Foresee: A user-centric home energy management system for energy efficiency and demand response
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Automated demand-side response can respond to electricity prices, smart meter signals, or learned user preferences.
“For this, automated demand-side response [357] can respond to electricity prices, smart meter signals, or learned user preferences [393].”
From Tackling climate change with machine learning (2023) · cites this paper as [393]
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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
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- Authors
- Jin, X., Baker, K., Christensen, D., & Isley, S.
- Affiliations
- National Laboratory of the Rockies (US)
- Corresponding author
- Jin, Xin
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 205
- Pages
- 1583-1595
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2748923504
- Cited by
- 204
- References
- 28
- Citations by year
- 2026: 8 · 2025: 22 · 2024: 22 · 2023: 28 · 2022: 27 · 2021: 33 · 2020: 26 · 2019: 23 · 2018: 14 · 2017: 1
- FWCI
- 7.28
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Applied Energy (acceptedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- U.S. Department of Energy (DE-AC36-08GO28308)
- Bonneville Power Administration under Technology Innovation (TIP-337)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
- Keywords
- home energy management systems, demand response, energy efficiency, user preferences, thermal comfort, appliance usage patterns, load forecasting, photovoltaic systems, battery energy storage, CO2 emissions, utility cost reduction, residential buildings
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