Review on Analysis of Power Supply and Demand in Maharashtra State for Load Forecasting Using ANN

Authors

  • Suraj G. Patil  ME Scholar, Electrical Engineering Department, Prof. Ram Meghe College of Engineering and Management, Badnera, Amravati, India.
  • Dr. M. S. Ali  Professor, Electrical Engineering Department, Prof. Ram Meghe College of Engineering and Management, Badnera, Amravati, India.

DOI:

https://doi.org/10.32628/IJSRST229152

Keywords:

Short term load forecasting, Back Propagation, Artificial Neural Network.

Abstract

The Electric load forecasting (ELF) is a critical procedure in the electrical industry's planning and plays a critical role in electric capacity scheduling and power system management, hence it has piqued academic attention. As a result, for energy generating capacity scheduling and power system management, the accuracy of electric load forecasting is critical. This document provides an overview of power load forecasting methodologies and models. A total of 40 scholarly publications were included in the comparison, which was based on certain criteria such as time frame, inputs, outcomes, project scale, and value. Despite the relative simplicity of all studied models, the regression analysis is still extensively employed and effective for long-term forecasting, according to the research. Machine learning or artificial intelligence-based models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy logic are preferred for short-term forecasts.

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International Journal of Scientific Research in Science and Technology

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Published

2022-02-28

Issue

Volume 9, Issue 1, January-February-2022

Section

Research Articles

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How to Cite

[1]
Suraj G. Patil, Dr. M. S. Ali "Review on Analysis of Power Supply and Demand in Maharashtra State for Load Forecasting Using ANN" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 1, pp.341-347, January-February-2022. Available at doi : https://doi.org/10.32628/IJSRST229152