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

Seyedeh Narjes Fallah, Mehdi Ganjkhani, Shahaboddin Shamshirband and Kwok-wing Chau
Additional contact information
Seyedeh Narjes Fallah: Independent Researcher, Sari 4816783787, Iran
Mehdi Ganjkhani: Department of Electrical Engineering, Sharif University of Technology, Tehran P.O. Box 11365-11155, Iran
Shahaboddin Shamshirband: Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Kwok-wing Chau: Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China
Authors registered in the RePEc Author Service: Shahab S Band

Energies, 2019, vol. 12, issue 3, 1-21

Abstract: 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.

Keywords: short-term load forecasting; demand-side management; pattern similarity; hierarchical short-term load forecasting; feature selection; weather station selection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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