Towards Energy Efficiency: Forecasting Indoor Temperature via Multivariate Analysis

Francisco Zamora-Martínez, Pablo Romeu, Paloma Botella-Rocamora and Juan Pardo
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Francisco Zamora-Martínez: Escuela Superior de Enseñanzas Técnicas, Universidad CEU Cardenal Herrera, C/ San Bartolomé 55, Alfara del Patriarca 46115, Valencia, Spain
Pablo Romeu: Escuela Superior de Enseñanzas Técnicas, Universidad CEU Cardenal Herrera, C/ San Bartolomé 55, Alfara del Patriarca 46115, Valencia, Spain
Paloma Botella-Rocamora: Escuela Superior de Enseñanzas Técnicas, Universidad CEU Cardenal Herrera, C/ San Bartolomé 55, Alfara del Patriarca 46115, Valencia, Spain
Juan Pardo: Escuela Superior de Enseñanzas Técnicas, Universidad CEU Cardenal Herrera, C/ San Bartolomé 55, Alfara del Patriarca 46115, Valencia, Spain

Energies, 2013, vol. 6, issue 9, 1-21

Abstract: 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 53:89% 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.

Keywords: energy efficiency; time series forecasting; artificial neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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