A hybrid model approach for forecasting future residential electricity consumption
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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 [195]
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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
Multimodal data fusion for systems improvement: A review
Gaw, N., Yousefi, S., & Gahrooei, M. R. (2022) · IISE Transactions · doi:10.1080/24725854.2021.1987593 · Cited by 101 · see citing works
- Authors
- Dong, B., Li, Z., Rahman, S. M. M., & Vega, R.
- Affiliations
- The University of Texas at San Antonio (US)
- Corresponding author
- Dong, Bing
- Venue
- Energy and Buildings
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 117
- Pages
- 341-351
- ISSN
- 0378-7788 (ISSN-L)
- OpenAlex
- W1619614355
- Cited by
- 229
- References
- 48
- Citations by year
- 2026: 15 · 2025: 34 · 2024: 20 · 2023: 27 · 2022: 34 · 2021: 26 · 2020: 22 · 2019: 18 · 2018: 16 · 2017: 14 · 2016: 3
- FWCI
- 5.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Energy and Buildings (publishedVersion)
- Funders
- CPS Energy of San Antonio, Texas
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Keywords
- hybrid forecasting model, physics-based modeling, thermal network, smart meter data, internal heat gains, day-ahead load forecasting, least squares support vector machine, Gaussian process regression, Gaussian mixture model, support vector regression, artificial neural networks, single-family house
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