A neural network model for energy consumption prediction of CIESOL bioclimatic building

Abstract

Energy efficiency in buildings is a topic that is being widely studied. In order to achieve energy efficiency it is necessary to perform both, a proper management of the electric demand, and an optimal exploitation of renewable sources, using for that appropriate control strategies. The main objective of this paper is to develop a short term predictive model, based on neural networks, of the electricity demand for the CIESOL research center. The performed experiments, using different techniques for weather forecast, show a quick prediction with acceptable final results for real data, obtaining a maximum root mean squared error of 5% in validation data, with a short-term prediction horizon of 60 minutes.