Soft computing approach in modeling energy consumption

Abstract

In this chapter, we build an intelligent model based on soft computing technologies to improve the prediction accuracy of Energy Consumption in Greece. The model is developed based on Genetic Algorithm and Co-Active Neuro Fuzzy Inference System (GACANFIS) for the prediction of Energy Consumption. For verification of the performance accuracy, the results of the propose GACANFIS model were compared with the performance of Backpropagation Neural network (BP-NN), Fuzzy Neural Network (FNN), and Co-Active Neuro Fuzzy Inference System (CANFIS). Performance analysis shows that the propose GACANFIS improve the prediction accuracy of Energy Consumption as well as CPU time. Comparison of the results with previous literature further proved the effectiveness of the proposed approach. The prediction of Energy Consumption is required for expanding capacity, strategy in Energy supply, investment in capital, analysis of revenue, and management of market research. © 2014 Springer International Publishing.