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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Adeniyi Kehinde Onaolapo & Rudiren Pillay Carpanen & David George Dorrell & Evans Eshiemogie Ojo, 2022. "A Comparative Assessment of Conventional and Artificial Neural Networks Methods for Electricity Outage Forecasting," Energies, MDPI, vol. 15(2), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:2:p:511-:d:722549
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    References listed on IDEAS

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    1. David Ward, 2013. "The effect of weather on grid systems and the reliability of electricity supply," Climatic Change, Springer, vol. 121(1), pages 103-113, November.
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