Combined peak reduction and self-consumption using proximal policy optimisation

Residential demand response programs aim to activate demand flexibility at the household level. In recent years, reinforcement learning (RL) has gained significant attention for these type of applications. A major challenge of RL algorithms is data efficiency. New RL algorithms, such as proximal pol...

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מידע ביבליוגרפי

Principais autores:	Thijs Peirelinck, Chris Hermans, Fred Spiessens, Geert Deconinck
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	Elsevier 2024-05-01
סדרה:	Energy and AI
נושאים:	Demand response Reinforcement learning Electric water heater Peak shaving Transfer learning
גישה מקוונת:	http://www.sciencedirect.com/science/article/pii/S2666546823000952
תגים:	הוספת תג אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!