arXiv：cs.LG（机器学习，全量分类）· Jing Shang, Mohammad Mehrabi, Xinyang Zhou, Mahmoud Saleh, Andrey Bernstein, Stefan Wager·· 13 小时前AI 评分23
用神经网络学习电力定价以实现最优需求响应
Learning to Price Electricity for Optimal Demand Response
AI 导读
研究者提出一种基于神经网络的上下文能源定价算法，将定价建模为 Stackelberg 博弈，并利用平均场解表示学习从上下文特征到可行价格信号的受限映射。该方法在美国多个城市的电网模拟中验证，显示引入上下文信息可显著提升需求响应项目的价值。
正文
Abstract:There is considerable interest in using time-varying electricity prices to shape consumer demand response, and better align energy demand with renewable production. However, optimal prices generally vary over time in response to complex signals such as weather forecasts, sunrise/sunset times, and day-of-week patterns; and existing methods are not able to make efficient use of such rich contextual information. Here, we propose a neural-network-based algorithm for contextual energy pricing, modeling pricing as a Stackelberg game and leveraging a mean-field solution representation from Mehrabi et al.~(2024). The approach learns constrained mappings from contextual features to feasible price signals. We validate our approach by simulating the energy grid in several US cities, and show that incorporating contextual information can considerably increase the value of the demand response programs.
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG); Applications (stat.AP) |
| Cite as: | arXiv:2610.00755 [stat.ML] |
| (or arXiv:2610.00755v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00755 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jing Shang [view email]
[v1]
Wed, 30 Sep 2026 21:50:02 UTC (1,867 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org