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Towards Energy Efficiency: Forecasting Indoor Temperature via Multivariate Analysis
AbstractThe 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.
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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.View more citation formats
Zamora-Martínez F, Romeu P, Botella-Rocamora P, Pardo J. Towards Energy Efficiency: Forecasting Indoor Temperature via Multivariate Analysis. Energies. 2013; 6(9):4639-4659.Chicago/Turabian Style
Zamora-Martínez, Francisco; Romeu, Pablo; Botella-Rocamora, Paloma; Pardo, Juan. 2013. "Towards Energy Efficiency: Forecasting Indoor Temperature via Multivariate Analysis." Energies 6, no. 9: 4639-4659.
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