A review on short-term load forecasting models for micro-grid application

被引:26
作者
Kondaiah, V. Y. [1 ]
Saravanan, B. [1 ]
Sanjeevikumar, P. [2 ,3 ]
Khan, Baseem [4 ]
机构
[1] VIT Univ, Sch Elect Engn, Vellore, Tamil Nadu, India
[2] KPR Inst Engn & Technol, Dept Elect & Elect Engn, Vellore, Tamil Nadu, India
[3] Aarhus Univ, Dept Business Dev & Technol, CTiF Global Capsule, Herning, Denmark
[4] Hawassa Univ, Dept Elect & Comp Engn, Hawassa 05, Ethiopia
来源
JOURNAL OF ENGINEERING-JOE | 2022年 / 2022卷 / 07期
关键词
INTEGRATED MOVING AVERAGE; SUPPORT VECTOR REGRESSION; EXTREME LEARNING-MACHINE; ARTIFICIAL NEURAL-NETWORK; ENERGY DEMAND PREDICTION; ELECTRICITY-LOAD; WIND-SPEED; TIME-SERIES; HYBRID MODEL; COMPUTATIONAL INTELLIGENCE;
D O I
10.1049/tje2.12151
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Load forecasting (LF), particularly short-term load forecasting (STLF), plays a vital role throughout the operation of the conventional power system. The precise modelling and complex analyses of STLF have become more significant in advanced microgrid (MG) applications. Several models are proposed for STLF and tested successfully in the literature. The selection of a forecasting method is mostly based on data availability and its objectives. This article presents a survey of the latest analytical and approximation techniques reported in the literature to model STLF in an MG environment. This article mainly focusses on the review on important methods applied to forecast renewable energy availability, energy demand, and price and load demand. Different models, their main objectives, methodology, error percentage, and so forth, are critically reviewed and analysed. For quick reference, we have highlighted the important points in the form tables. The researchers can quickly identify and frame their research problem related to the LF area by reading this review paper.
引用
收藏
页码:665 / 689
页数:25
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