A Comparative Assessment of Conventional and Artificial Neural Networks Methods for Electricity Outage Forecasting

Adeniyi Kehinde Onaolapo,Evans Eshiemogie Ojo,Rudiren Pillay Carpanen,David George Dorrell

doi:10.3390/en15020511

Abstract

The reliability of the power supply depends on the reliability of the structure of the grid. Grid networks are exposed to varying weather events, which makes them prone to faults. There is a growing concern that climate change will lead to increasing numbers and severity of weather events, which will adversely affect grid reliability and electricity supply. Predictive models of electricity reliability have been used which utilize computational intelligence techniques. These techniques have not been adequately explored in forecasting problems related to electricity outages due to weather factors. A model for predicting electricity outages caused by weather events is presented in this study. This uses the back-propagation algorithm as related to the concept of artificial neural networks (ANNs). The performance of the ANN model is evaluated using real-life data sets from Pietermaritzburg, South Africa, and compared with some conventional models. These are the exponential smoothing (ES) and multiple linear regression (MLR) models. The results obtained from the ANN model are found to be satisfactory when compared to those obtained from MLR and ES. The results demonstrate that artificial neural networks are robust and can be used to predict electricity outages with regards to faults caused by severe weather conditions.

Highlights

A careful analysis of the results shows that the accuracy of the prediction of the power system outages depends on the ten variables considered in artificial neural networks (ANNs) Model 1 and multiple linear regression (MLR) Model 1
Ten potential explanatory variables were used for Model 1, while seven potential explanatory variables were used for Model 2 after careful screening using correlation analysis
An analysis showed that ANN Model 1 was better than the other models in terms of performance

Summary

Introduction

Plans for economic development in developing countries are incomplete without including planned reliable electricity supply. This is virtually indispensable in modern society and requires dependable generation, transmission and distribution stages. There is an interrelation between weather events and outage events; an increase in weather events leads to an increase in the rate of grid fault occurrence. This makes the supply less reliable with higher maintenance costs. There is a comparative evaluation of the performance of artificial neural networks (ANN), multiple linear regression (MLR), and the exponential smoothing (ES) models

Methods

Results

Conclusion