Machine learning-based algorithms to estimate thermal dynamics of residential buildings with energy flexibility

Abstract

In the residential sector, the building heating system is an energy-intensive consumer. Heat pumps are energy-efficient devices to integrate renewable power into buildings and provide flexibility for energy systems. Heat pump controllers assist in the release of flexibility potentials of thermal inertia and storage while meeting residents’ comfort. The heat controllers optimize the operation of building thermal dynamics which are stated by differential equations mathematically. The differential equations include dynamic thermal characteristics, i.e., thermal resistance and capacity, which are specified by estimation methods. The precision of the estimation methods affects the operation of heat controllers significantly. In this paper, the dynamic thermal characteristics of residential buildings are estimated using two grey-box models, i.e., the Continuous-Time Stochastic Model (CTSM) and Bayesian Optimization (BO), in R and Python software, respectively. Then, the estimated thermal characteristics are exported to UPPAAL-STRATEGO software to unlock the heat-to-power flexibility of heat pumps. The heat flexibility is generated using the probabilistic FlexOffer concept considering uncertain weather variables. Finally, the suggested approaches are examined on a 150 m2 family house with four temperature zones. Based on the simulation results, the BO exhibits an average of 31% higher accuracy in the estimation of dynamic thermal characteristics than the CTSM. Also, the FlexOffer concept generates 39.03 kWh and 36.93 kWh energy flexibility for the residential building using the BO and the CTSM with a gap of 5.38%.