Journal
ENERGIES
Volume 6, Issue 9, Pages 4639-4659Publisher
MDPI
DOI: 10.3390/en6094639
Keywords
energy efficiency; time series forecasting; artificial neural networks
Categories
Funding
- Banco Santander
- CEU Cardenal Herrera University through the project [Santander-PRCEU-UCH07/12]
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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 53.8 9 % 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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