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

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

doi:10.3390/en12030393

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

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

Research output: Journal article publication › Review article › Academic research › peer-review

101 Citations (Scopus)

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.

Original language	English
Article number	393
Journal	Energies
Volume	12
Issue number	3
DOIs	https://doi.org/10.3390/en12030393
Publication status	Published - 27 Jan 2019

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

More information

10.3390/en12030393

http://hdl.handle.net/10397/80670

Cite this

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title = "Computational intelligence on short-term load forecasting: A methodological overview",

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.",

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T2 - A methodological overview

AU - Fallah, Seyedeh Narjes

AU - Ganjkhani, Mehdi

AU - Shamshirband, Shahaboddin

AU - Chau, Kwok wing

PY - 2019/1/27

Y1 - 2019/1/27

N2 - 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.

AB - 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.

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KW - Feature selection

KW - Hierarchical short-term load forecasting

KW - Pattern similarity

KW - Short-term load forecasting

KW - Weather station selection

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DO - 10.3390/en12030393

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AN - SCOPUS:85060932815

SN - 1996-1073

VL - 12

JO - Energies

JF - Energies

IS - 3

M1 - 393

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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