An Expression-Driven Approach for Long-Term Electric Power Consumption Forecasting

This study deals with estimation of electricity demand of Iran on the basis of economic criteria using a genetic-based approach called Gene Expression Programming (GEP) as an expression-driven approach. The GEP-based mathematical model is provided based on population, gross domestic product, exports, and imports. The proposed model is derived based on available data obtained from 1992 to 2006. To assess the forecasting accuracy of the model, the electricity demand from 2007 until 2012 are calculated by the GEP-based model and the obtained results are compared with the real data during this period. To show the accuracy of the model, the results obtained by GEP model are compared with the results obtained from Multi-Layer Perceptron (MLP) neural network and Multiple Linear Regression (MLR) as the two conventional methods. In addition, a five-fold cross-validation and future year prediction are used to show the robustness of the model in predicting the electricity demand. Future estimation of Iran's electric energy consumption is then projected up to 2030 according to three different scenarios. Finally, a sensitivity analysis is conducted to identify the most important independent variables affecting electricity demand.