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Energies 2013, 6(9), 4639-4659; doi:10.3390/en6094639

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.
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)
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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 energy efficiency; time series forecasting; artificial neural networks
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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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