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Short-Term Fuzzy Load Forecasting Model Using Genetic–Fuzzy and Ant Colony–Fuzzy Knowledge Base Optimization

1
Department of Electric and Electronics Engineering, Kırıkkale University, 71451 Kırıkkale, Turkey
2
Department of Informatics, Kırıkkale University, 71451 Kırıkkale, Turkey
3
Department of Computer Engineering, Gazi University, Technology Faculty, 06500 Ankara, Turkey
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(6), 864; https://doi.org/10.3390/app8060864
Received: 15 April 2018 / Revised: 19 May 2018 / Accepted: 20 May 2018 / Published: 25 May 2018
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Abstract

The estimation of hourly electricity load consumption is highly important for planning short-term supply–demand equilibrium in sources and facilities. Studies of short-term load forecasting in the literature are categorized into two groups: classical conventional and artificial intelligence-based methods. Artificial intelligence-based models, especially when using fuzzy logic techniques, have more accurate load estimations when datasets include high uncertainty. However, as the knowledge base—which is defined by expert insights and decisions—gets larger, the load forecasting performance decreases. This study handles the problem that is caused by the growing knowledge base, and improves the load forecasting performance of fuzzy models through nature-inspired methods. The proposed models have been optimized by using ant colony optimization and genetic algorithm (GA) techniques. The training and testing processes of the proposed systems were performed on historical hourly load consumption and temperature data collected between 2011 and 2014. The results show that the proposed models can sufficiently improve the performance of hourly short-term load forecasting. The mean absolute percentage error (MAPE) of the monthly minimum in the forecasting model, in terms of the forecasting accuracy, is 3.9% (February 2014). The results show that the proposed methods make it possible to work with large-scale rule bases in a more flexible estimation environment. View Full-Text
Keywords: short-term load forecasting; fuzzy logic; genetic algorithm; artificial intelligence; ant colony optimization short-term load forecasting; fuzzy logic; genetic algorithm; artificial intelligence; ant colony optimization
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Luy, M.; Ates, V.; Barisci, N.; Polat, H.; Cam, E. Short-Term Fuzzy Load Forecasting Model Using Genetic–Fuzzy and Ant Colony–Fuzzy Knowledge Base Optimization. Appl. Sci. 2018, 8, 864.

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