Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

Kumilachew Chane,Fsaha Mebrahtu Gebru,Baseem Khan

doi:10.54060/jieee/002.01.002

Kumilachew Chane, Fsaha Mebrahtu Gebru + Show 1 more

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https://doi.org/10.54060/jieee/002.01.002

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Abstract

This paper explains the load forecasting technique for prediction of electrical load at Hawassa city. In a deregulated market it is much need for a generating company to know about the market load demand for generating near to accurate power. If the generation is not sufficient to fulfill the demand, there would be problem of irregular supply and in case of excess generation the generating company will have to bear the loss. Neural network techniques have been recently suggested for short-term load forecasting by a large number of researchers. Several models were developed and tested on the real load data of a Finnish electric utility at Hawassa city. The authors carried out short-term load forecasting for Hawassa city using ANN (Artificial Neural Network) technique ANN was implemented on MATLAB and ETAP. Hourly load means the hourly power consumption in Hawassa city. Error was calculated as MAPE (Mean Absolute Percentage Error) and with error of about 1.5296 % this paper was successfully carried out. This paper can be implemented by any intensive power-consuming town for predicting the future load and would prove to be very useful tool while sanctioning the load.

Highlights

The most used thing in today’s world is energy
The short term load forecasting is performed for planning of the distribution feeder
The main purpose of this study is to investigate an intelligence method for the short term load forecasting, by using three layer feed-forward and back-propagation neural networks for Hawassa city, Ethiopia

Summary

Introduction

The most used thing in today’s world is energy. We use energy in various forms in our day to day life, electricity, electricity, solar energy, wind energy, chemical energies in form of batteries and many other forms. Load forecast accurate models for electric power load forecasting are essential to the operation and planning of a utility company. An artificial neural network (ANN) is the piece of a computing system designed to simulate the way the human brain analyzes and processes information. It is the foundation of artificial intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standards. For the purpose of optimal planning and operation of electric power system, there is need for proper evaluation of present day and future electric power load [1, 6]. Power system expansion planning starts with a forecast of anticipated future load requirements Estimates of both demand and energy requirements are crucial to valuable system planning [7]. An electric load forecasting is used by an electric power company to anticipate the amount of electric energy needed to supply so as to meet up the demand [8]

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