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This paper presents a data fusion system, which models and handles uncertainties in buildings, in order to design a smart heating system. The data fusion model provides a temperature trend that will be inserted in smart and predictive heating systems in order to reduce energy consumption in the building. The multilevel data fusion model is based on the belief functions theory. Different simulations...
The objective of this paper is to investigate a method to model data uncertainties in order to regulate a smart heating system that reduces energy consumption. To achieve this, we propose a multilevel data fusion system that provides a contextual trend, based on the belief theory of Dempster-Shafer for data combination and the Transferable Belief Model (TBM) to take the decision. The fusion system...
In this paper, we propose an intelligent data-analysis method for modeling and prediction of daily electricity consumption in buildings. The objective is to enable a building-management system to be used for forecasting and detection of abnormal energy use. First, an outlier-detection method is proposed to identify abnormally high or low energy use in a building. Then a canonical variate analysis...
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