STTR Phase I: Solar Irradiance Microforecasting

该项目开发短期太阳辐照度预测的更广泛影响/商业潜力将通过降低缓解云引起的SPV发电波动的成本来支持太阳能光伏（SPV）发电容量的大规模部署。这种增加的SPV系统部署将减少温室气体排放和耗水化石燃料发电机的基本负荷和峰值发电量。这样的预测将使得能够开发预缓解策略，而不是使用电存储系统的后缓解策略。先前的研究表明，与缓解后情景相比，这将导致在缓解前情景中使用的电存储系统的输入/输出要求减少多达五倍。这些好处将在并网、微并网和离网SPV系统中看到。这为引入智能传感器和控制系统以减少大量电力存储开辟了商业机会。该项目使用的技术领域包括传感器、3D打印、基于神经网络的学习系统、嵌入式计算机和云计算。将看到积极影响的市场部门包括所有人口统计数据，如消费者，SPV模块制造商和SPV系统供应商的平衡。这个小企业技术转让（STTR）第一阶段项目解决了减轻云运动引起的SPV系统输出波动的问题。第一阶段的研究目标是（a）原型的全天空成像仪，提供足够的环日图像的歧视，以驱动神经网络为基础的学习系统？这将需要开发一个3D-3D打印机‐用于全天空传感器的印刷安装系统，以及与云连接的本地单板计算机的接口，（B）开发和优化图像采集、合成、分析和预测算法，以提供15-&EP#8208; 500秒的太阳辐照度预报，以及（c）部署成像器+软件原型，以评估具有不同天气模式的多个位置的真实的实况天空图像，通过收集数据来？火车？神经网络。预计在后续阶段将继续对原型性能进行评价和分析，以获得高置信度的结果。第一阶段研究的预期结果是：（i）改进图像采集系统，以产生？好吗？（ii）发展调整神经网络学习系统的程序，以取得高置信度的预测，以及（iii）了解本地单板计算机的性能要求。

The broader impact/commercial potential of this project to develop short term Solar irradiance forecasting, will be to support very large deployment of Solar photovoltaic (SPV) generation capacity, by reducing the cost of mitigating cloud caused fluctuation of SPV electricity generation. This increased SPV system deployment will reduce the amount of base load and peaking generation from greenhouse gas causing, and water consuming fossil fuel generators. Such forecasting will enable development of pre-­‐ mitigation strategies instead of post mitigation using electrical storage systems. Prior studies indicate that this will result in the reduction by up to a factor of five, of the input/output requirements of the electrical storage system used in the pre-­‐mitigation scenario, compared to the post mitigation scenario. These benefits will be seen with grid-­‐tied, micro-­‐grid and off-­‐grid SPV systems. This opens commercial opportunities for introducing intelligent sensors and control systems to reduce bulk electrical storage. The technology areas used in this project include sensors, 3D printing, neural network based learning systems, embedded computers and cloud computing. The market sectors that will see a positive impact include all demographics as consumers, and manufacturers of SPV modules and SPV balance of system suppliers.This Small Business Technology Transfer (STTR) Phase I project addresses the problem of mitigating cloud movement induced fluctuation in the output of SPV systems. The research objectives of Phase I are (a) prototype a whole sky imager that provides sufficient circumsolar image discrimination, to drive a neural network based learning system ? this will require development of a 3D-­‐printed mounting system for a whole sky sensor, and interface to a cloud connected, local single board computer, (b) develop and optimize Image Acquisition, Compositing, Analysis, and Forecasting Algorithms to provide 15-­‐500 second forecasts of Solar irradiance, and (c) deploy imager + software prototypes to evaluate real live sky imagery in multiple locations with different weather patterns, by gathering data to ?train? the neural network. It is anticipated that this evaluation and analysis of prototype performance will continue in subsequent phases, to obtain high confidence results. The anticipated results of the research in Phase I are (i) refinement of the image capture system to produce ?good? imagery, (ii) development of procedures to tune neural network learning system towards obtaining high confidence forecasts, and (iii) understanding of performance requirements of local single board computer.