Prediction of indoor temperatures for energy optimization in buildings

The reduction of energy consumption in buildings is one of the goals to improve energy eciency. One way to achieve energy savings in buildings is to develop intelligent control strategies for heating systems that are able to reduce power consumption without aecting the thermal comfort. An intelligent control system must be able to predict the temperature of the building in order to manage the heating system. In this paper, we present three rule-based models that are able to predict the indoor temperature. The models have been learned with FRULER, a genetic fuzzy system that generates accurate and simple knowledge bases. Our approach has been validated with real data from a residential college showing errors lower than 0.50°C in the prediction of the temperatures.

keywords: energy optimization, indoor temperatures prediction, TSK fuzzy rules for regression, genetic fuzzy systems