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• W3089273662 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&#x0025; additional cost savings, compared to starting from scratch." @default.
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• W3089273662 date "2021-03-01" @default.
• W3089273662 modified "2023-10-12" @default.
• W3089273662 title "Domain Randomization for Demand Response of an Electric Water Heater" @default.
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• W3089273662 doi "https://doi.org/10.1109/tsg.2020.3024656" @default.
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