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Weather-adaptive reinforcement learning framework for heat pump control under time-of-use tariffs
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DOI：10.1016/j.enconman.2026.122015.png)
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Abstract
En 中文
• Novel reinforcement learning framework for heat pump control under TOU tariffs. • Mixture-of-experts model improves policy generalization across weather conditions. • Achieves 16.5% average electricity cost reduction vs rule-based control. • Reduces electricity costs by over 21% under cold temperature conditions.
Keywords:
Demand-side management
Economic optimization
Energy flexibility
Energy management system
Heat pump
HVAC
Reinforcement learning
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