I-Corps: Deep Learning for Solar Energy Systems to Optimize Whole System Performance

I-Corps：太阳能系统深度学习以优化整个系统性能

• 批准号: 1953473
• 负责人: Janet Roveda
• 金额: $ 5万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-15 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953473&HistoricalAwards=false
• 关键词: Corps; Deep; Learning; Solar; Energy

The broader impact/commercial potential of this I-Corps project is to provide secure and high-performance solar energy to reduce pollution and energy cost, and help establish self-sustainable micogrids. As the proposed new deep learning-based controller taps into both hardware and software sub-segments for microgrids, the commercial opportunity is considerable. The proposed technology has the potential provide a large-scale microgrid system with affordable off-grid capability and establish a reliable microgrid system with cyberprotection. It offers a cost-effective energy solution to homeowners, schools, business owners, hospitals and clinics, military facilities and other organizations with significant power demands.This I-Corps project is based on the development of a new microgrid controller integrated with a deep neural network algorithm to optimize the system performance with both output and security. The model will help redesign photovoltaic (PV) filters, boost converters and micro-inverters by setting different parameters. Comparing with a simulation-based model, the deep neural network algorithm has a 15% margin on prediction accuracy and speeds up the response time from minute-level to millisecond-level. As a result, 4700 kWh of electricity can be saved annually for a 2000 sq ft house in a state with 80% sunny days. Due to the millions of parameters used in the algorithm, the new controller is able to support hundreds of users in a single grid. The core of the proposed project includes two deep neural networks (DNNs) using internet of things (IoT)-based sensors for the microgrid data processing. By leveraging the deep learning model and IoT sensors, the proposed system can monitor and detect abnormal usage pattern for the entire system in real time.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.

这个I-Corps项目的更广泛的影响/商业潜力是提供安全和高性能的太阳能，以减少污染和能源成本，并帮助建立自我可持续的微网。 随着提出的新的基于深度学习的控制器进入微电网的硬件和软件子部分，商业机会相当可观。所提出的技术有可能提供具有可负担的离网能力的大规模微电网系统，并建立具有网络保护的可靠微电网系统。它为家庭、学校、企业主、医院和诊所、军事设施以及其他有重大电力需求的组织提供了一个经济高效的能源解决方案。这个I-Corps项目基于开发一种新型微电网控制器，该控制器集成了深度神经网络算法，以优化系统的输出和安全性能。该模型将有助于重新设计光伏（PV）滤波器，升压转换器和微型逆变器通过设置不同的参数。与基于模拟的模型相比，深度神经网络算法的预测精度提高了15%，响应时间从分钟级加快到毫秒级。因此，在一个80%晴天的州，一个2000平方英尺的房子每年可以节省4700千瓦时的电力。由于算法中使用了数百万个参数，新的控制器能够在单个网格中支持数百个用户。拟议项目的核心包括两个深度神经网络（DNN），使用基于物联网（IoT）的传感器进行微电网数据处理。通过利用深度学习模型和物联网传感器，该系统可以真实的实时监控和检测整个系统的异常使用模式。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进行评估，被认为值得支持。