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Open AccessArticle

Towards Energy Efficiency: Forecasting Indoor Temperature via Multivariate Analysis

Escuela Superior de Enseñanzas Técnicas, Universidad CEU Cardenal Herrera, C/ San Bartolomé 55, Alfara del Patriarca 46115, Valencia, Spain
Author to whom correspondence should be addressed.
Energies 2013, 6(9), 4639-4659;
Received: 1 July 2013 / Revised: 17 August 2013 / Accepted: 21 August 2013 / Published: 9 September 2013
(This article belongs to the Special Issue Energy Efficient Building Design 2013)
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. View Full-Text
Keywords: energy efficiency; time series forecasting; artificial neural networks energy efficiency; time series forecasting; artificial neural networks
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Zamora-Martínez, F.; Romeu, P.; Botella-Rocamora, P.; Pardo, J. Towards Energy Efficiency: Forecasting Indoor Temperature via Multivariate Analysis. Energies 2013, 6, 4639-4659.

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