Hou Shengren
Research
Research
Methods and themes behind the work
This work is driven by a long-term ambition: improving power-market efficiency and helping build a more sustainable energy system through AI, market design, and trading practice.
I pay particular attention to short-term power markets, where renewable uncertainty, self-dispatch, and price formation meet most directly.
Research Themes
Core domains
Electricity Markets
Market design, price formation, short-term market behavior, and cross-border trading mechanisms.
Energy AI
Forecasting, probabilistic modeling, optimization, and safe decision systems for complex energy environments.
Storage and Flexibility
Battery dispatch, flexibility assets, and market participation under operational and regulatory constraints.
Energy Digitalization
Turning models and workflows into decision systems that real energy organizations can deploy and use.
Methods
What I work with
Modeling Stack
Time-series forecasting, feature engineering, probabilistic modeling, optimization, and scenario analysis.
Decision Stack
Constraint-aware decision models, reinforcement learning, and safe AI for energy systems and market workflows.
Selected Public Projects
Representative work
- RL-ADN An open research environment for energy storage dispatch and deep reinforcement learning.
- DATALESs System-theoretic data analytics for local energy systems and green-building coordination.
- TU Delft campus energy digital twin work Monitoring, forecasting, and decision-support ideas for campus-scale energy systems.
研究
支撑这些工作的研究方法与主题
这部分工作的长期目标，是通过 AI、市场设计与交易实践提升电力市场效率，并帮助构建更可持续的能源系统。
我尤其关注短期电力市场，因为可再生能源不确定性、自调度机制与价格发现，往往在这里最直接地相遇。
研究主题
核心方向
电力市场
关注市场设计、价格形成、短期市场行为以及跨区交易机制。
能源 AI
关注预测、概率建模、优化以及复杂能源场景中的安全决策系统。
储能与灵活性
聚焦储能调度、灵活性资源以及受运行与规则约束影响的市场参与逻辑。
能源数字化
把模型与工作流做成能源组织真正能部署、能使用的决策系统。
方法
我常用的方法栈
建模栈
时间序列预测、特征工程、概率建模、优化与情景分析。
决策栈
约束感知决策模型、强化学习与面向能源系统和市场工作流的安全 AI。
公开项目
代表性工作
- RL-ADN 面向储能调度与深度强化学习的开源研究环境。
- DATALESs 面向本地能源系统与绿色建筑协同的系统理论数据分析项目。
- 代尔夫特理工校园能源数字孪生相关工作 围绕监测、预测与决策支持展开的校园级能源系统探索。