Offline-Online Reinforcement Learning for Energy Pricing in Office Demand Response: Lowering Energy and Data Costs [article]

Doseok Jang, Lucas Spangher, Manan Khattar, Utkarsha Agwan, Selvaprabuh Nadarajah, Costas Spanos
2021 arXiv   pre-print
Our team is proposing to run a full-scale energy demand response experiment in an office building. Although this is an exciting endeavor which will provide value to the community, collecting training data for the reinforcement learning agent is costly and will be limited. In this work, we examine how offline training can be leveraged to minimize data costs (accelerate convergence) and program implementation costs. We present two approaches to doing so: pretraining our model to warm start the
more » ... eriment with simulated tasks, and using a planning model trained to simulate the real world's rewards to the agent. We present results that demonstrate the utility of offline reinforcement learning to efficient price-setting in the energy demand response problem.
arXiv:2108.06594v1 fatcat:hy3l5fclgbgezp2aqkwvtzsdx4