Preprint Review Version 1 This version is not peer-reviewed

Computational Intelligence Load Forecasting: A Methodological Overview

Version 1 : Received: 17 December 2018 / Approved: 18 December 2018 / Online: 18 December 2018 (10:38:10 CET)

A peer-reviewed article of this Preprint also exists.

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

Journal reference: Energies 2019, 12, 393
DOI: 10.3390/en12030393

Abstract

Electricity demand forecasting has been a real challenge for power system scheduling in the different levels of the energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for load forecasting; although, scant evidence is available about the feasibility of each of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationale behind intelligent forecasting methods, based on the work of previous researchers in the field of energy. The fundamental benefits and main drawbacks of the aforementioned methods are discussed in order to depict the efficiency of each approach in various situations. In the end, a proposed hybrid strategy is represented.

Subject Areas

Intelligent Load Forecasting; Demand-Side Management; Pattern Similarity; Hierarchical Forecasting; Feature Selection; Weather Station Selection

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