Learning to Price Electricity for Optimal Demand Response

arXiv:2610.00755 · stat.ML, cs.LG, stat.AP · Submitted 2026-09-30 · Read on arXiv

stat.ML, cs.LG, stat.AP

Submitted: 2026-09-30

Updated: 2026-10-02

Terminology

• Clustering — 9× in this paper · explained in 2 episodes
• Activation Function — 7× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Loss Function — 5× in this paper · explained in 2 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Trace — 4× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 3× in this paper · explained in 2 episodes
• Objective Function — 3× in this paper · explained in 2 episodes
• Convergence Guarantees — 2× in this paper · explained in 3 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes

Sources

• On Approximation Capabilities of ReLU Activation and Softmax Output Layer in Neural Networks
• Optimal Mechanisms for Demand Response: An Indifference Set Approach
• Unlocking Deep Demand Flexibility via Dynamic Signals

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