Some Empirical Evaluations of a Temperature Forecasting Module based on Artificial Neural Networks for a Domotic Home Environment

Abstract

This work presents the empirical evaluation of an indoor temperature prediction module which is integrated in an ambient intelligence control software. This software is running on the SMLhouse, a domotic house built by our university. A study of impact on prediction error of future window size has been performed. We use Artificial Neural Networks models for a multi-step-ahead direct forecasting, using an output size of 60, 120, and 180. Interesting results have been obtained, in the worst case a Mean Absolute Error of 0.223 • C over a validation set, and 0.566 • C over a hard unseen test set. This results inspire the development of an automatic control built over this predictions, that could manage the climate system in order to enhance the comfort and energy efficiency of our house.