%0 Journal Article
%T Towards Energy Efficiency: Forecasting Indoor Temperature via Multivariate Analysis
%A Francisco Zamora-Mart¨ªnez
%A Pablo Romeu
%A Paloma Botella-Rocamora
%A Juan Pardo
%J Energies
%D 2013
%I MDPI AG
%R 10.3390/en6094639
%X The small medium large system (SMLsystem) is a house built at the Universidad CEU Cardenal Herrera (CEU-UCH) for participation in the Solar Decathlon 2013 competition. Several technologies have been integrated to reduce power consumption. One of these is a forecasting system based on artificial neural networks (ANNs), which is able to predict indoor temperature in the near future using captured data by a complex monitoring system as the input. A study of the impact on forecasting performance of different covariate combinations is presented in this paper. Additionally, a comparison of ANNs with the standard statistical forecasting methods is shown. The research in this paper has been focused on forecasting the indoor temperature of a house, as it is directly related to HVAC¡ªheating, ventilation and air conditioning¡ªsystem consumption. HVAC systems at the SMLsystem house represent 53:89% of the overall power consumption. The energy used to maintain temperature was measured to be 30%¨C38:9% of the energy needed to lower it. Hence, these forecasting measures allow the house to adapt itself to future temperature conditions by using home automation in an energy-efficient manner. Experimental results show a high forecasting accuracy and therefore, they might be used to efficiently control an HVAC system.
%K energy efficiency
%K time series forecasting
%K artificial neural networks
%U http://www.mdpi.com/1996-1073/6/9/4639