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In general, U.S. municipal and state benchmarking and disclosure programs have proven effective in encouraging the development of a strong market for building energy efficiency. Available data for select cities in the United States shows that energy savings per unit of floor space for these programs range between 6% and 8% over a two-year period (Pan et al. 2016, 10). Despite the merit of these programs, however, several shortcomings have been identified, including the need for: (1) more efficient and cost-effective assessment of buildings for retrofit opportunities, and (2) greater standardization and automation of the benchmarking and disclosure processes. To address these shortcomings, Lawrence Berkeley National Laboratory, Johnson Controls, and ICF International are developing a free, on-line, open-source, building energy efficiency upgrade targeting tool. The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Inverse Modeling Toolkit (IMT), and Johnson Controls’ LEAN Energy Analysis, serve as the technical basis for the tool, which will automatically regress monthly energy usage versus ambient temperature; compare model coefficients to quantify energy and cost-savings potential; and analyze model coefficients to identify energy conservation measures (operational or equipment) for a single building or portfolio. The tool is unique in its open-source, modular, online, and 100% automated format. The source code will be published on GitHub and thus available for use and modification by the buildings community. This paper discusses: (1) the market’s need for the tool; (2) the tool’s analytic methodology, based on a combination of ASHRAE’s IMT and LEAN Energy Analysis; (3) tool innovations with industry impact, including a fully automated approach for building change-point selection; (4) and the outcomes of early pilot applications of the tool among 36 hotel buildings. Future work and potential applications are also discussed.
Citation: 2019 Winter Conference, Atlanta, GA, Conference Papers
Product Details
- Published:
- 2019
- Number of Pages:
- 9
- Units of Measure:
- Dual
- File Size:
- 1 file , 2 MB
- Product Code(s):
- D-AT-19-C056
