ugr-sail/paper-drl_building
Supplementary material to the paper "An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control".
This project provides an in-depth, reproducible evaluation of Deep Reinforcement Learning (DRL) algorithms for controlling heating, ventilation, and air conditioning (HVAC) systems in buildings. It takes in various DRL algorithms and building energy simulation data, then outputs their performance metrics in terms of energy consumption and occupant comfort. Building managers, facilities engineers, and energy efficiency consultants can use this to understand the practical implications of advanced HVAC control strategies.
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Use this if you are a building energy manager or facilities engineer interested in the potential of AI-driven HVAC systems to optimize energy use and maintain occupant comfort.
Not ideal if you are looking for a plug-and-play HVAC control system ready for immediate deployment without further customization or development.
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Oct 03, 2024
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