The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Neural networks for short-term load forecasting: a review and evaluation
Abstract
Load forecasting has become one of the major areas of research in electrical engineering, and most traditional forecasting models and artificial intelligence techniques have been tried out in this task. Artificial neural networks (NNs) have lately received much attention, and a great number of papers have reported successful experiments and practical tests with them. Nevertheless, some authors remain skeptical, and believe that the advantages of using NNs in forecasting have not been systematically proved yet. In order to investigate the reasons for such skepticism, this review examines a collection of papers (published between 1991 and 1999) that report the application of NNs to short-term load forecasting. Our aim is to help to clarify the issue, by critically evaluating the ways in which the NNs proposed in these papers were designed and tested.
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Cites
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Cited by
Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. (2018) · Energies · doi:10.3390/en11071636 · Cited by 915
Electric load forecasting methods: Tools for decision making
Hahn, H., Meyer-Nieberg, S., & Pickl, S. (2009) · European Journal of Operational Research · doi:10.1016/j.ejor.2009.01.062 · Cited by 549
Neural network load forecasting with weather ensemble predictions
Taylor, J. W., & Buizza, R. (2002) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2002.800906 · Cited by 459
Short-term load forecasting using lifting scheme and ARIMA models
Lee, C.-M., & Ko, C.-N. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.11.033 · Cited by 438
Forecasting and operational research: a review
Fildes, R., Nikolopoulos, K., Crone, S. F., & Syntetos, A. A. (2008) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2602597 · Cited by 287
Metadata
- Authors
- Hippert, H. S., Pedreira, C. E., & Souza, R. C.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2001
- Volume
- 16
- Issue
- 1
- Pages
- 44-55
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2151767444
- Cited by
- 2,221
- References
- 1
- FWCI
- 25.93
- Type
- journal article
- Language
- en
- Open access
- closed
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- Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001). Neural networks for short-term load forecasting: a review and evaluation. IEEE Transactions on Power Systems. https://doi.org/10.1109/59.910780
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