Towards estimation of electricity demand utilizing a robust multi-gene genetic programming technique

Multi-gene genetic programming (MGGP) is a new nonlinear system modeling approach that integrates the capabilities of standard genetic programming and classical regression. This paper deals with the application of this robust technique for the prediction of annual electricity demand in Thailand. The predictor variables included in the analysis were population, gross domestic product, stock index, and total revenue from exporting industrial products. Several statistical criteria were used to verify the validity of the model. A sensitivity analysis was performed to evaluate the contributions of the input features. The correlation coefficients between the measured and predicted electricity demand values are equal to 0.999 and 0.997 for the calibration and testing data sets, respectively. In addition to its high accuracy, MGGP outperforms regression and other powerful soft computing-based techniques.