WO2014085777A1 - Automated model simulation and calibration tool - Google Patents

Abstract

Methods and systems for automated simulation and calibration of a model are provided. According to one embodiment, a computer-implemented method is provided for calibrating a building energy model and based thereon providing feedback regarding one or more energy conservation measures (ECMs). A building energy model for a building at issue is calibrated by simulating energy usage for the building at issue and comparing simulated results to actual utility data for the building at issue. Then, feedback regarding one or more ECMs is provided by determining a result of implementing the one or more ECMs for the building at issue by simulating energy usage for the building at issue based on the calibrated building energy model and the one or more ECMs. In one embodiment, the method further involves calculating or estimating carbon emission savings associated, with implementation of the one or more ECMs for the building at issue.

Description

AUTOMATED MODEL SIMULATION AND CALIBRATION TOOL

Cross-Reference to Related Applications

[0001] This application claims the benefit of priority to US Provisional Application No. 61/731,232, filed on November 29, 2012, which is hereby incorporated by reference in its entirety for all purposes.

COPYRIGHT NOTICE

[0002] Contained herein is material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction of the patent disclosure by any person as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights to the copyright whatsoever. Copyright © 2012-2013, The Regents of the University of Colorado.

BACKGROUND

Field

[0003] Embodiments of the present invention generally relate to computer simulation and calibration of models. In particular, embodiments of the present invention relate to automated simulation and calibration of building energy models for, among other things, recommending energy conservation measures and facilitating energy auditing.

Description of the Related Art

[0004] Calibrated energy models are useful for identifying potentially cost- effective energy conservation measures (ECMs), but the associated manual calibration iterations and expertise required typically make creation of such calibrated energy models both time-intensive and expensive. Similar limitations are observed in connection with obtaining ECM recommendations from such calibrated energy models. SUMMARY

[0005] Methods and systems are described for automated simulation and calibration of a model. According to one embodiment, a computer-implemented method is provided for calibrating a building energy model and based thereon providing feedback regarding one or more energy conservation measures (ECMs). A building energy model for a building at issue is calibrated by simulating energy usage for the building at issue and comparing simulated results to actual utility data for the building at issue. Then, feedback regarding one or more ECMs is provided by determining a result of implementing the one or more ECMs for the building at issue by simulating energy usage for the building at issue based on the calibrated building energy model and the one or more ECMs. In one embodiment, the method further involves calculating or estimating carbon emission savings associated with implementation of the one or more ECMs for the building at issue.

[0006] Other features of embodiments of the present invention will be apparent from the accompanying drawings and from the detailed description that follows.

BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Embodiments of the present invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to similar elements and in which:

[0008] FIG. 1 is a context level diagram illustrating potential interactions with an automated building energy model simulation and calibration system in accordance with an embodiment of the present invention.

[0009] FIG.2 is a system level diagram conceptually illustrating an architecture of an automated building energy model simulation and calibration system in accordance with an embodiment of the present invention.

[0010] FIG.3 illustrates a portion of a parameter key file in accordance with an embodiment of the present invention.

[0011] FIGs. 4A and 4B illustrate examples of parameter files in accordance with an embodiment of the present invention.

[0012] FIG. 5 illustrates a sample of a population table in accordance with an embodiment of the present invention.

[0013] FIG. 6 is an example of a computer system with which embodiments of the present invention may be utilized.

[0014] FIG. 7 is a high-level flowchart providing an overview of process flow for an automated building energy model simulation and calibration system in accordance with an embodiment of the present invention.

[0015] FIG. 8 is a flowchart illustrating building parameter processing in accordance with an embodiment of the present invention.

[0016] FIG. 9 is a flowchart illustrating utility data processing in accordance with an embodiment of the present invention.

[0017] FIG. 10 is a flowchart illustrating schedule processing in accordance with an embodiment of the present invention.

[0018] FIG. 11 illustrates a portion of a population member list in accordance with an embodiment of the present invention. [0019] FIG. 12 is a flowchart illustrating master population table creation processing in accordance with an embodiment of the present invention.

[0020] FIG. 13 is a simulation results dialog box in accordance with an embodiment of the present invention.

[0021] FIG. 14 is a simulation results schedule dialog in accordance with an embodiment of the present invention.

[0022] FIG. 15 is a flowchart illustrating simulation processing in accordance with an embodiment of the present invention.

[0023] FIG. 16 is a high-level flowchart providing an overview of automatic calibration processing in accordance with an embodiment of the present invention.

[0024] FIG. 17 is a flowchart illustrating heating available schedule creation processing in accordance with an embodiment of the present invention.

[0025] FIG. 18 is a flowchart illustrating shade schedule adjustment processing in accordance with an embodiment of the present invention.

[0026] FIG. 19 is a flowchart illustrating thermostat set point adjustment processing in accordance with an embodiment of the present invention.

[0027] FIG. 20 is an ECM selection screen in accordance with an embodiment of the present invention.

[0028] FIG. 21 is an ECM results screen in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION

[0029] Methods and systems are described for automated simulation and calibration of a model. In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent, however, to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details. In other instances, well-known structures and devices are shown in block diagram form.

[0030] Embodiments of the present invention include various steps, which will be described below. The steps may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general- purpose or special-purpose processor programmed with the instructions to perform the steps. Alternatively, the steps may be performed by a combination of hardware, software, firmware and/or by human operators.

[0031] Embodiments of the present invention may be provided as a computer program product, which may include a machine-readable storage medium tangibly embodying thereon instructions, which may be used to program a computer (or other electronic devices) to perform a process. The machine-readable medium may include, but is not limited to, fixed (hard) drives, magnetic tape, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, PROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions (e.g., computer programming code, such as software or firmware). Moreover, embodiments of the present invention may also be downloaded as one or more computer program products, wherein the program may be transferred from a remote computer to a requesting computer by way of data signals embodied in a carrier wave or other propagation medium via a communication link (e.g., a modem or network connection).

[0032] In various embodiments, the article(s) of manufacture (e.g., the computer program products) containing the computer programming code may be used by executing the code directly from the machine-readable storage medium or by copying the code from the machine-readable storage medium into another machine-readable storage medium (e.g., a hard disk, RAM, etc.) or by transmitting the code on a network for remote execution. Various methods described herein may be practiced by combining one or more machine-readable storage media containing the code according to the present invention with appropriate standard computer hardware to execute the code contained therein. An apparatus for practicing various embodiments of the present invention may involve one or more computers (or one or more processors within a single computer) and storage systems containing or having network access to computer program(s) coded in accordance with various methods described herein, and the method steps of the invention could be accomplished by modules, routines, subroutines, or subparts of a computer program product

[0033] For sake of brevity, examples of model calibration and simulation are provided herein in the context of a particular building energy model as applied to residential buildings. Those of ordinary skill in the art will appreciate the broader applicability to other models and to other types of buildings, including but not limited to, public buildings, such as schools and commercial buildings, such as offices, hotels and retail stores.

[0034] Notably, while embodiments of the present invention may be described using modular programming terminology, the code implementing various embodiments of the present invention is not so limited. For example, the code may reflect other programming paradigms and/or styles, including, but not limited to object-oriented programming (OOP), agent oriented programming, aspect-oriented programming, attribute-oriented programming (AOP), automatic programming, dataflow programming, declarative programming, functional programming, event- driven programming, feature oriented programming, imperative programming, semantic-oriented programming, functional programming, genetic programming, logic programming, pattern matching programming and the like. Terminology

[0035] Brief definitions of terms used throughout this application are given below.

[0036] The terms "connected" or "coupled" and related terms are used in an operational sense and are not necessarily limited to a direct connection or coupling.

[0037] The phrases "in one embodiment," "according to one embodiment," and the like generally mean the particular feature, structure, or characteristic following the phrase is included in at least one embodiment of the present invention, and may be included in more than one embodiment of the present invention. Importantly, such phases do not necessarily refer to the same embodiment

[0038] If the specification states a component or feature "may", "can", "could", or ''might" be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.

[0039] The term "responsive" includes completely or partially responsive.

[0040] FIG. 1 is a context level diagram illustrating potential interactions with an automated building energy model simulation and calibration system 100 in accordance with an embodiment of the present invention. Depending upon the particular implementation, system 100 may be used as a research tool and/or for business purposes as a commercial or residential building energy simulation tool. In one embodiment, system 100 facilitates automated calibration of building energy models for a commercial or residential building of interest based on building parameters (e.g., physical and operational parameters) and utility information for the building of interest Based on the calibrated building energy model, the system 100 may further facilitate, among other things, energy auditing, simulation of desired scenarios and energy conservation measure (ECM) analysis (e.g., automated evaluation of proposed ECMs and automated identification of optimal ECMs based on a given budget).

[0041] According to the present example, system 100 interfaces with weather files 120, utility data 130 and a computer 111 operated by a user 110. System 100 may be implemented as one or more software routines/modules that are executed by one or more processors of computer 111 or system 100 may be a web service, cloud service or other online accessible service to which computer 111 may be permitted access as a result of user 110 being a subscriber.

[0042] In one non-limiting usage scenario, system 100 may be implemented by or otherwise used by a utility company to offer value-added services to its customers and/or prospective customers. For example, system 100 may be used by customers to identify potential ECMs that may save them money on their natural gas, water and/or electricity bills. Similarly, system 100 may be used to in connection with an energy auditing process or to otherwise assist with identifying trouble spots in one's home or business and improve its energy efficiency.

[0043] In embodiments described below, computer 111 is used by system 100 to present various user interface screens through which system 100 may prompt for and receive user input from user 110 and/or display results of simulation and/or ECM evaluation to user 110.

[0044] Weather files 120 may represent typical meteorological year (TMY) or similarly formatted weather files (e.g., TMY, TMY2 or TMY3, TRY, WYEC, IWEC2 data sets) specific to a given location. Ideally, weather files 120 also correspond to the same time period as utility data 130, but TMY data sets are not specific to a given year. One possible way to address weather file and utility bill period issues would be to calculate heating and cooling energy use statistics from utility bills and then use those statistics to calculate predicted energy consumption using a given weather file.

[0045] Utility data 130 may be manually input by user 110, accessed from appropriate utility services or accessed from a previously stored and user-identified utility file. In one embodiment, user 110 is requested to input or otherwise make available monthly utility bill data, including days in the billing period, average temperature, kilowatt hour (kWh) consumption and natural gas therm consumption for consecutive months over a predetermined amount of time (e.g., at least a 12-month period). In some embodiments, user 110 may download the desired monthly utility bill data from their utility service provider and upload such data to system 100. Alternatively, system 100 may be authorized by user 110 to directly access the desired monthly utility bill data directly from the utility service provider. [0046] FIG.2 is a system level diagram conceptually illustrating an architecture of an automated building energy model simulation and calibration system 200 in accordance with an embodiment of the present invention. According to the present example, system 200 includes a user input processing module 220, an auto-calibration module 22S, a simulation module 230, an optimization module 260, an energy consumption calculation module 240, a geometry calculation module 250, parameter files 270, population tables 280 and templates 290.

[0047] User input processing module 220 may implement the various input dialogs and results dialogs to collect user input and present results.

[0048] Auto-calibration module 225 may run multiple simulations to identify a selection for "unknown" building parameters that results in energy use most closely resembling mat of the utility data for the building at issue.

[0049] Simulation module 230 implements logic relating to the simulation of a given population member in the context of a calibrated or uncalibrated building energy model.

[0050] Optimization module 260 may implement one or more search optimization methods (e.g., a Bayesian optimization algorithm, a genetic algorithm, etc.) to identify an optimal solution from a large population of possible combinations. Alternatively, the process may be approached in a systematic manner rather than seeking an optimal solution. In some circumstances, a systematic approach may be desirable to minimize the number of iterations required

[0051] Energy consumption calculation module 240 determines energy consumption for a particular population member being simulated.

[0052] Geometry calculation module 250 may include calculation of square footage and or surface areas based on user-defined shapes provided through a drawing tool or from pre-defined shapes, such as rectangle, trapezoid, cross, L-shape, U- Shape, H-shape and T-shape, each of which can be associated with multiple (e.g., two) characteristic dimensions. For example, the characteristic dimensions used for a rectangle may be the length and width. [0053] Parameter files 270 may include parameter data values input by the user and/or intermediate parameter data values generated by system 200. As described further below, examples of parameter data values that may be input by the user are building parameters, which may include basic information, building envelope, heating, ventilation and air conditioning (HVAC) systems, thermostat settings, building geometry, window properties and the like. A feature helpful for research is the ability to save and re-use inputs, which eliminates the need to navigate through the input screens for each run. As such, in one embodiment, costs and parameter data may be stored in a single text file to facilitate additions and updates.

[0054] In one embodiment, files used in system 200 are tab-delimited text format files. In various embodiments described herein, system 200 frequently utilizes hash tables in which each record contains a paired identifier and a value (also known as key-value pair). Parameter files 270 may be stored using this format. System 200 may be architected in such a manner to ensure that all files associated with a particular project are stored in an associated project folder. In this manner, multiple projects can be run using different project folders without affecting each other.

[0055] Population tables 280 may include a master population table, subset population tables and tables used to collect simulation results. As described further below, the master population table contains lines for every possible combination of selected parameters whether they have been simulated or not The subset population table may be utilized to keep track of results for a given subset When an optimization method is in use, the subset table may contain only a fraction of the records contained in the master; however, when no optimization method is in use, the subset and the master tables will be the same. In one embodiment, an ECM evaluation process also generates a population table to keep track of the results of individual ECMs and the selected package.

[0056] According to one embodiment, templates 290 are text files containing special markers of "tokens" that serve as placeholders for specific data. The tokens can be identified and replaced with appropriate data. In various examples contained herein, tokens are shown as being contained within percent signs ('%') or exclamation points ('!'). Those skilled in the art will recognize that various other token identifiers may be used In accordance with an embodiment of the present invention, tokens within percent signs are to be replaced with a single value (e.g., an R-Value), whereas tokens within exclamation points are to be replaced with an array (e.g., a schedule containing multiple lines). The use of two different token identifiers is convenient for processing templates in the context of a Perl programming language implementation. A non-limiting example of a template line containing a token is the following:

NUMBER -OF -PEOPLE = %OCC-perFLR%

[0057] When processed, this line may become something like:

NUMBER -OF -PEOPLE = 2

[0058] According to one embodiment, templates are utilized extensively in the construction of simulation input files, which are the product of the above-described template token-replacement process.

[0059] System 200 may be coupled in communication with a public or private network 210, such as the Internet or a local area network (LAN) through which weather files 120 and/or utility data 130 may be accessed, for example. To the extent the functional units of system 200 are implemented separate and apart from an end user's computer system (e.g., computer 111), network 210 may provide the means through which the end user's computer system interacts with system 200.

[0060] While in the environment of the present example, the various functional units could be understood as residing within or as part of a single physical system, in alternative embodiments one or more of these functional units may be implemented within distributed physical systems. For example one server may be dedicated to performing calibration and another may be dedicated to performing ECM evaluation.

[0061] in one embodiment, the functionality of one or more of the above- referenced functional units may be merged in various combinations. For example, according to one embodiment, all back-end processing involving the preparation of a population for simulation, processing of simulation results and processing of user inputs is contained in a single Perl module. All processing related to the simulation of a given population member may be contained in another Perl module. This Perl module may also be where processing of simulation template files, energy consumption calculations, geometry calculations and most of the schedule processing occurs. [0062] Moreover, the various functional units can be communicatively coupled using any suitable communication method (e.g., message passing, parameter passing, and/or signals through one or more communication paths, etc.)- Additionally, the functional units can be physically connected according to any suitable interconnection architecture (e.g., fully connected, hypercube, etc.).

[0063] According to embodiments of the invention, the functional units can be any suitable type of logic (e.g., digital logic, software code and the like) for executing the operations described herein. Any of the functional units used in conjunction with embodiments of the invention can include machine-readable media including instructions for performing operations described herein. Machine-readable media include any mechanism that provides (i.e., stores and/or transmits) information in a form readable by a machine (e.g., a computer). For example, a machine-readable medium includes read only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media or flash memory devices.

[0064] FIG. 3 illustrates a portion of a parameter key file 300 in accordance with an embodiment of the present invention. According to the present example, parameter key file 300 is essentially a text file database supplying parameter data to they system. Most possible parameter values are contained within parameter key file 300. Parameter key file 300 includes information on all possible menu selections as well as cost and other data associated with those selections.

[0065] FIGs. 4A and 4B illustrate examples of parameter files in accordance with an embodiment of the present invention. In the context of the present example, parameter files are hash tables containing sets of parameters. As an example of one possible naming convention, the names of building parameter files may begin with "BldgParm." In general, parameter files can contain values or selections. Values may represent descriptions of the item at issue (e.g., BuildingYear = 2002 or HtgFuelType = naturalgas), while selections may be integer values indicating a particular menu selection (e.g., Location = 3 or BuildingType = 1). Names of building parameter files containing values may begin with the prefix "BldgParmVal," while those containing selections may begin with the prefix "BldgParmSel." [0066] FIG. 5 illustrates a sample of a population table 500 in accordance with an embodiment of the present invention. According to one embodiment, population tables, such as population table 500, are used to collect simulation results. The columns in population table 500 are: ID (Col. A), Score (Col. B), annual kWh (Col. C - "ETotkWH"), 12 columns for Jan - Dec kWh (Cols. D through O), annual Therms (Col. N - "GTotTherms"), 12 columns for Jan - Dec Therms (not shown), total 30- year LCC (not shown), columns for initial cost of sub-components (not shown).

[0067] As noted above, in one embodiment, a master population table contains lines for every possible combination of selected parameters whether they have been simulated or not A subset population table may also be utilized to keep track of results for a given subset. When no optimization method is employed, the subset and master tables are the same. When an optimization method is in use, however, the subset table would contain a fraction of the records contained in the master. The ECM evaluation process may also generate a population table to keep track of results of individual ECMs and the selected package.

[0068] FIG. 6 is an example of a computer system with which embodiments of the present invention may be utilized Embodiments of the present invention include various steps, which will be described in more detail below. A variety of these steps may be performed by hardware components or may be tangibly embodied on a computer-readable storage medium in the form of machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor programmed with instructions to perform these steps. Alternatively, the steps may be performed by a combination of hardware, software, and/or firmware. As such, FIG.6 is an example of a computer system 600, such as a workstation, personal computer, laptop, client or server upon which or with which embodiments of the present invention may be employed.

[0069] According to the present example, the computer system includes a bus 630, one or more processors 605, one or more communication ports 610, a main memory 615, a removable storage media 640, a read only memory 620 and a mass storage 625. [0070] Processors) 60S can be any future or existing processor, including, but not limited to, an Intel® Itanium® or Itanium 2 processors), or AMD® Opteron® or Athlon MP® processors), or Motorola® lines of processors. Communication port(s) 610 can be any of an RS-232 port for use with a modem based dialup connection, a 10/100 Ethernet port, a Gigabit port using copper or fiber or other existing or future ports. Communication port(s) 610 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system 600 connects.

[0071] Main memory 61S can be Random Access Memory (RAM), or any other dynamic storage device(s) commonly known in the art Read only memory 620 can be any static storage device(s) such as Programmable Read Only Memory (PROM) chips for storing static information such as start-up or BIOS instructions for processor 605.

[0072] Mass storage 62S may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), such as those available from Seagate (e.g., the Seagate Barracuda 7200 family) or Hitachi (e.g., the Hitachi Deskstar 7K1000), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, such as an array of disks (e.g., SATA arrays), available from various vendors including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc. and Enhance Technology, Inc.

[0073] Bus 630 communicatively couples processors) 60S with the other memory, storage and communication blocks. Bus 630 can include a bus, such as a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X), Small Computer System Interface (SCSI), USB or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects the processors) 60S to system memory.

[0074] Optionally, operator and administrative interfaces, such as a display, keyboard, and a cursor control device, may also be coupled to bus 630 to support direct operator interaction with computer system 600. Other operator and administrative interlaces can be provided through network connections connected through communication ports 610.

[0075] Removable storage media 640 can be any kind of external hard-drives, floppy drives, IOMEGA® Zip Drives, Compact Disc - Read Only Memory (CD- ROM), Compact Disc - Re-Writable (CD-RW), Digital Video Disk - Read Only Memory (DVD-ROM).

[0076] Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the invention.

[0077] FIG. 7 is a high-level flowchart providing an overview of process flow for an automated building energy model simulation and calibration system in accordance with an embodiment of the present invention. Depending upon the particular implementation, the various process and decision blocks described herein may be performed by hardware components, embodied in machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor programmed with the instructions to perform the steps, or the steps may be performed by a combination of hardware, software, firmware and or involvement of human participation/interaction.

[0078] At block 705, building parameter entry is performed. This first part of the process is to collect information to facilitate simulations. According to one embodiment, a dialog, such as that depicted in FIG. 13 is presented to the end user to prompt for and receive selected building parameters for the building at issue. Further information regarding building parameter entry in accordance with an embodiment of the present invention is described with reference to FIG. 8.

[0079] At block 710, utility data processing is performed As described further below, according to one embodiment, the process is only performed when the system is run in auto-calibration mode. Depending upon availability of utility data (e.g., utility data 130), utility data processing may involve, among other things, accessing utility data 130 from a utility service provider or may involve manual data entry by the end user. Further information regarding utility data processing in accordance with an embodiment of the present invention is described with reference to FIG. 9.

[0080] At block 7 IS, schedule entry is performed to collect and process schedule data. The schedule data may include information, such as hourly day schedules for occupancy, hot water, lighting and equipment usage. According to one embodiment, the schedule entry process occurs only during the initial entry of data. In such an embodiment, modifications to schedules after the initial entry of data are handled in connection with the display simulation results process. Further information regarding schedule entry in accordance with an embodiment of the present invention is described with reference to FIG. 10.

[0081] At block 720, a manage population process is performed. According to one embodiment this process involves making a master population table 746 and selecting a subset of population members (e.g., subset population table 747) from master population table 746 for which simulations are to be performed. The subset is selected by running an optimization routine. The optimization routine may be a predetermined or configurable optimization method (e.g., a Bayesian optimization algorithm, a genetic algorithm, etc.) or no optimization at all. When an optimization routine is employed, subset population table 747, representing a selected subset of population members from master population table 746, is created upon which simulations are performed. When no optimization is employed, selection of the subset simply involves copying the entire master population table 746 to the subset population table 747. At this point, the simulations are performed by calling a run simulations process, an example of which is illustrated by the flow diagram of FIG. 15.

[0082] At block 725, the simulation results are displayed on a display device of the computer system (e.g., computer 111). According to one embodiment, display of the simulation results involves presenting the selected building parameters and monthly schedules for editing. For example, a user interface dialog may present the selected building parameters. An exemplary simulation results dialog box containing the selected building parameters is depicted in FIG. 13. An exemplary simulation results schedule dialog containing monthly schedule results is depicted in FIG. 14. [0083] At decision block 730, it is determined if a request has been received by the system to save the current set of inputs. If so, then processing branches to block 73S; otherwise, processing continues with decision block 740.

[0084] At block 73S, a process is performed to save the current simulation inputs. According to one embodiment, this involves storing information that identifies the current simulation inputs. According to one embodiment, a corresponding tag string (identified in parenthesis) is created and stored for each of hour schedule parameters 734a (hour schedule tag string 736a), the monthly schedule parameters 734b (monthly schedule tag siring 736b), the low score building parameter values 734c (building parameter values tag string 736c) and the utility data 734d (utility data tag string 736d). For example, a string, i.e., a population member ID, can be stored that can be cross-referenced with the parameter key and population key to identify which parameter set is represented. In one embodiment, the string is made up of the "Alpha ID" of each parameter in the population member.

[0085] At decision block 740, it is determined if a request has been received by the system to perform simulations. If so, processing continues with block 745; otherwise, processing branches to decision block 750.

[0086] At block 745, the manage population process is performed as described earlier with reference to block 720 and processing continues with block 725.

[0087] At decision block 74S, it is determined if a request has been received by the system to perform auto-calibration processing. If so, processing continues with block 7SS; otherwise, processing branches to decision block 760.

[0088] At block 755, auto-calibration processing is performed. In one embodiment, a building energy model (e.g., an EnergyPlus energy model, a DOE2 energy model, such as the DOE-2.2 building energy simulation and cost calculation engine described in James J. Hirsch & Associates, DOE-2.2: Building Energy Use and Cost Analysis Program, Volume 1: Basics, October 2004), or the like, for the building at issue is automatically calibrated based on simulated energy usage for the building at issue and by comparing the simulated results to actual utility data for the building at issue. According to one embodiment, automatic calibration processing is performed after all building parameters have been identified. In this manner, all that the automatic calibration processing needs to do is adjust schedules. Further information regarding automatic calibration processing in accordance with an embodiment of the present invention is described with reference to FIGs. 16-19.

[0089] At decision block 760, it is determined if a request has been received by the system to perform ECM evaluation. If so, processing continues with block 765; otherwise, processing is complete.

[0090] At block 76S, information regarding individual ECMs to be evaluated are received. According to one embodiment, ECM selection is via a user interface dialog, an example of which is depicted in FIG. 20. According to one embodiment^ after the ECMs have been identified, they are simulated individually and the individual results are stored to an ECM evaluation population table 771. The results may be presented in an ECM results screen, an example of which is depicted in FIG. 21. As described further below, the results may include information regarding energy savings, initial costs and life cycle cost for each ECM individually.

[0091] At block 770, input is received from the end user regarding whether the individual ECMs identified and for which results were presented in block 765 are to be evaluated as a package. According to one embodiment, after the user has indicated his/her desire to evaluate a package of two or more ECMs together, the selected package of ECMs is simulated to calculate costs and energy savings, including interactive effects, and the results may be again stored to ECM evaluation population table 771. The results may also be presented via the ECM results screen.

[0092] FIG. 8 is a flowchart illustrating building parameter processing in accordance with an embodiment of the present invention. In one embodiment, the building parameter processing described with reference to FIG. 8 represents exemplary processing that may be associated with block 705 of FIG. 7.

[0093] At block 810, information (e.g., user input 811) is received regarding the desired operation of the system. According to one embodiment, a user interface dialog is displayed to collect information, including a run mode (e.g., minimum cost or calibration), a project folder, a selected simulation engine (e.g., DOE2 or other) and whether to use input screens or files for building parameters, utility data and schedules. [0094] At block 820, the information collected is stored. According to one embodiment, once the user interface dialog for the system operational parameters has been completed, a run parameters file 812 is updated.

[0095] At decision block 830, it is determined if input dialogs have been requested to be used. If so, men one or more user interface dialogs (e.g., FIG. 13) may be presented for input

[0096] At block 850, a building parameter input values file 842 is created based on user input 841 provided via the one or more user interface dialogs or based on a user provided parameter file name 8S1. In the present example, certain assumed building parameter values, including, but not limited to window height, wind shielding coefficient, initial balance temperature, domestic/commercial hot water supply temperature and/or outside air film R-value, may be received from a building parameter values assumed file 8S2 and added to the building parameter input values file 842. In one embodiment, more advanced users may be provided an opportunity to override these assumed values.

[0097] FIG. 9 is a flowchart illustrating utility data processing in accordance with an embodiment of the present invention. In one embodiment, the utility data processing described with reference to FIG. 9 represents exemplary processing that may be associated with block 710 of FIG. 7.

[0098] At decision block 910, it is detennined whether the system is to be run in calibration mode. According to one embodiment, the run mode is read from run parameters file 812, which, in one embodiment, is updated in block 820 of FIG. 8. If the system is to be run in calibration mode, then processing continues with decision block 920; otherwise processing is complete (as utility data processing need not be performed for minimum cost run mode).

[0099] At decision block 920, it is determined whether the system has been requested to display utility data input dialogs to collect user input and create a utility data user interface file 931. According to one embodiment, this system configuration information is read from run parameters file 812, which, in one embodiment, is updated in block 820 of FIG. 8. If the system is to display utility data input dialogs, then processing continues with block 930; otherwise, processing branches to block 940.

[0100] At block 930, utility data input dialogs are presented to the end user and utility data input (e.g., user input 932) is collected from the end user. According to one embodiment, the utility data input includes monthly utility bill data, including days in the billing period, average temperature, kilowatt hour (kWh) consumption and natural gas therm consumption for consecutive months over a predetermined amount of time (e.g., at least a 12-month period). According to one embodiment, the utility data may be entered via the utility data input dialogs. Alternatively, the user may provide account information (e.g., username and password) to the system to allow the system to directly access online-accessible utility data from the user's utility service provider. In yet another alternative embodiment, the user may download the desired monthly utility bill data from their utility service provider and simply specify a name and/or location of a utility file 941 in which the monthly utility bill data is contained.

[0101] At block 940, the utility data is processed. According to one embodiment, utility data processing involves taking the input and calculating monthly averages for temperature, energy consumption and degree-days.

[0102] At block 950, the averages for degree-days are calculated. According to one embodiment, heating degree-days and cooling degree-days are defined by the following equations and results are stored to one or more of a utility data hash file 931 and a utility data processed file 9S2:

Where:

T_b = Balance Temperature

T₀ = Outside Air Temperature

N_H = Number of Days in Heating Season N_c = Number of Days in Cooling Season

[0103] When daily temperature data is not available, according to one embodiment, heating and cooling season days are calculated based on average billing period temperature and billing period days.

[0104] At block 960, base load calculations are performed In one embodiment, base load calculations for natural gas are based on gas use that occurs outside of the heating season, typically summer months. This information along with domestic hot water efficiency is used to calculate base load domestic hot water consumption.

[0105] First, the mass of monthly hot water may be calculated as follows:

[0106] Then, the mass of hot water may be converted into gallons:

[0107] The resulting volume is then available to be used in the simulation. According to one embodiment, the last part of the process is to calculate the correlation of natural gas and electricity use to outdoor air temperature using linear regression, for example. The output of this process may be stored in a building parameter values baseload file 961, which may then be merged into building parameter input values file 9S3.

[0108] FIG. 10 is a flowchart illustrating schedule processing in accordance with an embodiment of the present invention. In one embodiment, the schedule processing described with reference to FIG. 10 represents exemplary processing that may be associated with block 715 of FIG. 7. According to the present example, the primary output of this process is an hourly schedule simulation file 1031 and a monthly schedule simulation file 1032.

[0109] At decision block 1010, it is determined whether the system is to be run in calibration or minimum cost mode. According to one embodiment, the run mode is read from run parameters file 812, which, in one embodiment, is updated in block 820 of FIG. 8. If the system is to be run in either calibration mode or minimum cost mode, then processing continues with decision block 1020; otherwise processing is complete (as schedule processing need not be performed unless the system is being run in calibration or minimum cost run mode).

[0110] At decision block 1020, it is determined whether the system has been requested to display schedule data input dialogs to collect user input and create hourly schedule simulation file 1031 and monthly schedule simulation file 1032. According to one embodiment, this system configuration information is read from run parameters file 812, which, in one embodiment, is updated in block 820 of FIG. 8. If the system is to display schedule data input dialogs, then processing continues with block 1040; otherwise, processing branches to block 1030.

[0111] At block 1040, an hourly schedule data input dialog is presented to the end user and inputs are collected. According to one embodiment, hourly day schedules for occupancy, domestic hot water, lighting and equipment use are collected and hourly schedule simulation file 1031 is created.

[0112] At block 10S0, a monthly schedule data input dialog is presented to the end user. According to one embodiment, a screen showing the monthly schedules is shown for user editing and monthly schedule simulation file 1032 is created. At this point, schedule processing is complete.

[0113] At block 1030, it has been determined the system has been requested not to display schedule data input dialogs to collect user input, therefore, the system copies a user-specified custom hourly schedule file 1033 and a user-specified custom monthly schedule file 1034 to hourly schedule simulation file 1031 and monthly schedule simulation file 1032, respectively. At this point, schedule processing is complete. [0114] FIG. 11 illustrates a portion of a population member list 1100 in accordance with an embodiment of the present invention. As described earlier, in one embodiment, a population member ID is a siring that can be cross-referenced with the parameter key and population key to identify which parameter set is represented. This string can be made from the "Alpha ID" of each parameter in the population member. Once the population key has been created, all possible combinations of the unknown parameters can be enumerated. In one embodiment, this enumeration can be performed with reference to the population and parameter key files.

[0115] FIG. 12 is a flowchart illustrating master population table creation processing in accordance with an embodiment of the present invention. According to one embodiment, the process described below is used to create master population table 746 for use in connection with the simulation processing and automatic calibration processing of FIG. 7. In one embodiment, given a population list (e.g., building parameter input values file 1211) and a parameter key file (e.g., building parameter key file 1221), simulations are run for each population member in the population list and results are recorded.

[0116] At block 1210, a population key is made. According to one embodiment, building parameter input values file 1211 is read and sorted by parameter value to get the entries with unknown values (e.g., marked as "!unknown") to the top of the file. The sorted data is then saved as building parameter value population key 1212 (the population key file), which can be used to identify which parameters make up the population member ID.

[0117] At block 1220, combinations of the unknown parameters are enumerated. According to one embodiment, the population and parameter key files are read and all possible combinations of the unknown parameters (e.g., those marked with "!unknown") are enumerated. An example of a population member list is depicted in FIG. 11. Notably, at times, a user believes a particular building parameter is one of a limited set of options. In one embodiment, rather than requiring the user to select "unknown" in such circumstances, which typically results in simulation of a large number of potentially irrelevant combination, the user interface may allow the user to select a couple of possible items from a parameter list, thereby reducing simulation processing.

[0118] At block 1230, a master population member list is created. According to one embodiment, the master population member list is created based on the enumerations created in block 1220.

[0119] At decision block 1240, it is determined whether the system has been requested to be run in calibration mode or ECM evaluation mode. According to one embodiment, the run mode is read from run parameters file 812, which, in one embodiment, is updated in block 820 of FIG. 8. If the system is to be run in calibration mode or ECM evaluation mode, then processing continues with block 1260; otherwise processing branches to block 1250.

[0120] At block 1260, energy consumption reference information is loaded. According to one embodiment, the reference information points to the utility data (for calibration mode) or the calibrated simulation results (for ECM evaluation mode).

[0121] At block 1250, master population table 1251 is created based on the master population member list created in block 1230 and with the reference information, if any, loaded at block 1260.

[0122] FIG. 13 is a simulation results dialog box 1300 in accordance with an embodiment of the present invention. According to the present example, simulation results dialog box 1300 displays/collects selected building parameters, non-limiting examples of which include basic information 1310, building envelope 1320, HVAC systems 1330, thermostat settings 1340, building geometry 1350 and window properties 1360.

[0123] In the present example, basic information 1310 includes location 1311, building type 1312, year 1313, bedroom quantity 1314 and occupant quantity 1315. Location 1311 may be specified by a combination of city and state. Building type 1312 may be selected from a group comprising one or more of "Single Family Residence," "Patio Home," "Villa," "Cottage," "Cabin," ''Courtyard Home," "Duplex," 'Triplex," Quadraplex," 'Townhome," "Apartment," "Carriage Home," ' ffice Building," "School," "Hotel," "Retail Store" and "Condominium." Those skilled in the art will recognize various other options for residential and/or commercial building type designations. Bedroom quantity 1314 is typically an integer number representing the number of bedrooms in use. Occupant quantity 1315 is an integer number representing me number of occupants residing in or working in the building at issue.

[0124] In the present example, building envelope 1320 includes foundation type 1321, slab insulation 1322, building mass 1323, wall type 1324, attic insulation 132S and infiltration 1326. Building envelope 1320 may also include window glazing type and shading features.

[0125] Foundation type 1321 may be selected from a group comprising one or more of 'Basement," "Concrete- T-Shaped," "Concrete- Slab-on-grade," '"'Concrete- Frost Protected," "Permanent Wood," "Raised" and typically depends upon the type of building. Those skilled in the art will recognize various other options for residential and/or commercial foundation type designations.

[0126] Slab insulation 1322 may be selected from a predefined list of options including one or more of full insulation along the entire slab floor, vertical insulation along the foundation footing walls, and partial insulation along the perimeter of the slab floor. Those skilled in the art will recognize various other options for residential and/or commercial slab insulation type designations.

[0127] Building mass 1323 may be selected from a predefined list of options including, but not limited to, light, medium, or heavy mass levels. The building mass can also be expressed in weight (in lbs. or kg) per unit floor area. Those skilled in the art will recognize various other options for residential and/or commercial building mass type designations.

[0128] Wall type 1324 may be selected from a predefined list of options including, but not limited to, wood frame, metal frame, concrete construction or brick construction. The user may also be provided with the ability to define the specific construction layers of the exterior walls. Those skilled in the art will recognize various other options for residential and/or commercial wall type designations.

[0129] Attic insulation 1325 may be selected from a group comprising one or more of the type of insulation layer and its R-value. Those skilled in the art will recognize various other options for residential and/or commercial attic insulation type designations.

[0130] Air infiltration 1326 may be selected from a group comprising one or more of a predefined list of options including typical, tight, and tighter levels. It can be also specified in terms of air changes per hour. Those skilled in the art will recognize various other options for air infiltration type designations.

[0131] In the present example, HVAC systems 1330 includes heating HVAC type 1331, heating type option 1332, heating efficiency 1333, cooling HVAC type 1334, cooling type option 133S, cooling efficiency 1336, DHW system 1337 and duct loss 1338.

[0132] Heating HVAC type 1331 may be selected from a group comprising one or more of a predefined list of options including packaged systems or central systems. Those skilled in the art will recognize various other options for residential and/or commercial heating HVAC type designations.

[0133] Heating type option 1332 may be selected from a group comprising one or more of a predefined list of options including electrical resistances, heat pumps, furnaces, and boilers. Those skilled in the art will recognize various other options for characterizing residential and/or commercial heating type options.

[0134] Heating efficiency 1333 may be selected from a wide range of values depending one whether the efficiency is expressed in terms of average seasonal fuel efficiency or coefficient of performance, for example. Those skilled in the art will recognize various other options for characterizing residential and/or commercial heating system efficiency.

[0135] Cooling HVAC type 1334 may be selected from a group comprising one or more of a predefined list of options including packaged systems or central systems. Those skilled in the art will recognize various other options for characterizing residential and/or commercial cooling HVAC system types.

[0136] Cooling type option 1335 may be selected from a group comprising one or more of a predefined list of options including, but not limited to, air conditioners, evaporative cooling, heat pumps, and chillers. Those skilled in the art will recognize various other options for characterizing residential and/or commercial cooling type options.

[0137] Cooling efficiency 1336 may be selected from a wide range of values depending on whether the efficiency is expressed in terms of seasonal electric efficiency ratio, integrated part load value or coefficient of performance, for example. Those skilled in the art will recognize various other options for characterizing residential and/or commercial cooling system efficiency.

[0138] DHW system 1337 may be selected from a group comprising one or more of a predefined list of options including tank sizes and fuel types. Those skilled in the art will recognize various other options for characterizing residential and/or commercial hot water systems.

[0139] Duct loss 1338 may be selected from a group comprising one or more of a predefined list of options including typical and improved. The duct loss can be also provided in terms of heat transfer rate or U-value, for example. Those skilled in the art will recognize various other options for characterizing residential and/or commercial duct loss.

[0140] in the present example, thermostat settings 1340 includes heating set point 1341, heating set back 1342, heating setback schedule 1343, cooling set point 1344, cooling setup 134S and cooling setup schedule 1346.

[0141] Heating set point 1341 is a temperature in degrees Fahrenheit or Celsius at or below which the heating HVAC system is set to turn on. Heating set back 1342 is an integer number of degrees or a percentage by which the heating set point 1341 may be reduced during predefined time intervals (e.g., low occupancy, unoccupied or sleep hours) defined by heating setback schedule 1343. Notably, setback temperatures are often dependent on recovery time of the HVAC equipment capacity to reestablish the normal occupied building temperature prior to occupants waking in the morning, occupants returning from work in the evening, people arriving for work or students arriving for school, for example.

[0142] Heating setback schedule 1343 may be selected from a group comprising one or more of a predefined set of setback schedules including, but not limited to, the hours of and level of heating temperature setback. Those skilled in the art will recognize various other options for characterizing residential and/or commercial heating system setback schedules.

[01 3] Cooling set point 1344 is a temperature in degrees Fahrenheit or Celsius at or above which the cooling HVAC system is set to turn on. Cooling setup 1345 is an integer number of degrees or a percentage by which the cooling set point 1344 may be increased during predefined time intervals (e.g., low occupancy, unoccupied or sleep hours) defined by cooling setup schedule 1346.

[0144] Cooling setup schedule 1346 may be selected from a group comprising one or more of a predefined set of setup schedules including, but not limited to, the hours of and level of cooling temperature setup. Those skilled in the art will recognize various other options for characterizing residential and/or commercial cooling system setup schedules.

[0145] In the present example, building geometry 1330 includes floor area 1351, floor quantity 1352, shape type 1353, aspect ratio 1354, PI and P2 1355 and azimuth 1356. Floor area 1351 represents the number of square feet above ground. Floor quantity 1352 represents the number of floors above ground.

[0146] Shape type 1353 may be user-defined shapes or selected from predefined shapes, such as rectangle, trapezoid, cross, L-shape, U-shape, H-shape and T- shape. Those skilled in the art will recognize various other options for characterizing residential and/or commercial building geometry shape types.

[0147] Aspect ratio 1354 may be selected for a predefined set of building floor shapes such as those listed above. The aspect ratio may be represented as characteristic dimension #1 (e.g., PI) over characteristic dimension #2 (e.g., P2). For example, the aspect ratio for a rectangular shaped building may be the ratio of width to length. Those skilled in the art will recognize various other options for characterizing residential and/or commercial building geometry aspect ratios.

[0148] PI and P2 1355 represent the characteristic dimensions for the selected floor shape. For a rectangular floor shape, PI may represent the length of the floor and P2 may represent the width, for example.

[0149] Azimuth 1356 generally relates to the orientation of the building at issue. For example, azimuth 1356 may be selected from 0 to 359 degrees and may represent a number of degrees West of South (e.g., the angle between a line perpendicular to a front face of the building at issue and true South).

[0150] In the present example, window properties 1360 include window type 1361, window-wall ratio (WWR) front 1362, WWR back 1363, WWR left 1364 and WWR right 1365.

[0151] Window type 1361 may be selected from a group comprising one or more of a predefined set of window glazing types including, but not limited to, single pane, double pane, and triple pane glazing. Those skilled in the art will recognize various other options for characterizing residential and/or commercial building window types.

[0152] WWR front 1362 is a decimal value typically between 0 and 1 representing a ratio of window area to wall area for a front face of the building at issue.

[0153] WWR back 1363 is a decimal value typically between 0 and 1 representing a ratio of window area to wall area for a back face of the building at issue.

[0154] WWR left 1364 is a decimal value typically between 0 and 1 representing a ratio of window area to wall area for a left face of the building at issue.

[0155] WWR right 1365 is a decimal value typically between 0 and 1 representing a ratio of window area to wall area for a right face of the building at issue. The user may also select from a predefined set of window shading options such as the length of the shade extending above the windows. The user can also provide the geometric details of the shades for all the windows.

[0156] In the present example, area 1370 of simulation results dialog box 1300 contains radio buttons that allow me end user to re-run the simulation, perform automatic calibration, perform ECM evaluation or exit the system

[0157] In the present example, check box 1375 allows the current simulation inputs to be saved.

[0158] According to one embodiment, score 1380 displays a score calculated based on how closely the simulated energy use matches the utility data. In one embodiment and as described further below, score 1380 may be determined based on an average percent error (APE) of the current simulation (range: greater than or equal to zero). In such an embodiment, lower scores (representing lower error) are better than high scores.

[0159] Schedules button 1385 may be selected by end user to display a simulation results schedule dialog, an example of which is depicted in FIG. 14.

[0160] Continue button 1390 may be selected by end user to perform the function selected by radio buttons in area 1370.

[0161] FIG. 14 is a simulation results schedule dialog 1400 in accordance with an embodiment of the present invention. According to one embodiment, simulation results schedule dialog 1400 is presented responsive to selection of the schedules button 1383 on simulation results dialog box 1300. According to the present example, simulation results schedule dialog 1400 displays results 1410 and displays/collects schedules 1420.

[0162] In the present example, results 1410 show simulation kWh, reference kWh and simulated kWh percent error by month.

[0163] Schedules 1420 display/collect values regarding me availability of heating and cooling by month. According to one embodiment, heating or cooling months are given a "1" value and non-heating or cooling months are given a "0" in the appropriate schedules. Transition months may be given a fraction value that identifies the percentage of the month that heating or cooling is available.

[0164] Schedules 1420 also display/collect information regarding shading, occupancy, domestic hot water, lighting and equipment. In one embodiment, schedules 1420 are a set of 24 values of fractions that indicate the typical daily operation of the building. The schedules 1420 may be represented in terms of a ratio of actual versus a reference value. For instance, an occupancy schedule provides the fraction of the maximum number of people occupying the building for each hour in a typical day. [0165] After the end user has reviewed and/or adjusted information contained within schedules 1420, the end user can select the continue button 1430 to return to simulation results dialog box 1300, for example.

[0166] FIG. 15 is a flowchart illustrating simulation processing in accordance with an embodiment of the present invention. In one embodiment, the simulation processing described with reference to FIG. 15 represents exemplary processing that may be called by blocks 720 and 745 of FIG. 7 and that may be associated with block 1660 of FIG. 16. In general, given a population list (i.e., population list subset file 1521) and a parameter key file (i.e., building parameter key file 1523), simulations are run for each member of the population list and results are recorded.

[0167] At block 1510, a weather hash file 1513 is created. According to one embodiment, building parameter input values file 1612 is first checked to determine the location of the building at issue. Then, a weather file 1511 associated with that location is loaded and the hourly data contained therein is processed to create a weather hash file 1513 containing daily average temperatures.

[0168] At blocks 1520 and 1530, a simulation input parameter file 1531 is created. According to one embodiment, the first member of population list subset file 1521 is cross referenced using population key file 1522 and building parameter key file 1523 to create the simulation input parameter file 1531 containing all of the values for that population member.

[0169] At block 1540, the simulation input parameter file 1531 is simulated with reference to the desired energy model (e.g., EnergyPlus, DOE2 or the like). According to one embodiment, simulation input parameter file 1531 is read, appropriate calculations are performed, the necessary templates are loaded and an input file is assembled that is formatted for the desired energy model. The results of the simulation are then read, scored and written to master population table 1551 and subset population table 1552. With processing of the current population member complete, processing loops back to block 1520 where the next member is selected from population list subset file 1521 and processed until all members have been processed. [0170] At block 1S60, after all members of population list subset file 1S21 have been processed, the lowest-scoring member is identified and those parameter values are saved to a building parameter value low score file 1561. The low-scoring simulation results (energy consumption) are also saved to a simulation results hash file 1S62.

[0171] FIG. 16 is a high-level flowchart providing an overview of automatic calibration processing in accordance with an embodiment of the present invention. In one embodiment, the automatic calibration processing described with reference to FIG. 16 represents exemplary processing that may be associated with block 755 of FIG. 7. In general, the automatic calibration process seeks to adjust schedules to achieve the best score possible. According to one embodiment, the automatic calibration process takes place after all building parameters have been identified, so all that need be done at this point is to adjust schedules.

[0172] At block 1610, heating and cooling available schedules are set According to one embodiment, this is done using a weather hash file 1611 created for the selected location (e.g., block 1510 of FIG. 15) and heating and cooling balance temperatures for the given heating and cooling equipment. As described above, according to one embodiment, heating or cooling months are given a "1" value and non-heating or cooling months are given a "0" in the appropriate schedules. Transition months may be given a fraction value that identifies the percentage of the month that heating or cooling is available. Further information regarding heating available schedule creation processing in accordance with an embodiment of the present invention is described with reference to FIG. 17.

[0173] At block 1620, a monthly schedule simulation file 1621 is created. According to one embodiment, this involves converting Julian days to a fraction of a month.

[0174] At block 1630, a shade schedule is automatically set based on monthly schedule simulation file 1621. According to one embodiment, this process looks at simulated energy use error and adjusts the shading schedule according to whether it is a heating or cooling month. For example, if it is a heating month and simulated fuel use is too high then the shading schedule will be adjusted higher to simulate more sunlight entering the space. Further information regarding shade schedule adjustment processing in accordance with an embodiment of the present invention is described with reference to FIG. 18.

[0175] At block 1640, a thermostat schedule is automatically set Further information regarding thermostat set point adjustment processing in accordance with an embodiment of the present invention is described with reference to FIG. 19.

[0176] At block 1650 remaining error is automatically allocated by looking at simulated energy use error and making appropriate adjustments to the shading schedule.

[0177] At block 1660, simulations are performed a predetermined or configurable number of times while iterating blocks 1630 and 1650. The lowest- scoring schedule is identified and the schedule and score is saved to a simulation results low score file 1652.

[0178] FIG. 17 is a flowchart illustrating heating available schedule creation processing in accordance with an embodiment of the present invention. In one embodiment, the heating available schedule creation processing described with reference to FIG. 17 represents exemplary processing that may be associated with block 1610 of FIG. 16. In general, the setting of the heating available schedule creates a heating available schedule based on a weather hash file 1711 and a building parameter input value file 1706. In one embodiment, the determination of whether heating is available is done by comparing the balance temperature to a moving average temperature. The moving average temperature may be calculated over a predetermined or configurable number of days (e.g., 3, 5, 7 or 10) to smooth out minor fluctuations. Those skilled in the art will be able to extend the present example to creation of a cooling available schedule.

[0179] FIG. 18 is a flowchart illustrating shade schedule adjustment processing in accordance with an embodiment of the present invention. In one embodiment, the shade schedule adjustment processing described with reference to FIG. 18 represents exemplary processing that may be associated with block 1630 of FIG. 16. In general, the shade schedule is adjusted based on heating or cooling season and energy use.

[0180] At block 1810, the heating available schedule and the cooling available schedule are read from a monthly schedule simulation file 1811 (e.g., created in blocks 1610 and 1620 of FIG. 16).

[0181] At block 1820, each month of the shade schedule is initialized to a predetermined or configurable value (e.g., 0.5).

[0182] At decision block 1830, it is determined whether the current month being processed is a cooling month. If so, them processing continues with decision block 1840; otherwise, processing branches to decision block 1860.

[0183] At decision block 1840, during cooling season, it is determined whether electric usage is too high. If so, then processing continues to block 1850 at which the shade value for the current month is decreased by a predetermined or configurable value (e.g., 0.1); otherwise, processing branches to block 1845 at which the shade value for the current month is increased by a predetermined or configurable value (e.g., 0.1).

[0184] At decision block 1860, it is determined whether the current month being processed is a heating month. If so, them processing continues with decision block 1870; otherwise, processing loops back to decision block 1830 until a predetermined or configurable number of iterations (e.g., 5) through each month has been performed at which point processing is complete.

[0185] At decision block 1870, during heating season, it is determined whether gas usage is too high. If so, then processing continues to block 1880 at which the shade value for the current month is increased by a predetermined or configurable value (e.g., 0.1); otherwise, processing branches to block 1885 at which the shade value for the current month is decreased by a predetermined or configurable value (e.g., 0.1).

[0186] FIG. 19 is a flowchart illustrating thermostat set point adjustment processing in accordance with an embodiment of the present invention. In one embodiment, the thermostat set point adjustment processing described with reference to FIG. 19 represents exemplary processing that may be associated with block 1640 of FIG. 16. In general, the thermostat set point adjustment processing starts with the cooling set point and adjusts it downward by a predetermined or configurable degree value (e.g., 1 degree). A simulation is run with the new set point, and if the score improves, a next lower set point is tried. If the first step down does not result in a better score then a set point one predetermined or configurable degree value (e.g., 1 degree) higher than initial is tried Once the optimal set point is located, a final simulation is run using that setting. This process is then repeated for the heating set point

[0187] FIG. 20 is an ECM selection screen in accordance with an embodiment of the present invention. According to the present example, ECM selection screen identifies a number of investment opportunities and thermostat settings. For each investment opportunity, a baseline description and an ECM selection selected by the end user, if any, are presented. Similarly, for each thermostat setting, a baseline description and an ECM selection selected by the end user, if any, are presented. Once the end user has made desired ECM selections in the ECM selection column, selecting the continue button will initiate ECM evaluation processing. As described above, in one embodiment, each ECM selection is simulated individually and the individual results are stored. After the simulation results are available, the end user may be presented with an ECM results screen, an example of which is depicted in FIG.21.

[0188] FIG. 21 is an ECM results screen 2100 in accordance with an embodiment of the present invention. In the present example, ECM results screen 2100 includes an ECM results area 2110, which includes for each of multiple investment opportunities a baseline description 2111, an ECM description 2112, an indication of electricity savings 2113, an indication of gas savings 2114, information regarding initial cost 21 IS and information regarding life cycle costs 2116.

[0189] The baseline description 2111 describes the current state of the particular building parameter. The ECM description 2112 describes the selected investment opportunity in relation to the particular building parameter. The indication of electricity savings 2113 provides information regarding an increase or decrease, if any, in kWh/yr. as a result of implementing the corresponding investment opportunity as indicated by simulation processing. Similarly, the indication of gas savings 2114 provides information regarding an increase or decrease, if any, in Therms/yr. as a result of implementing the corresponding investment opportunity as indicated by simulation processing. The information regarding initial costs 21 IS provides the end user with an estimate of initial costs to implement the corresponding investment opportunity. The information regarding life cycle costs 2116 provides the end user with an estimate of life cycle costs over a predetermined period of time (e.g., 30 years).

[0190] As noted earlier, in one embodiment, the savings associated with the investment opportunities is initially simulated and displayed individually. As such, in the present example, ECM results screen 2100 gives the end user the ability to evaluate two or more selected investment opportunities 2117a- d as a package. According to one embodiment, after the user has indicated his/her desire to evaluate a package of two or more ECMs together, the selected package of ECMs is simulated to calculate costs and energy savings, including interactive effects, and the results may be presented via the package row of the ECM results area 2110.

Scoring

[0191] The method for scoring a population member depends on in which mode the system is run. According to one embodiment, for calibration mode, scoring is based on how closely the simulated energy use matches the entered utility data. An exemplary equation that may be used for identifying the "closeness" of a simulation is given in Equation #5 and is referred to as the Average Percent Error (APE) of the simulation.

Where: E_pd,util = Period of Utility Electric Consumption E_pd,sim = Period of Simulated Electric Consumption G_pd,util = Period of Utility Natural Gas Consumption G_pd,sim = Period of Simulated Natural Gas Consumption

[0192] Periods for the computation include each month as well as the total annual values. Variances between simulated and utility period values may converted to percentages which also has the effect of weighting variances in electricity and natural gas consumption equally.

[0193] According to one embodiment, when the system is run in minimum cost mode, the score is calculated as the 30-year life cycle cost of the population member. Life cycle cost accounts for the financial benefits of energy use reduction as well as the investments in upgrades. This calculation of life cycle cost (IXC) may be based on the following exemplary equation given by Equation #6.

Where: d = Discount rate

k = Each year N = Total years

CF_k = Cash flow in a given year

SPPW d,k) = Single payment present worth of discount rate d at year k

[0194] Regardless of run mode, the population member with the lowest score is deemed to be the best option.

Calculation Methodology [0195] Many parameters are calculated in connection with preparing inputs for any simulation model. According to one embodiment, costs for all applicable parameters described herein are based on RS Means or equivalent

[0196] In one embodiment, geometric calculations employed may be based on those described in an earlier paper investigating the effect of building shape on energy consumption data, D. Tuhus-Dubrow and M. Krarti, "Genetic-Algorithm Based Approach to Optimize Building Envelope Design for Residential Buildings," Building and Environment, Vol. 44, pp. 1574-1482, (2010).

[0197] Calculations for other parameters can be made in accordance with the calculations described in "Comparative Analysis of Optimization Approaches to Design Building Envelope for Residential Buildings," D. Tuhus-Stewart and M. Krarti, ASHRAE Transactions, Vol. 115, Part 2, pp. 554-561, (2009), which is based on the method in the Building America Benchmark. Calculation of energy consumption of individual pieces of equipment not covered in the Building America Benchmark may be performed based on or extrapolated from information from the US Department of Energy energysavers.gov website and typical rated energy consumption.

[0198] In addition to automated calibration capabilities, the tool described herein may also be configured to determine the best options to minimize energy use, energy cost and/or the carbon foot print of the building at issue through a set of optimization techniques. For example, it is a simple matter to convert energy savings into carbon emission savings based on the utility and energy mix. In one embodiment, the method further involves calculating or estimating carbon emission savings associated with implementation of the one or more ECMs for the building at issue.

[0199] While embodiments of the invention have been illustrated and described, it will be clear that the invention is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the invention, as described in the claims.

Claims

CLAIMS What is claimed is:

1. A computer-implemented method comprising:

calibrating, by one or more modules running on one or more computer systems, a building energy model for a building at issue by simulating energy usage for the building at issue and comparing simulated results to actual utility data for the building at issue; and

providing feedback, by the one or more modules, regarding one or more energy conservation measures (ECMs) by determining a result of implementing the one or more ECMs for the building at issue by simulating energy usage for the building at issue based on the calibrated building energy model and the one or more ECMs.

2. The method of claim 1 , further comprising calculating or estimating carbon emission savings associated with implementation of the one or more ECMs for the building at issue.

3. The method of claim 1, wherein said calibrating comprises performing a plurality of simulations and determining based thereon selections for one or more unknown building parameters of a plurality of building parameters for the building at issue by identifying those from a plurality of possible selections that result in energy usage most closely resembling that of the actual utility data.

4. The method of claim 3, wherein said calibrating involves seeking an optimal solution from a large population of possible combinations of selections by performing a search optimization method.

5. The method of claim 3, wherein the plurality of building parameters include one or more physical parameters.

6. The method of claim 5, wherein the plurality of building parameters include one or more operational parameters.

7. The method of claim 4, wherein the search optimization method comprises a Bayesian optimization algorithm or a genetic algorithm.

8. A computer-implemen ted method comprising:

receiving, by one or more modules running on one or more computer systems, a plurality of physical and operational parameters for a building at issue;

creating, by the one or more modules, a building energy model for the building at issue by simulating energy usage for me building at issue;

calibrating the building energy module, by the one or more modules, by comparing simulated results produced by said simulating to actual utility data for the building at issue; and

based on the calibrated building energy module, performing, by the one or more routines, one or more of (i) an energy audit, (ii) a simulation of a particular scenario involving changes to one or more of the plurality of physical and operational parameters and (iii) an energy conservation measure (ECM) analysis for the building at issue.

9. The method of claim 8, wherein the energy audit comprises identification of potential areas for improvement of energy efficiency of the building at issue.

10. The method of claim 8, wherein the ECM analysis comprises automated evaluation of a proposed set of one or more ECMs.

11. The method of claim 8, wherein the ECM analysis comprises automated identification of an optimal set of one or more ECMs for a given budget.

12. The method of claim 8, wherein the ECM analysis is performed by a utility service provider on behalf of a customer of the utility service provider to identify one or more potential ECMs that may save the customer money on one or more of their natural gas, water and electricity bills.

13. The method of claim 8, further comprising calculating or estimating carbon emission savings associated with implementation of one or more ECMs for the building at issue.

14. The method of claim 8, wherein said calibrating comprises performing a plurality of simulations and deterrnining based thereon selections for one or more unknown building physical and operational parameters for the building at issue by identifying those from a plurality of possible selections that result in energy usage most closely resembling that of the actual utility data.

15. The method of claim 14, wherein said calibrating involves seeking an optimal solution from a large population of possible combinations of selections by performing a search optimization method.

16. The method of claim 15, wherein the search optimization method comprises a Bayesian optimization algorithm or a genetic algorithm.

17. A non-transitory computer-readable storage medium tangibly embodying a set of instructions, which when executed by one or more processors of one or more computer systems, cause the one or more processors to perform a method comprising:

calibrating a building energy model for a building at issue by simulating energy usage for the building at issue and comparing simulated results to actual utility data for the building at issue; and

providing feedback regarding one or more energy conservation measures (ECMs) by detennining a result of implementing the one or more ECMs for the building at issue by simulating energy usage for the building at issue based on the calibrated building energy model and the one or more ECMs.

18. The computer-readable storage medium of claim 17, wherein the method further comprises calculating or estimating carbon emission savings associated with implementation of the one or more ECMs for the building at issue.

19. The computer-readable storage medium of claim 17, wherein said calibrating comprises performing a plurality of simulations and determining based thereon selections for one or more unknown building parameters of a plurality of building parameters for the building at issue by identifying those from a plurality of possible selections that result in energy usage most closely resembling that of the actual utility data.

20. The computer-readable storage medium of claim 19, wherein said calibrating involves seeking an optimal solution from a large population of possible combinations of selections by performing a Bayesian optimization algorithm or a genetic algorithm.