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Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview

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Independent Researcher, Sari 4816783787, Iran
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Department of Electrical Engineering, Sharif University of Technology, Tehran P.O. Box 11365-11155, Iran
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Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China
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Author to whom correspondence should be addressed.
Energies 2019, 12(3), 393; https://doi.org/10.3390/en12030393
Received: 17 December 2018 / Revised: 24 January 2019 / Accepted: 25 January 2019 / Published: 27 January 2019
(This article belongs to the Special Issue Short-Term Load Forecasting 2019)
Electricity demand forecasting has been a real challenge for power system scheduling in different levels of energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for short-term load forecasting, although scant evidence is available about the feasibility of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationales behind short-term load forecasting methodologies based on works of previous researchers in the energy field. Fundamental benefits and drawbacks of these methods are discussed to represent the efficiency of each approach in various circumstances. Finally, a hybrid strategy is proposed. View Full-Text
Keywords: short-term load forecasting; demand-side management; pattern similarity; hierarchical short-term load forecasting; feature selection; weather station selection short-term load forecasting; demand-side management; pattern similarity; hierarchical short-term load forecasting; feature selection; weather station selection
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MDPI and ACS Style

Fallah, S.N.; Ganjkhani, M.; Shamshirband, S.; Chau, K.-W. Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview. Energies 2019, 12, 393.

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