Forecasting of peak electricity demand in Mauritius using the non-homogeneous Gompertz diffusion process

In this study, the non-homogeneous Gompertz diffusion process (NHGDP) is used to model the monthly peak electricity demand in Mauritius in order to predict the future values on the basis of a Genetic Algorithm (GA) approach. Our model is developed based a key economic indicator which is the gross domestic product (GDP) and the weather factors such as temperature, hours of sunshine and humidity. Genetic Algorithm then searches for the best coefficients by minimizing the root mean square error. Monthly data from January 2005 to December 2008 are considered to test the model. Finally, the Artificial Neural Network (ANN) is used to forecast each independent variable for the year 2009 and the NHGDP model is validated for that year. Our results show that the model provides an accurate and reliable prediction for the monthly peak electricity demand in Mauritius.

► We develop a non-homogeneous Gompertz diffusion process model.
► The model developed is based on GDP, temperature, hours of sunshine and humidity.
► We use the Genetic Algorithm (GA) to find the design variables.
► We estimate the weather factors and GDP using neural network and GA respectively.
► The model is used to predict the monthly peak electricity demand in Mauritius.