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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.

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Abstract. In this chapter, a study of deep learning of time-series forecasting techniques is presented. Using Stacked Denoising Auto-Encoders, it is possible to disentangle complex characteristics in time series data. The effects of complete and partial fine-tuning are shown. SDAE prove to be able to train deeper models, and consequently to learn more complex characteristics in the data. Hence, these models are able to generalize better. Pre-trained models show a better generalization when used without covariates. The learned weights show to be sparse, suggesting future exploration and research lines.

This work presents the empirical evaluation of an indoor temperature prediction module which is integrated in an ambient intelligence control software. This software is running on the SMLhouse, a domotic house built by our university. A study of impact on prediction error of future window size has been performed. We use Artificial Neural Networks models for a multi-step-ahead direct forecasting, using an output size of 60, 120, and 180. Interesting results have been obtained, in the worst case a Mean Absolute Error of 0.223 • C over a validation set, and 0.566 • C over a hard unseen test set. This results inspire the development of an automatic control built over this predictions, that could manage the climate system in order to enhance the comfort and energy efficiency of our house.