Use of Machine Learning Algorithms in Power System Load Forecasting: A review

Open Access

Year : 2021 | Volume : | Issue : 1 | Page : 23-30
By

    Balarihun Mawtyllup

  1. Bikramjit Goswami

  1. , Assam Don Bosco University Airport Road, Azara, Guwahati, India
  2. , Assam Don Bosco University Airport Road, Azara, Guwahati, India

Abstract

The electrical load forecasting has become one of the most important fields of research for secured, efficient, accurate, reliable power dispatch and management. The main aim of load demand forecasting is to predict the load demand for accurate generation scheduling, system security and economic dispatch of load at any time. Many Authors have done research on the evaluation of load forecasting methods, to improve the accuracy of prediction. In load forecasting, it is essential to cover all time zones from short term to long term, to have a better management of the power system. It is challenging to have an accuracy result with less input data. Machine learning methods have obtained more attention as dealing with randomness in load arrangement is the need. This paper presents the review of several machine learning methods used for load forecasting, viz., SVM, Regression, ANN, Fuzzy logic etc. and to evaluate performance of different methods using electricity consumption dataset, compare between different methods as well as in analyzing calculation complexities of parameters such as-MAPE, RMSE etc.

Keywords: Load forecasting, Machine Learning, ANN, Regression, RMSE.

[This article belongs to International Journal of Microwave Engineering and Technology(ijmet)]

How to cite this article: Balarihun Mawtyllup, Bikramjit Goswami Use of Machine Learning Algorithms in Power System Load Forecasting: A review ijmet 2021; 7:23-30
How to cite this URL: Balarihun Mawtyllup, Bikramjit Goswami Use of Machine Learning Algorithms in Power System Load Forecasting: A review ijmet 2021 {cited 2021 Dec 14};7:23-30. Available from: https://journals.stmjournals.com/ijmet/article=2021/view=90608

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Regular Issue Open Access Article
Volume 7
Issue 1
Received November 16, 2021
Accepted December 11, 2021
Published December 14, 2021