  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---heating, ventilation and air conditioning---system
consumption. HVAC systems at the SMLSystem house represent 53.9% of the overall
power consumption. The energy used to maintain temperature was measured to be
30--38.9% 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.
