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

Adeniyi Kehinde Onaolapo, Rudiren Pillay Carpanen, David George Dorrell and Evans Eshiemogie Ojo
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Adeniyi Kehinde Onaolapo: Discipline of Electrical, Electronic and Computer Engineering, University of Kwazulu-Natal, Durban 4041, South Africa
Rudiren Pillay Carpanen: Discipline of Electrical, Electronic and Computer Engineering, University of Kwazulu-Natal, Durban 4041, South Africa
David George Dorrell: School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg 4041, South Africa
Evans Eshiemogie Ojo: Department of Electrical Power Engineering, Durban University of Technology, Durban 4000, South Africa

Energies, 2022, vol. 15, issue 2, 1-21

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.

Keywords: artificial neural networks; multiple linear regression; exponential smoothing; predictive model; weather events (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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