The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review
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Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Swarm Intelligence: From Natural to Artificial Systems
Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999) · Oxford University Press eBooks · doi:10.1093/oso/9780195131581.001.0001 · Cited by 6,435
AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION
Karaboğa, D. (2005) · Cited by 5,466
Time series forecasting using a hybrid ARIMA and neural network model
Zhang, G. P. (2003) · Neurocomputing · doi:10.1016/s0925-2312(01)00702-0 · Cited by 4,428
A review on the prediction of building energy consumption
Zhao, H.-X., & Magoulès, F. (2012) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.02.049 · Cited by 1,937
Electric load forecasting using an artificial neural network
Park, D.-C., El-Sharkawi, M. A., Marks, R. J., Atlas, L., & Damborg, M. J. (1991) · IEEE Transactions on Power Systems · doi:10.1109/59.76685 · Cited by 1,440
Swarm Intelligence: From Natural to Artificial Systems
Webb, B. (2002) · Connection Science · doi:10.1080/09540090210144948 · Cited by 1,201
Raza, M. Q., & Khosravi, A. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.04.065 · Cited by 1,021
A review on applications of ANN and SVM for building electrical energy consumption forecasting
Ahmad, A. S., Hassan, M. Y., Abdullah, M. P., Rahman, H. A., Hussin, F., Abdullah, H., & Saidur, R. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.01.069 · Cited by 954
Self-citationState of the art in building modelling and energy performances prediction: A review
Foucquier, A., Robert, S., Suard, F., Stéphan, L., & Jay, A. (2013) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.03.004 · Cited by 832
Cited by
State of the Art of Machine Learning Models in Energy Systems, a Systematic Review
Mosavi, A., Salimi, M., Faizollahzadeh Ardabili, S., Rabczuk, T., Shamshirband, S., & Varkonyi-Koczy, A. R. (2019) · Energies · doi:10.3390/en12071301 · Cited by 578
A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting
Sajjad, M., Khan, Z. A., Ullah, A., Hussain, T., Ullah, W., Lee, M. Y., & Baik, S. W. (2020) · IEEE Access · doi:10.1109/access.2020.3009537 · Cited by 528
A deep learning framework for building energy consumption forecast
Somu, N., Raman M R, G., & Ramamritham, K. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110591 · Cited by 500
O’Dwyer, E., Pan, I., Acha, S., & Shah, N. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.024 · Cited by 410
Zhao, Y., Zhang, C., Zhang, Y., Wang, Z., & Li, J. (2020) · Energy and Built Environment · doi:10.1016/j.enbenv.2019.11.003 · Cited by 303
Metadata
- Authors
- Mat Daut, M. A., Hassan, M. Y., Abdullah, H., Rahman, H. A., Abdullah, M. P., & Hussin, F.
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2017-04
- Volume
- 70
- Pages
- 1108-1118
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W2560599441
- Cited by
- 346
- References
- 65
- FWCI
- 23.44
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Mat Daut, M. A., Hassan, M. Y., Abdullah, H., Rahman, H. A., Abdullah, M. P., & Hussin, F. (2017). Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review. Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2016.12.015
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
