Open Access
Balarihun Mawtyllup
Bikramjit Goswami
- Assam Don Bosco University Airport Road Azara, Guwahati India
- 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.
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International Journal of Microwave Engineering and Technology
| Volume | |
| Received | November 16, 2021 |
| Accepted | December 11, 2021 |
| Published | January 11, 2023 |