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Reinforcement Learning-Based School Energy Management System

Yassine Chemingui, Adel Gastli and Omar Ellabban
Additional contact information
Yassine Chemingui: Electrical Engineering Department, Qatar University, P.O. Box 2713 Doha, Qatar
Adel Gastli: Electrical Engineering Department, Qatar University, P.O. Box 2713 Doha, Qatar
Omar Ellabban: Iberdrola Innovation Middle East, Qatar Science & Technology Park, P.O. Box 210177 Doha, Qatar

Energies, 2020, vol. 13, issue 23, 1-21

Abstract: Energy efficiency is a key to reduced carbon footprint, savings on energy bills, and sustainability for future generations. For instance, in hot climate countries such as Qatar, buildings are high energy consumers due to air conditioning that resulted from high temperatures and humidity. Optimizing the building energy management system will reduce unnecessary energy consumptions, improve indoor environmental conditions, maximize building occupant’s comfort, and limit building greenhouse gas emissions. However, lowering energy consumption cannot be done despite the occupants’ comfort. Solutions must take into account these tradeoffs. Conventional Building Energy Management methods suffer from a high dimensional and complex control environment. In recent years, the Deep Reinforcement Learning algorithm, applying neural networks for function approximation, shows promising results in handling such complex problems. In this work, a Deep Reinforcement Learning agent is proposed for controlling and optimizing a school building’s energy consumption. It is designed to search for optimal policies to minimize energy consumption, maintain thermal comfort, and reduce indoor contaminant levels in a challenging 21-zone environment. First, the agent is trained with the baseline in a supervised learning framework. After cloning the baseline strategy, the agent learns with proximal policy optimization in an actor-critic framework. The performance is evaluated on a school model simulated environment considering thermal comfort, CO 2 levels, and energy consumption. The proposed methodology can achieve a 21% reduction in energy consumption, a 44% better thermal comfort, and healthier CO 2 concentrations over a one-year simulation, with reduced training time thanks to the integration of the behavior cloning learning technique.

Keywords: energy efficiency; energy management; indoor air quality; reinforcement learning; smart building; thermal comfort (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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