Accelerating Reinforcement Learning for HVAC Systems Using an LSTM-based Surrogate Simulator
Creators
- 1. Middle East Tech Univ, Dept Elect & Elect Engn, Ankara, Turkiye
Description
Reinforcement learning (RL) has shown great potential in optimizing the operation of HVAC systems, improving energy efficiency, and enhancing user comfort. However, the slow input/output operations associated with simulation tools like EnergyPlus significantly hinder the training process. This paper proposes a novel approach to accelerate RL training by using a data-driven LSTM model to replicate the behavior of a building energy simulator. By training the LSTM model on a set of observations and actions, the model learns to approximate the simulator's dynamics, providing a faster and more efficient training environment for RL agents. We demonstrate that using the LSTM-based surrogate simulator leads to substantial reductions in computational time while maintaining the accuracy of the system's behavior.
Files
bib-3056cd6f-f897-4ead-8b87-4ce4cf0d9897.txt
Files
(242 Bytes)
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