STTR Phase I: Solar Irradiance Microforecasting

该项目开发短期太阳辐照度预测的更广泛的影响/商业潜力将是通过降低缓解云引起的太阳能光伏发电量波动的成本，支持非常大规模的太阳能光伏发电能力的部署。这一增加的SPV系统部署将减少温室气体产生的基本负荷量和峰值发电量，以及化石燃料发电机的耗水量。这样的预测将有助于制定事前缓解战略，而不是使用电力储存系统的事后缓解战略。先前的研究表明，与缓解后情景相比，这将使缓解前情景中使用的电力储存系统的输入/输出需求减少多达五倍。这些好处将在并网、微型并网和离网SPV系统中体现出来。这为引入智能传感器和控制系统以减少大量电力存储打开了商业机会。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作为消费者的所有人口结构，以及SPV模块制造商和系统供应商的SPV平衡。此小型企业技术转移(STTR)第一阶段项目解决了缓解云移动导致SPV系统输出波动的问题。第一阶段的研究目标是：(A)研制出一台能够提供足够的环绕太阳图像分辨能力的全天成像仪，以驱动基于神经网络的学习系统？这将需要开发用于全天空传感器的3D打印安装系统，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秒的太阳辐射预测，以及(C)部署Imager+软件原型，通过收集数据进行培训，以评估不同天气模式下多个地点的真实天空图像。神经网络。预计这项对原型性能的评估和分析将在后续阶段继续进行，以获得高度可信的结果。第一阶段研究的预期成果是：(一)图像采集系统的精细化生产？良好？(Ii)开发程序以调整神经网络学习系统以获得高置信度预测，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.