The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Electric load forecasting: Literature survey and classification of methods
Abstract
A review and categorization of electric load forecasting techniques is presented. A wide range of methodologies and models for forecasting are given in the literature. These techniques are classified here into nine categories: (1) multiple regression, (2) exponential smoothing, (3) iterative reweighted least-squares, (4) adaptive load forecasting, (5) stochastic time series, (6) ARMAX models based on genetic algorithms, (7) fuzzy logic, (8) neural networks and (9) expert systems. The methodology for each category is briefly described, the advantages and disadvantages discussed, and the pertinent literature reviewed. Conclusions and comments are made on future research directions.
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Citations
Cites
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
Gross, G., & Galiana, F. D. (1987) · Proceedings of the IEEE · doi:10.1109/proc.1987.13927 · Cited by 878
A regression-based approach to short-term system load forecasting
Papalexopoulos, A. D., & Hesterberg, T. C. (1990) · IEEE Transactions on Power Systems · doi:10.1109/59.99410 · Cited by 823
Analysis and evaluation of five short-term load forecasting techniques
Moghram, I., & Rahman, S. (1989) · IEEE Transactions on Power Systems · doi:10.1109/59.41700 · Cited by 706
IEEE Transactions on Power Systems
(2023) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.59 · Cited by 666
Short-term load forecasting using an artificial neural network
Lee, K. Y., Cha, Y. T., & Park, J. H. (1992) · IEEE Transactions on Power Systems · doi:10.1109/59.141695 · Cited by 602
Advancement in the application of neural networks for short-term load forecasting
Peng, T.-M., Hubele, N. F., & Karady, G. G. (1992) · IEEE Transactions on Power Systems · doi:10.1109/59.141711 · Cited by 382
Regression based peak load forecasting using a transformation technique
Haida, T., & Muto, S. (1994) · IEEE Transactions on Power Systems · doi:10.1109/59.331433 · Cited by 350
Nonparametric regression based short-term load forecasting
Charytoniuk, W., Chen, M. S., & Olinda, P. V. (1998) · IEEE Transactions on Power Systems · doi:10.1109/59.708572 · Cited by 301
Neural network based short term load forecasting
Lu, C.-N., Wu, H.-T., & Vemuri, S. (1993) · IEEE Transactions on Power Systems · doi:10.1109/59.221223 · Cited by 270
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Yildiz, B., Bilbao, J. I., & Sproul, A. B. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.02.023 · Cited by 560
On recent advances in PV output power forecast
Raza, M. Q., Nadarajah, M., & Ekanayake, C. (2016) · Solar Energy · doi:10.1016/j.solener.2016.06.073 · Cited by 544
Deep learning framework to forecast electricity demand
Bedi, J., & Toshniwal, D. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.113 · Cited by 481
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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
Metadata
- Authors
- Alfares, H. K., & Nazeeruddin, M.
- Venue
- International Journal of Systems Science
- Publisher
- Informa UK Limited
- Published
- 2002-01
- Volume
- 33
- Issue
- 1
- Pages
- 23-34
- ISSN
- 0020-7721 (ISSN-L)
- 1464-5319
- OpenAlex
- W2049751389
- Cited by
- 743
- References
- 33
- FWCI
- 1.40
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
