Energy’s exports forecasting by a neuro-fuzzy controller

Since accurate forecasting of energy export is very important for planning potential energy demand and improving the energy production sector, various forecasting methods have been developed. The present work is focused to apply a novel technique, an integrated neuro-fuzzy controller named PATSOS. The forecasting system is based on two Adaptive Neural Fuzzy Inference Systems (ANFIS) that form an inverse controller. An ANFIS model represents the controller and another ANFIS represents the energy export model that is going to be controlled. ANFIS uses a combination of the least-squares method and the backpropagation gradient descent method to estimate the optimal energy export forecast parameters. The ANFIS controller belongs to direct control and is based on inverse learning, also known as general learning. Hourly data sets during the period 1 January 2009 to 31 December 2009 were used to learn and evaluate the proposed system. The forecast accuracy of the proposed technique was evaluated using out of sample tests. The results of the simulation based on statistical errors and the experimental investigations carried out on the laboratory showed that the model despite the high data volatility, is suitable for forecasting hourly energy exports. © 2015, Springer-Verlag Berlin Heidelberg.