用生成式AI构建美国住宅光伏的微观数字孪生数据集
A Generative AI Technique for Synthesizing a Digital Twin for U.S. Residential Solar Adoption and Generation
- 融合机器学习与可解释AI，识别家庭级光伏采用者
- 生成全美范围小时级住宅光伏出力数据，精度经真实数据验证
- 适用于能源政策模拟，尤其适合低收入社区研究
住宅屋顶光伏部署对减少碳排放至关重要。但缺乏家庭级、小时级的光伏数据严重制约了科学决策。本文提出一种新型数据驱动方法，构建覆盖美国本土各州的高粒度住宅光伏采用数据集。方法包括：(i) 使用集成机器学习模型识别光伏采纳者；(ii) 通过可解释AI技术增强数据，揭示关键特征及其交互关系；(iii) 基于解析模型生成家庭级小时尺度太阳能发电量。合成数据经真实数据验证，可作为下游任务的数字孪生。最后，基于弗吉尼亚州的政策案例研究显示，30%联邦太阳能投资税收抵免可显著提升光伏普及率，尤其在低至中等收入社区。
原文摘要 · Abstract (English)
Residential rooftop solar adoption is considered crucial for reducing carbon emissions. The lack of photovoltaic (PV) data at a finer resolution (e.g., household, hourly levels) poses a significant roadblock to informed decision-making. We discuss a novel methodology to generate a highly granular, residential-scale realistic dataset for rooftop solar adoption across the contiguous United States. The data-driven methodology consists of: (i) integrated machine learning models to identify PV adopters, (ii) methods to augment the data using explainable AI techniques to glean insights about key features and their interactions, and (iii) methods to generate household-level hourly solar energy output using an analytical model. The resulting synthetic datasets are validated using real-world data and can serve as a digital twin for modeling downstream tasks. Finally, a policy-based case study utilizing the digital twin for Virginia demonstrated increased rooftop solar adoption with the 30\% Federal Solar Investment Tax Credit, especially in Low-to-Moderate-Income communities.
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