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

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.

Automated summary

The reliability of the power supply depends on the reliability of the grid structure, which is prone to faults due to varying weather events. With the concern of increasing and severe weather events caused by climate change, it is important to explore predictive models for electricity outages caused by weather factors. This study presents a model using artificial neural networks to predict electricity outages caused by severe weather conditions and demonstrates their robustness compared to conventional models.