SCC-PG: Advanced Learning for Energy Risk Tracking (ALERT)

能源供应是智能互联城市的中坚力量(S和CC)。它将不同的能源利益相关者(供应商、消费者和服务)联系在一起，跨越不同的社会、经济和文化层面，以及与住宅、商业或工业环境相对应的群体之间。最近的统计数据表明，尽管公用事业行业采取了各种措施来维护能源供应的可靠性和安全性，但全球范围内的重大停电事件仍在增加。停电影响到居民、学生、私营和公共部门员工、小型和大型企业以及关键的城市服务，如警察、消防员和急救人员。根本的问题是，数据科学和工程，与社会科学和技术相结合，是否有助于减少损失和相关的社会影响。该项目假定，大数据时空分析和物理模型的结合将使我们能够实现预测性停机风险能力，以解决目前不可用的缓解方案。能源风险跟踪高级学习(ALERT)方法将侧重于使用公用事业公司的历史数据(停电、智能电表数据等)以及与天气有关的政府和私人来源(雷达、卫星、ASOS、NLDN和NDFD、Vaisala)以及拓扑和植被数据来预测停电和资产故障风险。此类风险预测将与参与的公用事业公司及其客户共享，以动员缓解措施，这需要强有力的社会研究方面来更好地了解客户行为。这些措施可包括：(A)设备维修/更换和支线切换行动；(B)重新安置不稳定的人口，为学校、企业和基本城市服务恢复当地电力供应；(C)安排消防员和紧急服务人员疏散高危人口；(D)派遣警察部队，防止抢劫空出的房屋和企业。社区将通过让社区利益攸关方参与创建和共享停电预测风险图来实现将电力供应中断和不良环境影响降至最低的目标，以便采取单独和协作的缓解行动。主要目标是建立研究能力，开发一种预测停电风险的方法，重点是S和CC方面。为了加强社区的参与，并整合研究团队和德克萨斯州大都市地区不同利益相关者群体的想法，将举行不同类型的会议。这些多学科会议将侧重于确定警报目标：数据收集和行为试点的需要、对整合平台的要求、为传播警报信息和处理假阳性和假阴性情况而实施的用户门户的后勤工作，以及在今后的步骤中收集小组调查数据。该项目将确定研究差距和解决这些差距的方法，数据要求，并将为每个合作伙伴组织和每个潜在的试点参与者建立一个全面的联系网络。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。

The energy supply is the backbone of Smart and Connected Communities (S&CC). It ties together various energy stakeholders (providers, consumers, and services) across different social, economic and cultural layers, and among groups corresponding to the residential, commercial, or industrial settings. Recent statistics indicate that despite all the measures taken by the utility industry to maintain the reliability and security of the energy supply, the number of major blackouts throughout the world is increasing. Loss of electricity affects residents, students, private and public sector employees, small and large businesses, and critical city services, such as, police, firefighters, and first responders. The fundamental question is whether the data sciences and engineering, combined with social sciences and technology can help reduce the losses and related societal impacts. This project postulates that the combination of the Big Data spatiotemporal analytics and physical models will allow us to achieve predictive outage risk capabilities to address the mitigation options not presently available. The Advance Learning for Energy Risk Tracking (ALERT) approach will focus on predicting the outages and asset failure risks using historical data from utilities (outage, smart meter data, etc.), along with additional data from weather-related government and private sources (radar, satellite, ASOS, NLDN and NDFD, Vaisala), as well as topology and vegetation data. Such risk predictions will be shared with participating utility companies, and with their customers to mobilize mitigation measures, which needs a strong social study aspect to better understand customer behavior. Those measures may include: a) equipment repair/replacement and feeder switching actions, b) relocation of the volatile population, and local power back up for schools, businesses and essential city services, c) scheduling firefighter and emergency services for evacuation of at-risk populations, and d) dispatching police forces to prevent looting of vacated houses and businesses. The goal of the communities to minimize the risk of electricity supply failure and undesirable environmental impacts will be achieved by engaging community stakeholders in creating and sharing outage prediction risk maps to allow for individual and collaborative mitigation actions. The main objective is to build research capability to develop a methodology for predicting the risk of electricity outages, which emphasizes the S&CC aspects. To strengthen the community engagement, and to consolidate thinking in the research team and among the various stakeholder groups in metropolitan areas in Texas, different types of meetings will be held.. These multidisciplinary meetings will focus on defining ALERT goals: the need for data collection and behavioral pilots, the requirements for the integration platform, the logistics of the user portal implementations for the dissemination of ALERT messages and handling of false positive and false negative scenarios, as well as collection of panel polling data in future steps. The project will identify the research gaps and means of addressing them, the data requirements, and will develop a comprehensive network of contacts for each partner organization, and each prospective pilot participant.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.