Authors:
F. Zamora-Martinez
;
P. Romeu
;
J. Pardo
and
D. Tormo
Affiliation:
Universidad CEU Cardenal Herrera, Spain
Keyword(s):
Artificial Neural Networks, Temporal Series Forecasting, Domotic Home Automation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Intelligence Applications
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
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 0.223ºC over a validation set, and 0.566ºC 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.