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Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system
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“however, ML approaches such as deep belief networks can potentially increase accuracy with less computational expense [4,255] (see also Section 5.3).”
From Tackling climate change with machine learning (2023) · cites this paper as [255]
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Metadata
- Authors
- Fu, G.
- Affiliations
- South China University of Technology (CN)
- Corresponding author
- Fu, Guoyin
- ORCID
- Fu, Guoyin
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 148
- Pages
- 269-282
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W2792344217
- Cited by
- 181
- References
- 46
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 27 · 2023: 18 · 2022: 31 · 2021: 22 · 2020: 22 · 2019: 22 · 2018: 8
- FWCI
- 14.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- 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 › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Keywords
- cooling load forecasting, air conditioning, deep belief network, empirical mode decomposition, ensemble forecasting, building energy consumption, HVAC control, deterministic forecasting, model uncertainty, noisy data
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