SCC-IRG Track1: ADVANCED LEARNING FOR ENERGY RISK TRACKING (ALERT)

美国国家科学基金会S和CC项目旨在预测停电发生的风险状态(SOR)，并制定风险管理和缓解策略，将停电的影响降至最低。目前，电力公用事业公司只能对停电做出反应。在没有先发制人的选项来管理停机影响的情况下，消费者处于被动的角色，难以应对后果。该项目带来了变革性的变化，将允许公用事业公司预测停电，然后为消费者提供个人和社区缓解措施。这将通过提高S和CC对如何公平有效地处理停电影响的意识来实现。我们将部署高级数据分析，使用天气和历史停电数据来训练机器学习停电预测算法。该项目的智力优势包括新的风险预测方法，对停机预测的行为方面的研究，以及测量预测性警报消息有效性的实验。更广泛的影响包括，通过博物馆和图书馆的公共服务，通过包罗万象的适龄STEM方案，在学前教育和20岁以下学生、他们的教师和家长中开展教育和外联工作。我们将在圣安东尼奥的适当城市办事处以及费城一家主要电力零售供应商的总部，通过受邀的讲座和视频与更广泛的公民社区进行交流。对包容性劳动力发展的重视对推进S人力资源需求和复原力计划具有广泛的适用性和高度的影响力。为了实现SOR的时空预测，ALERT将通过融合几个学科的方法进行综合研究：a)高级数据分析(ADA)；b)社会、行为和经济科学(SBE)；以及c)智能电网基础(SGF)。项目活动将分别整合ADA和SGF数据以及物理电力系统模型，然后根据调查和实验数据设计SBE干预措施，以定义SOR模型并做出缓解决策，以降低停电风险。创新之处在于使用来自历史公用事业记录、天气记录、网格拓扑和植被数据的大数据对SOR进行基于物理约束和结构化学习的预测。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This NSF S&CC project aims to predict the State of Risk (SoR) of electricity outage occurrence and develop risk management and mitigation strategies to minimize the impact of outages. Currently, electric utilities are only able to reactively respond to outages. Consumer are left in a passive role of struggling to cope with the consequence without a preemptive option to manage the outage impacts. The project brings a transformative change that will allow utilities to predict outages, and then provide consumers with both individual and community mitigation measures. This will be achieved by increasing the S&CC awareness of how to deal with the outage impacts equitably and effectively. We will deploy advanced data analytics to train machine learning outage prediction algorithms using weather and historical outage data. The intellectual merits of the project include new risk prediction approaches, study of behavioral aspects of the outage prediction, and experiments that measure the effectiveness of predictive alert messages. Broader impacts include education and outreach efforts across PreK-20 students, their teachers and parents, through public services of museums and libraries by all-inclusive age-appropriate STEM programming. We will communicate with the broader community of citizens through the invited talks and videos at appropriate city offices in San Antonio, and at the headquarters of one of the major retail providers of electricity in Philadelphia. The emphasis on inclusive workforce development is broadly applicable and highly impactful to advance the S&CC human resource needs and resilience plans. To achieve the spatiotemporal prediction of the SoR, ALERT will perform integrative research by merging methodologies from several disciplines: a) Advanced Data Analytics (ADA); b) Social, Behavioral, and Economic Sciences (SBE); and c) Smart Grid Fundamentals (SGF). The project activity will integrate ADA and SGF data and physical power system models, respectively, and then design SBE interventions based on the survey and experimental data to define SoR models and make mitigation decisions to reduce outage risk. The innovation is in the physics-constrained and structured learning-based prediction of the SoR using big data from historical utility records, weather records, grid topology and vegetation data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.