Conference Paper

Proceedings of Building Simulation 2019: 16th Conference of IBPSA

     

Reinforcement Learning Control Algorithm for HVAC Retrofitting: Application to a Supermarket Building Model by Dynamic Simulation

Antonio Mastropietro 1,3, Fabio Castiglione 2, Stefano Ballesio 1, Enrico Fabrizio 2
1 Addfor S.p.A., Torino, Italy
2 Energy Department, TEBE Research Group, Politecnico di Torino, Torino, Italy
3 Department of Mathematical Sciences, Politecnico di Torino, Torino, Italy

DOI: https://doi.org/10.26868/25222708.2019.210614
Abstract: Efficient control of Heating, Ventilation and Air Conditioning systems can lead to great reduction in energy consumption. This can be achieved by new data-driven control algorithms based on Reinforcement Learning (RL). In this work Dynamic Simulation is coupled with a model-free RL algorithm to study its performance in terms of energy saving and thermal comfort in a realistic scenario. Two models are derived from the DOE Supermarket Reference Building for two climate locations. The simulations performed show a reduction between 5.4% and 9.4% in primary energy consumption for the two locations, guaranteeing the same thermal comfort of state-ofthe-art controls.
Keywords: Control; Reinforced Learning Algorithm; Machine Learning.
Pages: 1412 - 1419
Paper:

BS2019_210614