERS to cost. 
     The decision engine may also receive client&#39;s preferences  158  and these can contain filters (for example: I am only interested in window X out of all the possible options) and/or utility function defined by the homeowner. The preferences may also include components already selected by the user, financial constraints and desired payback. 
     The decision engine may include the processes of: data entry regarding the house geometry, climate and energy related usage; possible option input by user; user defines a utility function; and the system presents the best design. In the first data entry process, the data entry regarding the house geometry, climate and energy related usage is performed. The architect/builder/homeowner can enter the entire data herself or ask the system to “fill-in” the gaps using a smart algorithm that can, for example, fill in the climate info based on ZIP code or “guess” the house shape. The system uses that to promote an “onion” approach where the use can start using the system very early, entering few inputs and add more inputs throughout the design process to replace the automatic algorithm and produce better analysis. 
     During the possible options definition process, the user adds information regarding possible options for the different components (walls, windows, heating equipment, air conditioners, ceiling insulation, floor insulation, basement wall insulation, lighting scheme, photovoltaic (PV), etc.). During the utility function definition, the user defines a utility function. For example—finding the cheapest design that meets a LEED score of X. The utility function can be one goal, a set of weighted goals that include cost, desired payback, environmental goals, convenience etc. (For example, a utility function can be defined as a sum of 20% upfront cost reduction, 30% payback period reduction, 50% CO 2  emission reduction) or a combination of must meet and weighted nice to have goals. An example of a must meet goal—mandatory environmental code in a certain location. 
     The engine  106   b  may have an optimized output portion  160  that generates a list of the best components (enclosure, lighting, etc.) for a specific project based on the various input data. The engine  106   b  may also have a building performance information portion  162  that generates information about code compliance and incentive compliance for the specific design solution. The engine  106   b  also has a reporting unit  164  that generates various reports for different users of the system based on the inputs and processes. 
     Based on the above processes, the system finds and presents to the user the best design for the defined utility function (if the user is looking for one design) or a set of designs that meet criteria (if the user is interested in comparing several options). The process creates all possible design combinations that include all of the combinations of the components defined by the user above. The system also calculates the utility function for each design in which the utility function can be a combination of cost, projected energy consumption, payback period, code compliance etc. The system organizes the solutions according to their utility function score and filters out the design that do not meet the user thresholds (in case filters were defined). The system presents the ordered list to the user. Note: For easy understanding and alternative comparison, the system offers a translation of the results to a more easy to understand metrics that will allow the user to grasp the alternatives. For example—tons CO2 are translated into # of planted trees or converting regular cars to hybrid cars needed to offset the building environmental impact. 
       FIG.  8    illustrates an example of the user interface  170  for an architect. The system may also have a user interface for the builder, a home rater (energy analyst), a homeowner, HVAC engineer, parts provider (such as Pella windows, Home Depot etc.) and/or any other stakeholder in the design, construction and maintenance of houses. Each of the different user interfaces present different information to each possible user of the system since each user often has different goals for the system. 
       FIG.  9    illustrates low level details of the decision engine  106   b . The system provides an expandable/plugin computation for energy decisions. The general flow of the method is as follows:
         User defines house design ( 180 ).
           The house design can include one or more of the following items:
               House geometry   Geographical Location   Financial information (mortgage rate, length, etc.)   HVAC systems   
               
           User defines goals, preferences and restrictions
           Max budget   Energy goals   Allowed/desired house components:
               What type of windows to use? What type of doors?   User can use Ekotrope provided suggestions and/or add his/her own components.   
               User specifies what elements should be considered for analysis.
               All elements?   Just analyze window sizes?   HVAC?   Any combination of components.   
               
           The user&#39;s input is then sent to the system for analysis.
           System can compute/analyze based on complete or partial user information. Defaults will be provided for missing data if allowed.   
           After receiving user information, the system creates all possible combinations of house designs (permutations by a permutation engine  182 ) by matching initial user input with possible components and design changes.   All house designs are then analyzed using the system&#39;s defined analyzers ( 184 ) from an analyzer library  184   a  stored in the stores  106   c.  
           Analyzers can include Ekotrope analyzers and/or analyzers provided by 3 rd  party vendors ( 184   b ).   Analysis provides additional information to each house design such as energy consumption ( 184   c ), energy costs, HERS ( 184   d ), LEED ( 184   e ), etc.   The system incorporates a cost engine that allows comparisons of CAPEX (cost to build) and OPEX (utility costs.)   The system also permits full parametric analysis and any design parameter can be optimized on the fly.   The system also may allow early analysis which means that users do not have to wait until late in the design process to do an energy analysis.   
           All house designs are sent to the filtering system ( 186 ) that has a filter library  186   a.  
           The filtering system filters out invalid designs and/or designs that do not match the user preferences. An invalid design may be, for example, if the design exceeds capital cost, desired energy usage or payback economics.   The filtering process may include third party filters  186   d , client preference filters  186   c  and HVAC loading filters  186   b , for example.   
           Filtered set of house designs is presented to the user ( 188 ,  190 ). User can choose from a library of reports or view interactive information regarding the provided house designs.       

       FIG.  10    illustrates an example of the database schema of the system. Since most of the engine executes with in-memory data distributed over multiple servers, the database design is used to define configuration information prior to analysis. 
     While the foregoing has been with reference to a particular embodiment of the invention, it will be appreciated by those skilled in the art that changes in this embodiment may be made without departing from the principles and spirit of the disclosure, the scope of which is defined by the appended claims.