A comparison of computational intelligence techniques for energy time series forecasting

Energy time series forecasting plays a crucial role in the process of energy planning. This topic has been, and is still attracting vast research activities that are performed by researchers in the academia and energy companies. Various techniques exist for energy time series forecasting, and the selection of the most suitable forecasting algorithm is not an easy process. For a clear application of such techniques in energy time series forecasting, there should be a clear distinction between these techniques. This paper compares the overall performance of the Time Delay Neural Network (TDNN), Neuro Fuzzy Inference System and Support Vector Regression (SVR). The efficiency of these techniques is compared in energy time series forecasting, and the performances of them are tested. The results of our analysis indicate that the Time Delay Neural network (TDNN) shows the best performances overall.