I-Corps: Analyzing Customer Behavior for Energy Usage Moderation

这个I-Corps项目的更广泛的影响/商业潜力是有可能开发出一种优化算法，该算法可以确定能源消耗模式，从而可以调节和管理电力消耗。这项技术可以帮助基础设施规划，通过了解客户的能源行为来加强客户关系，并提供个性化或定制的反馈，以避免电路过载、电力质量问题和停电（停电），这些都是电力公用事业和微电网公司的主要问题。了解不同地区和人口统计数据的客户能源行为模式，以帮助在基础设施规划和部署方面做出明智的决策，这一需求尚未得到满足。除了在电力公司的应用之外，这项技术还可以应用于其他需要公用事业基础设施规划和消费者行为改变的应用中，比如天然气和水的分配。I-Corps的这个项目是基于一种专有的自动多参数优化和机器学习聚类算法的开发，该算法可以预测电网即将出现的负荷。这是通过分析消费者的整体和个性化能源消耗来完成的，这些变量反映了当地的地理位置、天气条件、季节条件（冬季与夏季）、时间和白天的电力消耗以及人口统计数据。利用多参数变量，开发了一种基于正则化的优化算法，该算法解耦了负载消耗中各个影响因素的贡献，以避免电路过载和电能质量问题。为了避免停电，多参数优化的输出与专有的机器学习聚类技术相结合，该技术分析不同人口统计数据中的负载消耗行为/模式，并将行为与人口密度相结合，以提高负载预测的准确性，以避免停电。多参数优化和聚类的结合有助于识别客户负载模式，因此可以帮助确定高度准确的负载预测，以实现负载平衡，从而消除一个地区的停电。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the potential development of an optimization algorithm that could identify energy consumption patterns that may enable moderation and management of electricity consumption. This technology could help in infrastructure planning, enhance customer relations by understanding the customer's energy behavior, and provide individualized or customized feedback to avoid circuit overload, power quality issues, and power outages (blackouts) which are major problems for the electricity utility and microgrid companies. There is an unmet need to understand customer energy behavior patterns in different geographies and demographics to help create informed decision-making on infrastructure planning and deployment. In addition to application in electricity utility companies, this technology can potentially be adapted for deployment in other applications that require utility infrastructure planning and consumer behavior change, such as gas and water distribution.This I-Corps project is based on the development of a proprietary automatic multi-parametric optimization and machine learning clustering algorithm that could forecast upcoming load on the electrical grid. This is done by analyzing overall and personalized energy consumption by consumers based on variables reflecting localized geography, weather conditions, seasonal conditions (winter vs summer), and time and day electricity consumption as well as demographics. Using multi-parametric variables, a regularization-based optimization algorithm is developed that decouples the contribution of each influencing factor in load consumption to avoid circuit overload and power quality issues. To avoid blackouts, the output of the multi-parametric optimization is combined with a proprietary machine-learning clustering technique that analyzes the load consumption behavior/ pattern in different demographics and maps behavior with the population density to improve load forecast for better accuracy to avoid power outages. The combination of multi-parametric optimization and clustering helps to identify customer load patterns and hence could help in identifying highly accurate load forecast for load balancing which will eliminate blackouts in a region.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.