State-of-the-art solar PV generation forecast for individual PV systems

针对单个光伏系统的最先进的太阳能光伏发电预测

• 批准号: 10031365
• 金额: $ 38.12万
• 依托单位: OPEN CLIMATE FIX LIMITED
• 依托单位国家: 英国
• 项目类别: Small Business Research Initiative
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10031365
• 关键词: State; art; solar; PV; generation

With SBRI support Open Climate Fix (OCF) will develop an open-source state-of-the-art solar PV generation forecast for individual PV systems. The forecast will help our first users to optimise and better manage their solar energy, storage or distributed energy assets, which reduces CO2 emissions and costs for all energy users. Additionally, with the expansion of solar, it could be used at the individual household level in the near future.As shown in our market research, there is a clear need for more accurate PV solar generation forecasts. Most of the forecasts currently available rely solely on numerical weather predictions (NWPs), do not make use of all the data available and struggle to forecast short time horizons ahead, which are important when making operational decisions on assets.How is our forecast different? Our near-term forecasting solutions incorporate cutting-edge machine learning (ML) research combining a range of available data sources such as five-minutely satellite imagery, NWPs, and PV data from thousands of individual PV systems. Satellite data provides weather information that is only five minutes old as opposed to hours, as in the case of NWPs. Currently, meteorologists analyse satellite imagery, but most small companies do not have the resources to hire these skills. Satellite imagery has not yet been widely used in traditional solar PV forecasting methodologies, thus we are particularly interested in incorporating it to improve our models.OCF is a non-profit product lab, fully focused on reducing CO2 emissions. Every part of the organisation is designed to maximise climate impact, such as our open and collaborative approach, our rapid prototyping, and our attention on finding scalable & practical solutions.OCF applies open-source principles stewarded by the Open Source Initiative (OSI) and an MIT licence to all our solutions. We will apply the same open-source approach to the SBRI project. We will make all the code openly available for other researchers to contribute to and learn from via GitHub. We are working on implementing current literature at the cutting edge of machine learning from Google Research and DeepMind. All our work to date is available on [GitHub][0]. With a team with deep experience in leading technology firms and machine learning, we bring the latest collaborative approaches and combine them with many years' experience in the energy industry. Through this mission-driven approach, we have already attracted significant interest and established a community of contributors around our work.[0]: https://github.com/openclimatefix

在SBRI的支持下，Open Climate Fix（OCF）将为单个光伏系统开发一个开源的最先进的太阳能光伏发电预测。该预测将帮助我们的第一批用户优化和更好地管理他们的太阳能，存储或分布式能源资产，从而减少二氧化碳排放和所有能源用户的成本。此外，随着太阳能的扩展，它可以在不久的将来在个人家庭层面上使用。正如我们的市场研究所示，显然需要更准确的光伏太阳能发电预测。目前可用的大多数预报仅依赖于数值天气预报（NWP），没有利用所有可用的数据，并且很难预测未来的短时间范围，这在做出资产运营决策时非常重要。我们的预报有何不同？我们的短期预测解决方案结合了尖端的机器学习（ML）研究，结合了一系列可用的数据源，如五分钟卫星图像，NWP和来自数千个光伏系统的光伏数据。卫星数据提供的天气信息只有五分钟，而不是像NWP那样的几个小时。目前，气象学家分析卫星图像，但大多数小公司没有资源雇用这些技能。卫星图像还没有被广泛应用于传统的太阳能光伏预测方法中，因此我们特别有兴趣将其纳入我们的模型中。OCF是一个非营利性的产品实验室，完全专注于减少二氧化碳排放。OCF的每一部分都旨在最大限度地提高气候影响，例如我们的开放和协作方法，我们的快速原型设计，以及我们对寻找可扩展和实用解决方案的关注。OCF将开源计划（OSI）和麻省理工学院许可证管理的开源原则应用于我们所有的解决方案。我们将把同样的开源方法应用于SBRI项目。我们将公开所有代码，供其他研究人员通过GitHub贡献和学习。我们正致力于在Google Research和DeepMind的机器学习前沿应用现有文献。到目前为止，我们所有的工作都可以在[GitHub][0]上找到。凭借在领先技术公司和机器学习领域拥有丰富经验的团队，我们带来了最新的协作方法，并将其与能源行业的多年经验联合收割机相结合。通过这种使命驱动的方法，我们已经吸引了大量的兴趣，并围绕我们的工作建立了一个贡献者社区。[0]：https://github.com/openclimatefix网站