Abstract:
A method of predicting energy usage having the steps of providing a computer, providing a temperature database, providing a building asset rating database, receiving inputs from a user having a floor area measure of a building, an energy usage measurement, an energy usage start date, an energy usage end date, and a geographic location identifier, determining an estimated energy rating as a function of said inputs, said temperature database, and said building asset rating database, and providing said estimated energy rating to said user.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims priority from U.S. provisional app. no. 61/647,415 which was filed on 15 May 2012, and U.S. provisional app. no. 61/769,193 which was filed on 25 Feb 2013, each of which is incorporated by reference in its entirety. 
     
    
     TECHNICAL FIELD 
       [0002]    The present invention relates generally to the field of rating the energy efficiency of buildings, and more specifically to methods and systems for automatically estimating established building energy rating methods. 
       BACKGROUND ART 
       [0003]    Several types of energy performance rating systems are known. Such systems are typically asset based, that is based upon physical characteristics and inspections of a given building. Two such systems are provided by the US Department of Energy, and RESNET. Both are nationally recognized as using certified rating methods. The Department of Energy&#39;s rating system is the newly released Home Energy Score, which rates houses on a scale of 1 to 10, with 10 being the most efficient and 1 being a home in need of extreme weatherization measures. The Department of Energy&#39;s rating is accomplished via a 3 page questionnaire which assesses building “characteristics” and is essentially designed to be a very high level (not detailed) asset assessment of a home&#39;s energy efficiency. A more detailed explanation is available at homeenergyscore.lbl.gov. 
         [0004]    RESNET created and utilizes the Home Energy Rating Score Index (the “HERS Index”). The HERS Index number is on a scale from 0 to 150. 0 is a zero net energy home, and 100 is the “standard” new home built in strict accordance with 2004 residential energy code and Energy Star Standards. Homes scoring above 100 are less energy efficient than the “standard” model. The lower the number on the HERS scale, the more energy efficient the home. The HERS Index rating is the nationally accepted method of rating the energy efficiency of new homes. The HERS rating method is used for rating the energy performance of newly constructed homes, and requires two inspections during the construction process and a final testing of the completed structure. More detail is available at www.resnet.us/hers-index. 
       BRIEF SUMMARY OF THE INVENTION 
       [0005]    With parenthetical reference to the corresponding parts, portions or surfaces of the disclosed embodiment, merely for the purposes of illustration and not by way of limitation, provided is. A system for predicting energy factors comprising: a user interface for receiving: a HERS index; a date range; a building square footage; a building location; a computer; a database comprising:average temperature for zipcode for a given day; an calculation module providing an estimate of the BTU/sqft/HDD, wherein said output is a function of said HERS index, date range, square footage and a building location. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0006]      FIG. 1  is a block diagram of a first general embodiment system for providing a building energy rating. 
           [0007]      FIG. 2  is a block diagram of a second embodiment system for providing an estimate of an asset based energy rating. 
           [0008]      FIG. 3  is a block diagram of a third embodiment system for providing a building energy rating. 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       [0009]    At the outset, it should be clearly understood that like reference numerals are intended to identify the same structural elements, portions or surfaces consistently throughout the several drawing figures, as such elements, portions or surfaces may be further described or explained by the entire written specification, of which this detailed description is an integral part. Unless otherwise indicated, the drawings are intended to be read (e.g., cross-hatching, arrangement of parts, proportion, degree, etc.) together with the specification, and are to be considered a portion of the entire written description of this invention. As used in the following description, the terms “horizontal”, “vertical”, “left”, “right”, “up” and “down”, as well as adjectival and adverbial derivatives thereof (e.g., “horizontally”, “rightwardly”, “upwardly”, etc.), simply refer to the orientation of the illustrated structure as the particular drawing figure faces the reader. Similarly, the terms “inwardly” and “outwardly” generally refer to the orientation of a surface relative to its axis of elongation, or axis of rotation, as appropriate. 
         [0010]    The disclosed invention embodiments provide a system and method for providing a building energy performance rating given a set of characteristics associated with a building. A building is broadly defined as a manmade structure used for shelter including a house, an apartment building, a commercial building, or any other similar structure. The disclosed embodiments provide a system and method for determining an energy performance rating needing only the limited information of an actual heating bill, a building&#39;s livable area, and the geographic location of a building. The disclosed embodiments are able to provide an accurate estimate of the popular RESNET and HERS Index rating systems, without the need for a lengthy inspection and/or an extensive questionnaire based process. The disclosed embodiments include an advanced algorithm which utilizes a large database of building data and geographic temperature data to calculate a most likely performance rating for a building based on limited information provided. Other calculations, such as estimated yearly heating fuel costs, cooling costs, ad or electricity costs are also provided by the system. In order to predict energy performance ratings with a higher degree of certainty, additional building data may be provided to the system including: the number of occupants residing at a building, the number of rooms and room types, actual past electricity usage records, local heating fuel and electricity costs, and other similar data. 
         [0011]    Referring now to the drawings, and more particularly to  FIG. 1 , shown at  100  is a system for providing an energy performance rating. System  100  receives building data  106  as input, and provides an energy rating number (“ERN”) energy performance rating  151  as output. System  100  has the major components of temperature database  174 , degree day calculation module  170 , and performance rating calculation module  160 . 
         [0012]    Building data  106  that is provided to system  100  includes both energy performance based data and static, physical characteristic based data. More specifically, building data  106  includes a measurement of energy usage over a given period of time, which consists of energy usage start date  111 , energy usage end date  112 , and energy usage amount  114 . The energy usage start date  111 , and energy usage end date  112  are typical month-date-year date values which are typically available from a heating bill. For example, a heating bill may have a energy usage start date  111  of Jan. 1, 2012, and an energy usage end date  112  of Jan. 31, 2012. As an alternative, a more accurate measurement of the exact start date and end date may include a time of day. The energy usage amount  114  is provided in BTU&#39;s (British Thermal Units). Many utility companies provide the number of BTU&#39;s used in a bill period. Alternatively, the energy usage amount may be provided in other energy units, such as gallons or liters of heating fuel, kilowatt-hours of electricity, kilograms or tons of pellets, therms of natural gas, cords of wood, or any other similar energy unit. Whatever type of energy unit is originally provided, the usage amount is converted to a common unit type, such as BTU&#39;s. 
         [0013]    Building data  106  also includes the building&#39;s geographic location  113 . Geographic location  113  may be provided in formats such as zip code, mailing address, GPS coordinates, or some other similar geographic location type. Whatever unit the geographic location is originally provided in, it is then converted to a geographic zip code for further processing by the system. Building data  106  additionally includes building area  115 . Building area  115  is a measurement of the livable area in the building. The provided livable area is converted into units of square feet (sq. ft.) when received by system  100 . 
         [0014]    Degree day calculation module  170  provides the cumulative number of degree days at a given geographic location for a provided date range. More specifically, degree day calculation module  170  is provided an energy usage data start date  111  and end date  112 , as well as geographic location  113 , and in return determines the number of degree days  154  at the provided geographic location for the provided date range. As used herein, a degree day is the sum of the difference between the average temperature at a given geographic location and an either heating or cooling threshold temperature  171  for a given time period. For example, a user may want to know the number heating degree days for the time period of Jan. 1, 2012 to Jan. 3, 2012 at the geographic zip code location of 14203, using a threshold temperature of 65 degrees Fahrenheit. Degree day calculation module  170  will first determine the average temperatures on of days Jan. 1, Jan. 2, and Jan. 3, 2012. Module  170  will then subtract the average temperature of each day from the threshold temperature of 65 degrees. The sum of these differences is provided by module  170  as the total (heating) degree days  154 . 
         [0015]    In order to calculate the degree days, module  170  uses historical temperature database  174 . Temperature database  174  consists of a number of historically recorded temperatures for given dates at given geographic locations. More specifically, temperature database  174  has a number of database records  175 , each database record  175  including geographic location  176 , temperature  177 , and date-time  178 . Geographic location  176  is a zip code or other similar geographic location type. Temperature  177  is a temperature measured in Fahrenheit, Celsius, or other scale. In this embodiment, temperature  177  is an average temperature recorded over a given day (such as the average temperature recorded over a day). However, other temperature measurements, such as a high or low temperature recorded over a day, or an instantaneous temperature measured at a specific date-time may be used in addition, or as an alternative to a daily average temperature. 
         [0016]    Degree day calculation module  170  uses temperature database  174  by sequentially requesting the average temperature for each day in the appropriate date range for the provided geographic zip code. As shown in  FIG. 1 . temperature database  174  receives the requested date-time from module  170  as shown by line  155 . Similarly, temperature database receives the geographic zip code as shown by line  156 . Temperature database  174  in response provides module  170  the average temperature for the provided date-time and geographic zip code as shown on return line  157 . 
         [0017]    Performance rating calculation module  160  calculates ERN performance rating  151  based on data received from degree day calculation module  170  and building data  106 . More specifically, performance rating calculation module  160  receives total degree days  154  from degree day module  170 . Performance rating calculation module  160  also receives energy usage amount  114  and building area  115  from building data  106 . Performance rating calculation module uses function/algorithm  162  to calculate performance rating  151  from inputs  114 ,  115 , and  154 . In a most basic form, function/algorithm  162  uses the following function to calculate the ERN energy performance rating: 
         [0000]        ERN= (energy usage)/(degree days)/(building area) 
         [0018]    In another form, a scaling constant is multiplied by the result: 
         [0000]      Scaled  ERN= (constant)×(energy usage)/(degree days)/(building area)
 
         [0019]    In another embodiment, function/algorithm  162  is a nonlinear function which converts the ERN to an alpha score which is a letter grade in the set [A+, A, A−, B+, B, B−, C+, C, C−, D+, D, D−, F]. More specifically, in this alternative embodiment, the function will provide the alpha score letter output according to the following table: 
         [0000]    
       
         
               
               
               
             
           
               
                   
                   
               
               
                   
                 Alpha Score 
                 ERN 
               
               
                   
                   
               
             
             
               
                   
                 A+ 
                 0.00 ≦ ERN &lt; 3.25 
               
               
                   
                 A 
                 3.25 ≦ ERN &lt; 4.62 
               
               
                   
                 A− 
                 4.62 ≦ ERN &lt; 5.67 
               
               
                   
                 B+ 
                 5.67 ≦ ERN &lt; 6.17 
               
               
                   
                 B 
                 6.17 ≦ ERN &lt; 7.50 
               
               
                   
                 B− 
                 7.50 ≦ ERN &lt; 8.50 
               
               
                   
                 C+ 
                 8.50 ≦ ERN &lt; 9.50 
               
               
                   
                 C 
                 9.50 ≦ ERN &lt; 10.50 
               
               
                   
                 C− 
                 10.50 ≦ ERN &lt; 11.00 
               
               
                   
                 D+ 
                 11.00 ≦ ERN &lt; 11.5 
               
               
                   
                 D 
                 11.5 ≦ ERN &lt; 13.00 
               
               
                   
                 D− 
                 13.00 ≦ ERN &lt; 14.00 
               
               
                   
                 F 
                 14.00 ≦ ERN 
               
               
                   
                   
               
             
          
         
       
     
         [0020]    System  100  is implemented on a microprocessor having a memory for holding software and data. In alternative embodiments, system  100  can be implemented on a server computer, a desktop computer, a smartphone, or other similar system. Temperature database  174  is implemented as a MySQL database, however other database systems, such as Oracle DB, Microsoft SQL, Postgre, or other similar database may be used. The software may be programmed in Excel, java, C++, C, python, or some other similar language. The computer system  100  is implemented on may include an operating system such as MacOS X, Microsoft Windows  7 , linux, or other similar operating system. 
         [0021]    Shown in  FIG. 2  is a second embodiment system  200  for determining an ERN performance rating  151  and for providing a correlated RESNET and HERS rating  142 . System  200  is similar to system  100 , but also has housing database  105 , and correlation engine  140 . Housing database  105  contains data records for a number of buildings. The data record for each building includes both building data  106  and certified asset based rating data  107 . Asset based ratings  107  includes certified asset based ratings such as RESNET rating  116  and HERS rating  117 . Having a large database of buildings with certified asset based ratings and performance based building data allows system  200  to accurately correlate performance based data to asset based data as is described in detail below. 
         [0022]    Correlation engine  140  receives the calculated ERN  151  for each building entry in database  105  and determines a correlation function to correlate the ERN with the asset based RESNET rating  116  and/or the asset based HERS rating  117 . More specifically, correlation engine  140  uses a function to correlate the ERN entries to the asset based ratings  107 . A simple function which may be used by correlation engine  140  is an interpolation function. Another simple function is a linear regression, in which a slope and intercept are calculated using well known methods. More advanced versions of a correlation function involve using a higher order curve fit involve using higher order coefficients as described in Coope, I. D. (1993), in “Circle fitting by linear and nonlinear least squares”, Journal of Optimization Theory and Applications 76 (2): 381. Other advanced correlation functions such as fuzzy logic and neural networks may also be used. 
         [0023]    Shown in  FIG. 3 , is third embodiment building performance rating estimation system  300 . System  300  has the major components of server computer  120  and user computer  192 . User  190  provides actual past housing/heating data to user computer  192  as shown at  181 , which is then relayed to server computer  120 . Server computer  120  calculates a an estimated performance rating which is relayed through user computer  192  back to user  190  as shown at  182 . 
         [0024]    User computer  192  is used to relay experimental data  106  to server computer  120 , and also relay the estimated performance rating from server computer  120  to user  190 . 
         [0025]    Therefore, while the presently-preferred form of the building energy performance rating system and method of building energy performance rating are disclosed and described, and several modifications discussed, persons skilled in this art will readily appreciate that various additional changes may be made without departing from the scope of the invention.