Computational intelligence on short-term load forecasting: A methodological overview

Seyedeh Narjes Fallah, Mehdi Ganjkhani, Shahaboddin Shamshirband, Kwok wing Chau

Research output: Journal article publicationReview articleAcademic researchpeer-review

94 Citations (Scopus)


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.

Original languageEnglish
Article number393
Issue number3
Publication statusPublished - 27 Jan 2019


  • Demand-side management
  • Feature selection
  • Hierarchical short-term load forecasting
  • Pattern similarity
  • Short-term load forecasting
  • Weather station selection

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering


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