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- W4248520587 abstract "Thermostatically Controlled Loads (TCLs) provide a source of demand flexibility, and are often considered a good source for Demand Response (DR) applications. Due to their heterogeneity, and as such a lack of dynamics models, Reinforcement Learning (RL) is often used to exploit this flexibility. Unfortunately, RL requires exploratory interaction with the TCL, resulting in a period of potential discomfort for the users. We present an approach to reduce this exploratory time by pre-training the RL-agent. Domain randomization is used to facilitate knowledge transfer. We evaluate the pre-training potential in a DR energy arbitrage scenario with an Electric Water Heater (EWH). Our experiments show that a priori knowledge about EWH dynamics can be used to initialize and improve the control policy. In our experiments, pre-training attributes to 8.8 % additional cost savings, compared to starting from scratch." @default.
- W4248520587 created "2022-05-12" @default.
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- W4248520587 date "2021-07-26" @default.
- W4248520587 modified "2023-10-15" @default.
- W4248520587 title "Domain Randomization for Demand Response of an Electric Water Heater" @default.
- W4248520587 doi "https://doi.org/10.1109/pesgm46819.2021.9638203" @default.
- W4248520587 hasPublicationYear "2021" @default.
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