Smart and predictive heating system: Belief model for indoor regulation

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 combines the weather forecast and the thermal comfort associated to the occupant's activities and habits. The challenge we took is complex as the data to be fused are highly uncertain and heterogeneous but our method proved its efficiency as we obtain very satisfactory simulation results.

[1]  Corinna Fischer.  Feedback on household electricity consumption: a tool for saving energy? , 2008 .

[2]  Petru-Daniel Morosan,et al.  Building temperature regulation using a distributed model predictive control , 2010 .

[3]  Manfred Morari,et al.  Use of model predictive control and weather forecasts for energy efficient building climate control , 2012 .

[4]  R. Nordman.  IEA Technology Roadmap - Energy-efficient Buildings : Heating and Cooling Equipment , 2011 .

[5]  Antonio Messineo,et al.  Coupling a neural network temperature predictor and a fuzzy logic controller to perform thermal comfort regulation in an office building , 2014 .

[6]  Ohbyung Kwon,et al.  Single activity sensor-based ensemble analysis for health monitoring of solitary elderly people , 2012, Expert Syst. Appl..

[7]  V. Masson,et al.  The AROME-France Convective-Scale Operational Model , 2011 .

[8]  Alistair B. Sproul,et al.  Optimisation of energy management in commercial buildings with weather forecasting inputs: A review , 2014 .

[9]  Stéphane Ploix,et al.  Managing Energy Smart Homes according to Energy Prices: Analysis of a Building Energy Management System , 2014 .

[10]  Soteris A. Kalogirou,et al.  Applications of artificial neural networks in energy systems , 1999 .

[11]  Philippe Smets,et al.  The Transferable Belief Model , 1991, Artif. Intell..

[12]  Jie Zhao,et al.  Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining , 2014 .

[13]  Philippe Smets,et al.  The Transferable Belief Model , 1994, Artif. Intell..

[14]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[15]  Philippe Smets,et al.  The Combination of Evidence in the Transferable Belief Model , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Anastasios I. Dounis,et al.  Comparison of Conventional and Fuzzy Control of Indoor Air Quality in Buildings , 1996, J. Intell. Fuzzy Syst..

[17]  Ji-Hyun Lee,et al.  Performance evaluation of artificial neural network-based variable control logic for double skin enveloped buildings during the heating season , 2014 .