Cited by 229Energy Load and Power ForecastingBuilding Energy and Comfort OptimizationSmart Grid Energy Management

A hybrid model approach for forecasting future residential electricity consumption

Dong, Bing, Li, Zhaoxuan, Rahman, S.M. Mahbobur, Vega, Rolando·2016·Energy and Buildings

Integrity signals

Registry

Crossref · 10.1016/j.enbuild.2015.09.033

Cites

48 of 48 references verified0 citation errors · 0 not checkable

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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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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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