GP-preprocessed fuzzy inference for the energy load prediction

This paper deals with a prediction system based on genetic programming and fuzzy inference system. In real problems with many parameters, the prediction performance depends on the feature extraction and selection. These processes are performed using methods of multivariate statistical analysis by human operators. However, we should automatically perform feature extraction and selection from many measured data. This paper applies genetic programming for the feature extraction and selection, and further use fuzzy inference for the building energy load prediction. The functions generated by GP translate the measured data into the meaningful information that is used as input data to the fuzzy inference system. The simulation results show that the proposed method can extract meaningful information from the measured data and can predict the building energy load of the next day