I-Corps: Residential Energy Management and Analytics

这个I-Corps项目更广泛的影响/商业潜力来自于它不仅对消费者产生积极影响，而且还提高了整个电力市场的效率。将用户与最佳电力计划相匹配的基本功能将导致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。 随着时间的推移，识别设备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，以及制造商利用现实世界的输入优化设计。 该平台将使公用事业公司面临的系统范围的问题，如高峰期需求激增的解决方案，通过需求响应计划，如激励客户修改其使用模式，以平滑负荷曲线。 所提出的系统的每个功能都是针对解决电能市场中的特定摩擦源，因此也具有商业潜力。 因此，整体的经济影响将是对消费者，代表，公用事业公司和电器制造商，同时促进更多的知识和参与电力消费者。这个I-Corps项目探讨了创建一个捆绑的能源管理系统，针对住宅用户的价值。 该系统使用基于机器学习的客户每日、每周、每月和季节性能源使用趋势分析，通过（1）推荐与客户使用模式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）的全面集成，（3）识别及预测不同电器的耗电量，并提供有关电器的最佳使用及维修的可行资料;及（4）提供服务，让顾客可在转换计划的过程中进行浏览。可用于iOS和Android平台的智能手机应用程序形成了系统的客户界面。 该项目的关键创新在于将机器学习工具和行为经济学思想开发和集成到住宅能源管理领域。 该项目的基础是对这些工具的设计、开发和验证的研究，例如预测一段时间内的住宅能源使用情况，按电器分列使用情况，该奖项反映了NSF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。

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.