I-Corps: Residential Energy Management and Analytics

I-Corps项目更广泛的影响/商业潜力源于它不仅对消费者产生积极影响，而且还提高了整个电力能源市场的效率。将用户匹配到最佳电力计划的基本功能将带来更高的消费者满意度，并导致零售电力供应商（rep）提供更具竞争力的费率套餐。确定设备在一段时间内的实际使用情况，并向消费者提供可操作的信息，将使消费者能够在购买设备时做出明智的决定，并使制造商能够根据实际输入优化设计。该平台将能够解决公用事业公司面临的全系统问题，如高峰时期的需求激增，通过激励客户修改其使用模式以平滑负荷曲线等需求响应举措来应对。所提出的系统的每个功能都针对解决电力能源市场中特定的摩擦源，因此也具有商业潜力。因此，整体经济影响将对消费者、销售代表、公用事业公司和家电制造商产生影响，同时促进电力消费者之间更多的知识和参与。这个I-Corps项目探索了创建一个针对住宅用户的捆绑能源管理系统的价值。该系统使用基于机器学习的分析，分析客户的每日、每周、每月和季节性能源使用趋势，通过以下方式提供潜在的节约：(1)推荐与客户使用模式相匹配的最佳零售能源服务提供商计划；(2)激励客户实现与智能家居设备（包括智能恒温器）的完全集成；(3)识别和预测不同电器的用电量，并就其最佳使用和维护提供可操作的信息；以及(4)提供服务，让客户在切换计划的过程中导航。一款适用于iOS和Android平台的智能手机应用构成了该系统的用户界面。该项目的关键创新之处在于将机器学习工具和行为经济学思想开发和整合到住宅能源管理领域。该项目是建立在研究这些工具的设计、开发和验证的基础上的，例如预测住宅能源使用的时间，在每个设备的基础上分解使用情况，以及如何最好地激励用户参与能源使用行为，从而诱导高效的电网运行。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 derives from its promise to positively affect not only consumers, but also enhance efficiencies in the electric energy marketplace as a whole. The basic functionality of matching users to optimal electric plans will result in both higher consumer satisfaction, and result in Retail Electric Providers (REPs) offering more competitive rate packages. Identifying the actual usage profile of appliances over time and providing actionable information to consumers will enable consumers to take informed decisions on appliance purchases, as well as manufacturers to optimize designs with real-world inputs. The platform will enable the solution of system-wide problems faced by utility companies like peak period demand surges to be countered through demand response initiatives such as incentivizing customers to modify their usage patterns to smoothen the load curve. Each function of the proposed system is geared toward addressing a specific source of friction in the electric energy marketplace, and consequently also possesses commercial potential. Thus, the overall economic impact will be on consumers, REPs, utility companies and appliance manufactures, while promoting a greater knowledge and engagement among the electricity consumers.This I-Corps project explores the value of creating a bundled energy management system aimed at residential users. The system uses machine-learning-based analytics of the customer's daily, weekly, monthly, and seasonal energy usage trends to offer potential savings through (1) recommending the best retail energy service provider plan matching the customer's usage patterns, (2) incentivizing customers to enable full integration with smart home devices, including smart thermostats, (3) identifying and predicting the electricity consumption of different appliances and providing actionable information on their optimal usage and maintenance, and (4) services to allow customers to navigate through the process of switching plans. A smartphone app available for the iOS and Android platforms forms the customer interface to the system. The key novelties of this project lie in the development and integration of machine learning tools and behavioral economics ideas into the domain of residential energy management. The project is founded on research into the design, development and validation of such tools in contexts such as predicting residential energy usage over time, disaggregating usage on a per-appliance basis, and experimentation on how best to motivate users to engage in energy usage behavior that induces efficient grid operation.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.