Derivation of simplified control rules from an optimal strategy for electrical heating in a residential building

Abstract : In France, 40 % of buildings are heated with electrical devices causing high peak load in winter. In this context, advanced control systems could improve buildings energy management. More specifically, optimal strategies have been developed using a dynamic programming method in order to shift heating load, taking advantage of the building thermal mass. However, this optimisation method is computationally intensive and can hardly be applied to real-time control. Statistical techniques can be used to derive near-optimal laws from the optimal control results. These rule extraction techniques model the relationship between explanatory variables and a response variable. This paper investigates the use of Beta regression model to identify near-optimal control rules. This regression-based strategy was able to mimic the general characteristics of the optimisation results with a small mean bias error (-6 %) and greatly reduce computational effort (150 times faster than the dynamic programming method). Given its simple mathematical formulation, it could be implemented in real-time building systems control.
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Submitted on : Tuesday, February 12, 2019 - 8:13:22 AM
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M. Robillart, P. Schalbart, Bruno Peuportier. Derivation of simplified control rules from an optimal strategy for electrical heating in a residential building. Journal of Building Performance Simulation, Taylor & Francis, 2017, 11 (3), pp.294-308. ⟨10.1080/19401493.2017.1349835⟩. ⟨hal-01982624⟩

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