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

被引：82

作者：

Fallah, Seyedeh Narjes

Ganjkhani, Mehdi ^{[1
]}

Shamshirband, Shahaboddin ^{[2
,3
]}

Chau, Kwok-wing ^{[4
]}

机构：

[1] Sharif Univ Technol, Dept Elect Engn, POB 11365-11155, Tehran, Iran

[2] Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh City, Vietnam

[3] Ton Duc Thang Univ, Fac Informat Technol, Ho Chi Minh City, Vietnam

[4] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China

来源：

ENERGIES | 2019年 / 12卷 / 03期

关键词：

short-term load forecasting; demand-side management; pattern similarity; hierarchical short-term load forecasting; feature selection; weather station selection; PARTICLE SWARM OPTIMIZATION; FEATURE-SELECTION; FEATURE-EXTRACTION; MEMETIC ALGORITHM; ELECTRICITY LOAD; NEURAL NETWORKS; VECTOR; MODEL; REGRESSION; IDENTIFICATION;

D O I：

10.3390/en12030393

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

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.

引用

页数：21

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