The disclosure relates generally to a system and method for determining building components.
The building of energy efficient buildings (known as green building) has become a very popular task. The demand for building of energy efficient buildings has accelerated recently due to various factors including widespread regulations, tax and cash incentives, availability of cost-effective energy-efficient solutions, expected energy cost growth, an overall desire to be more environmentally responsible and/or energy related comfort that is important to people with low price sensitivity.
Meeting environmental construction goals (for example—reducing home energy consumption by 25%) requires finding an optimal combination of house shell components like windows, walls, roofs, insulation and mechanical equipment. There are millions of possible ways to design and build each house, and each can greatly affect cost, energy consumption and comfort. Unfortunately, architects and builders are not aware of all these combinations and don't have the tools and skills to find the best one. Thus, their selection is based on past experience and preference and usually yields suboptimal results. In most cases, homeowners can achieve better energy results for their investment or reach their energy-related goals for a much lower cost.
Systems and methods exist in which a user can try to identify the best building materials for green building. The existing solutions to try to build energy efficient buildings are too expensive and give only partial support. The existing solutions may include an architect's experience, an architect hiring an energy analysis using energy analysis software, an architect using third party energy analysis and/or a homeowner using an on-line retrofit analysis software. Each of those existing solutions, the cost can be as much as $50,000 and has many limitations. For example, none of these tools offers quick design data capture, automatic optimization capabilities, full cost/benefit analysis, early design optimization (such as, house orientation and shape) or easy visualization, and they all have a very steep learning curve. Thus, architects and builders usually use a combination of in-house developed spreadsheets and gut feelings to identify and suggest a possible design to their clients and then hire an expert to validate their findings. This process is time consuming and does not provide the optimization analysis for finding best designs. The existing solutions also usually cannot answer the questions:
Thus, it is desirable to provide a green building system and method that overcomes the above limitations of the existing solutions and it is to this end that the disclosure is directed.
The disclosure is particularly applicable to a client/server based building system design, construction and maintenance and method and it is in this context that the disclosure will be described. It will be appreciated, however, that the system and method has greater utility, such as to other architectures of a building system and method and to other implementations of the building system design, construction and maintenance and method.
The system above is a software as a service (SaaS) solution since there is no installation on the client side and that upgrades are handled by the green building unit 106. This allows the system to make easy updates, for example in case we learn that a cost of a window changes. It also allows us to do statistics on our data. For example—In a specific project, the homeowner is charged X for a sqft of wall. Using the system, she can check whether this is the normal price for that type of wall using the summarized analysis of the data in the database. There are other ways to implement the system that may include: 1) a full/partial installation on the client side to give full control of data; 2) a semi manual process—where the optimization is given as a service. The user sends the inputs and someone else running the system is doing the analysis; and 3) a full manual process—User sends one house design and gets back the utility value for that design. If it does not pass the threshold—the user updates the design and send the updated design for evaluation. The green building system may also be implemented with a piece of software downloaded to each client computer (or delivered to each client computer on a computer readable medium), in a client/server system and in a cloud system in which the one or more server computers are cloud resources.
(a) External data sources:
(b) Internal data (most data is obtained from the homeowner):
The decision engine 106b establishes a utility function per client which is a combination of desires, financials, environmental awareness and code requirements, calculates all possible design permutations for the house based on a set of design components defined by the client (for example—4 types of potential windows, 5 types of potential walls . . . ); and/or finds the designs that best comply with the utility function.
An Architect/builder 120a, 120b uses the analysis from the decision engine 106b to compare and choose a design for the house (windows, walls, roof etc.), communicate the different design options as well as their utility (cost, benefit) and tradeoff to the home owner 120d (called client on the diagram), provide the needed “proof” to inspector 120e (for getting building, occupancy permit in case proof of environmental analysis is needed), and incentive providers 120f and compare design tradeoffs during construction (for example if a certain insulation is not available).
The system may have an input for the parts provider 120g who can enter information about new components available (for example new type of window) into the system. This will allow homeowners (clients) wider variety to choose from and will increase exposure for the parts provider. Future buyers 120c get information about energy consumption of a house (e.g., energy report) they are considering buying and in return willing to pay more for the house. Mortgage providers get information about energy consumption of a future house and, in return, they give a better mortgage terms (fewer risk of default due to smaller utility bills).
Another input to the decision engine 106b may be other related information 152 which are other inputs needed for running the analysis that may include: building component cost data; Weather and climate data to project the heating/cooling needs at the house location; Building material; Building code data; Government & utility incentives and tax breaks (some are location based); and Utility payment (cost) which can be location based.
The inputs may also include a list of potential components 154 which includes user input of possible selection of enclosure/wall components (see
The decision engine may also receive constraints & Incentives 156 which are a list of filters and financial inputs. This list might be location, house size and geometry or time based. For example—a certain building code mandated in a certain town or the potential to get a tax break if meeting a certain energy standard. An example of the user interface for this feature is shown in
The decision engine may also receive client's preferences 158 and these can contain filters (for example: I am only interested in window X out of all the possible options) and/or utility function defined by the homeowner. The preferences may also include components already selected by the user, financial constraints and desired payback.
The decision engine may include the processes of: data entry regarding the house geometry, climate and energy related usage; possible option input by user; user defines a utility function; and the system presents the best design. In the first data entry process, the data entry regarding the house geometry, climate and energy related usage is performed. The architect/builder/homeowner can enter the entire data herself or ask the system to “fill-in” the gaps using a smart algorithm that can, for example, fill in the climate info based on ZIP code or “guess” the house shape. The system uses that to promote an “onion” approach where the use can start using the system very early, entering few inputs and add more inputs throughout the design process to replace the automatic algorithm and produce better analysis.
During the possible options definition process, the user adds information regarding possible options for the different components (walls, windows, heating equipment, air conditioners, ceiling insulation, floor insulation, basement wall insulation, lighting scheme, photovoltaic (PV), etc.). During the utility function definition, the user defines a utility function. For example—finding the cheapest design that meets a LEED score of X. The utility function can be one goal, a set of weighted goals that include cost, desired payback, environmental goals, convenience etc. (For example, a utility function can be defined as a sum of 20% upfront cost reduction, 30% payback period reduction, 50% CO2 emission reduction) or a combination of must meet and weighted nice to have goals. An example of a must meet goal—mandatory environmental code in a certain location.
The engine 106b may have an optimized output portion 160 that generates a list of the best components (enclosure, lighting, etc.) for a specific project based on the various input data. The engine 106b may also have a building performance information portion 162 that generates information about code compliance and incentive compliance for the specific design solution. The engine 106b also has a reporting unit 164 that generates various reports for different users of the system based on the inputs and processes.
Based on the above processes, the system finds and presents to the user the best design for the defined utility function (if the user is looking for one design) or a set of designs that meet criteria (if the user is interested in comparing several options). The process creates all possible design combinations that include all of the combinations of the components defined by the user above. The system also calculates the utility function for each design in which the utility function can be a combination of cost, projected energy consumption, payback period, code compliance etc. The system organizes the solutions according to their utility function score and filters out the design that do not meet the user thresholds (in case filters were defined). The system presents the ordered list to the user. Note: For easy understanding and alternative comparison, the system offers a translation of the results to a more easy to understand metrics that will allow the user to grasp the alternatives. For example—tons CO2 are translated into # of planted trees or converting regular cars to hybrid cars needed to offset the building environmental impact.
While the foregoing has been with reference to a particular embodiment of the invention, it will be appreciated by those skilled in the art that changes in this embodiment may be made without departing from the principles and spirit of the disclosure, the scope of which is defined by the appended claims.
This application is a continuation of U.S. patent application Ser. No. 13/678,456, filed on Nov. 15, 2012, which claims the benefits under 35 U.S.C. 119(e) and 120 to U.S. Provisional Patent Application Ser. No. 61/560,284 filed on Nov. 15, 2011 and entitled “Green Building System and Method”, the entirety of which is incorporated herein by reference.
Number | Date | Country | |
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61560284 | Nov 2011 | US |
Number | Date | Country | |
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Parent | 13678456 | Nov 2012 | US |
Child | 17856047 | US |