i-manager Publications

Short-Term Load Forecasting using Artificial Neural Network

Vidya Pawar*, Shekhappa Giriyappa Ankaliki**, Manjula S. Sureban***

*,*** Department of Electrical & Electronics Engineering, Sri Dharmasthala Manjunatheshwara College of Engineering and Technology, Dharwad, Karnataka, India.

** Department of Electrical & Electronics Engineering, Visvesvaraya Technological University, Belagavi, Karnataka, India.

Periodicity:February - April'2022
DOI : https://doi.org/10.26634/jps.10.1.18841

Abstract

One of the major research topics in electrical engineering in recent years is load prediction. Short-term load forecasting is necessary for the design, operation, and management of the power system. It is used, among others, by utilities, system operators, electricity producers, and suppliers. Artificial Neural Networks (ANN) have been used for short-term load prediction. The work has been completed to ensure day-to-day operations. Here, the proposed neural networks were trained and tested using newly available data from Hubli Electricity Supply Company Limited (HESCOM). This paper presents a method for predicting the load of a power system based on a Neural Network (NN). Matrix Laboratory (MATLAB) software is used to create training and test simulations. The error was defined as Mean Absolute Percentage Error (MAPE).

Keywords

Artificial Neural Network, Short-Term Load Forecasting, Back Propagation Neural Network, Mean Absolute Percentage Error.

How to Cite this Article?

Pawar, V., Ankaliki, S. G., and Sureban, M. S. (2022). Short-Term Load Forecasting using Artificial Neural Network. i-manager’s Journal on Power Systems Engineering, 10(1), 13-23. https://doi.org/10.26634/jps.10.1.18841

References

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	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	35	35	200	15
Pdf & Online	35	35	400	25

Short-Term Load Forecasting using Artificial Neural Network

Abstract

Keywords

How to Cite this Article?

References

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