Towards Energy Efficiency: Forecasting Indoor Temperature via Multivariate Analysis

Energies

Francisco Zamora-Martínez¹  Pablo Romeu²  Paloma Botella-Rocamora² 

[1] Escuela Superior de Enseñanzas Técnicas, Universidad CEU Cardenal Herrera, C/ San Bartolomé 55, Alfara del Patriarca 46115, Valencia, Spain;

关键词: energy efficiency;    time series forecasting;    artificial neural networks;   

DOI  :  10.3390/en6094639

来源: mdpi

【 摘 要 】

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 representof 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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.