The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
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]
Citations
Cites
Heating and cooling energy trends and drivers in buildings
Ürge-Vorsatz, D., Cabeza, L. F., Serrano, S., Barreneche, C., & Petrichenko, K. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.08.039 · Cited by 1,101
Day-ahead wind speed forecasting using f-ARIMA models
Kavasseri, R., & Seetharaman, K. (2008) · Renewable Energy · doi:10.1016/j.renene.2008.09.006 · Cited by 960
A review of data-driven approaches for prediction and classification of building energy consumption
Wei, Y., Zhang, X., Shi, Y., Xia, L., Pan, S., Wu, J., Han, M., & Zhao, X. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.09.108 · Cited by 811
Deep learning based ensemble approach for probabilistic wind power forecasting
Wang, H., Li, G., Wang, G., Peng, J., Jiang, H. Q., & Liu, Y. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.11.111 · Cited by 754
A short-term building cooling load prediction method using deep learning algorithms
Fan, C., Xiao, F., & Zhao, Y. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.03.064 · Cited by 715
Analysis and evaluation of five short-term load forecasting techniques
Moghram, I. S., & Rahman, S. (1989) · IEEE Transactions on Power Systems · doi:10.1109/59.41700 · Cited by 709
Yildiz, B., Bilbao, J. I., & Sproul, A. B. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.02.023 · Cited by 563
Deep belief network based deterministic and probabilistic wind speed forecasting approach
Wang, H., Wang, G. B., Li, G. Q., Peng, J.-C., & Liu, Y. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.08.108 · Cited by 495
Applying support vector machine to predict hourly cooling load in the building
Li, Q., Meng, Q., Cai, J., Yoshino, H., & Mochida, A. (2009) · Applied Energy · doi:10.1016/j.apenergy.2008.11.035 · Cited by 485
Deep Neural Network Based Demand Side Short Term Load Forecasting
Ryu, S., Noh, J.-K., & Kim, H. (2016) · Energies · doi:10.3390/en10010003 · Cited by 414
Research works printed but not linked: 2
- Yao, 2004, Hourly cooling load prediction by a combined forecasting model based on Analytic Hierarchy Process, Int J Therm Sci, 43, 1107, 10.1016/j.ijthermalsci.2004.02.009 · DOI
- Deb, 2016, Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks, Energy Build, 121, 284, 10.1016/j.enbuild.2015.12.050 · DOI
Cited by
Sorted by citations
A review of deep learning for renewable energy forecasting
Wang, H., Lei, Z., Zhang, X., Zhou, B., & Peng, J. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.111799 · Cited by 1,211
State of the Art of Machine Learning Models in Energy Systems, a Systematic Review
Mosavi, A. H., Salimi, M., Ardabili, S., Rabczuk, T., Shamshirband, S., & Várkonyi-Kóczy, A. R. (2019) · Energies · doi:10.3390/en12071301 · Cited by 621
A hybrid deep learning model for short-term PV power forecasting
Li, P., Zhou, K., Lu, X., & Yang, S. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.114216 · Cited by 561
Benti, N. E., Chaka, M. D., & Semie, A. G. (2023) · Sustainability · doi:10.3390/su15097087 · Cited by 470
Building energy performance forecasting: A multiple linear regression approach
Ciulla, G., & D’Amico, A. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113500 · Cited by 383
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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