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Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems
Abstract
In this paper, a hybrid model for short-term load forecast that integrates artificial neural networks and fuzzy expert systems is presented. The forecasted load is obtained by passing through two steps. In the first procedure, the artificial neural networks are trained with the load patterns corresponding to the forecasting hour, and the provisional forecasted load is obtained by the trained artificial neural networks. In the second procedure, the fuzzy expert systems modify the provisional forecasted load considering the possibility of load variation due to changes in temperature and the load behavior of holiday. In the test case of 1994 for implementation in the short term load forecasting expert system of Korea Electric Power Corporation (KEPCO), the proposed hybrid model provided good forecasting accuracy of the mean absolute percentage errors below 1.3%. The comparison results with exponential smoothing method showed the efficiency and accuracy of the hybrid model.>
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McClelland, J. L., Rumelhart, D. E., & AU (1987) · doi:10.7551/mitpress/5237.001.0001 · Cited by 3,315
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Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach
Weron, R. (2006) · doi:10.1002/9781118673362 · Cited by 708
Zheng, H., Yuan, J., & Chen, L. (2017) · Energies · doi:10.3390/en10081168 · Cited by 635
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
A hybrid method based on wavelet, ANN and ARIMA model for short-term load forecasting
Fard, A. K., & Akbari-Zadeh, M.-R. (2014) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2013.813976 · Cited by 134
Metadata
- Authors
- Kim, K.-H., Park, J.-K., Hwang, K.-J., & Kim, S.-H.
- Venue
- IEEE Transactions on Power Systems
- Published
- 1995
- Volume
- 10
- Issue
- 3
- Pages
- 1534-1539
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2101393539
- Cited by
- 213
- References
- 1
- FWCI
- 3.83
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kim, K.-H., Park, J.-K., Hwang, K.-J., & Kim, S.-H. (1995). Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems. IEEE Transactions on Power Systems. https://doi.org/10.1109/59.466492
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