Streamlining building energy efficiency assessment through integration of uncertainty analysis and full scale energy simulations
Asset Score is United States’ national standardized rating system and tool to assess a building's energy-related systems. The tool models building energy use under standard operating conditions to enable fair comparisons of buildings. With basic building characteristics entered by users, the tool creates simplified EnergyPlus models. However, even with a reduced set of model inputs, data collection still remains a challenge for widespread adoption of this rating system. The commercial building market demands an even more simplified entry point to energy efficiency evaluation. This paper discusses a hybrid method that combines regression models with real-time simulations to allow users to enter as few as seven building characteristics to quickly assess the building performance before a full-scale analysis. Built upon large-scale building stock simulations, a Random Forest approach was used to develop a set of regression models for various building use types. The majority of the Asset Score inputs were sampled extensively and fed into regression models. Based on the minimum user inputs, the Asset Score tool infers the remaining building characteristics and queries a large set of energy use intensity (EUI) values to create a distribution of possible EUIs for the building using the regression analysis. The regression model also takes a user's confidence level into account by allowing user to modify or verify the defaults, if known. With additional user inputs, a regression model can be transferred to an energy model for a full-scale energy simulation. This streamlined assessment provides an easy entry point to Asset Score. It also enables users who manage a large number of buildings to screen and prioritize buildings that can benefit most from a more detailed evaluation and possible energy efficiency upgrades without intensive data collection.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1489190
- Report Number(s):
- PNNL-SA-129451
- Journal Information:
- Energy and Buildings, Vol. 176, Issue C; ISSN 0378-7788
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
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