Building Energy Use Prediction and System Identification Using Recurrent Neural Networks
Abstract
Following several successful applications of feedforward neural networks (NNs) to the building energy prediction problem (Wang and Kreider, 1992; JCEM, 1992, 1993; Curtiss et al., 1993, 1994; Anstett and Kreider, 1993; Kreider and Haberl, 1994) a more difficult problem has been addressed recently: namely, the prediction of building energy consumption well into the future without knowledge of immediately past energy consumption. This paper will report results on a recent study of six months of hourly data recorded at the Zachry Engineering Center (ZEC) in College Station, TX. Also reported are results on finding the R and C values for buildings from networks trained on building data.
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ML has the potential to greatly speed up building energy demand computations by ignoring physical knowledge, incorporating it, or learning surrogate models.
“ML has the potential to speed up these computations greatly, either by ignoring physical knowledge of the building entirely [452,617], by incorporating it into the computation [195], or by learning to approximate the physical model to reduce the need for expensive simulation (surrogate models) [274].”
From Tackling climate change with machine learning (2023) · cites this paper as [452]
Cites
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Kreider, J. F., Claridge, D. E., Curtiss, P., Dodier, R., Haberl, J. S., & Krarti, M.
- Affiliations
- University of Colorado Boulder (US)
- Texas A&M University (US)
- Venue
- Journal of Solar Energy Engineering
- Publisher
- ASME International
- Published
- 1995-08-01
- Volume
- 117
- Issue
- 3
- Pages
- 161-166
- ISSN
- 0199-6231 (ISSN-L)
- 1528-8986
- OpenAlex
- W2033435193
- Cited by
- 97
- References
- 6
- Citations by year
- 2024: 3 · 2023: 5 · 2022: 5 · 2021: 3 · 2020: 6 · 2019: 5 · 2018: 2 · 2017: 22 · 2016: 3 · 2015: 3 · 2014: 3 · 2013: 6 · 2012: 4
- FWCI
- 10.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Solar Energy Engineering (publishedVersion)
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- building energy consumption prediction, recurrent neural networks, system identification, long-term energy forecasting, building thermal properties, hourly energy data
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