Energy-focused geospatial system using multi-sectoral data to deliver net zero

英国是世界上第一个承诺到2050年实现净零碳排放的主要经济体。要实现这一目标，就需要在若干部门进行改革。为此，交通和城市规划等部门已开始采取灵活、重点突出和数据驱动的方法来应对挑战。能源部门可以采取类似的方法，从能源部门和其他部门（如运输部门）获取数据，在地方一级提供有针对性的解决方案。我们打算使用地理空间数据，它结合了位置信息和属性信息，通常是时间信息，可以促进分布式能源范式的发展。分析稀疏或不完整数据的能力，例如智能电表、电动汽车（EV）的吸收、电动汽车充电类型、位置和用户概况，具有一系列难以用传统方法建模的影响。我们建议创建一个以能源为中心的地理空间系统，使用户能够可视化多变量空间和时间变化数据的叠加，建模和预测趋势和相关性，推断稀疏数据收集的各个区域，并模拟变化对系统的影响，如变化的供应，需求或基础设施。它将进一步允许模拟和测试不同的策略，例如，替代充电点的放置。我们将利用我们在贝叶斯优化和可扩展高斯过程（GP）模型方面的世界级专业知识来解决这些挑战。与现有的基于人工智能的工具相比，使用GPs将显著提高人工智能模型的准确性。这一办法的成功应用将使当地能源部门在规划和确定资源优先次序方面更加量化和有针对性。成功过渡到本地净零能源系统不仅需要跨部门数据、先进的地理空间和机器学习模型和技术，而且至关重要的是，与广泛的利益相关者进行广泛合作，以正确了解他们的需求。我们已经在这一进程中采取了一些初步步骤。例如，在电动汽车方面，我们正在与当地议会进行对话，他们目前正在寻求有关电动汽车充电器类型分析、电动汽车充电点优化推广以及对当地能源网络容量的影响方面的立即支持。

The UK is the first major world economy to pledge net zero carbon emissions by 2050\. The achievement of this goal will require transformations in a number of sectors. To this end, sectors such as transport and urban planning, have embarked upon a nimble, focused and data-driven approach in addressing their challenges. The energy sector can undertake a similar approach, taking data from the energy sector and additional sectors , such as transport, to deliver targeted solutions at the local level.We intend to use geospatial data, which combines location information with attribute and often temporal information, can contribute to the evolution of the distributed energy paradigm.The ability to analyze sparse or incomplete data with respect to, for example smart meters, electric vehicle (EV) uptake, EV charging type, locations and user profile, has a range of implications that are difficult to model with conventional approaches.We propose the creation of an energy-focused geospatial system that will enable the user to visualise overlays of multivariate spatially and temporally varying data, model and predict trends and correlations, infer across areas of sparse data collection, and model the effects of changes on the system such as varying supply, demand or infrastructure. It will further allow for the simulation and testing of different strategies, for example, alternative charging point placement.We will address these challenges using our world-class expertise in Bayesian optimization and scalable Gaussian Process (GP) models. Using GPs will significantly improve the accuracy of AI models, over existing AI based tools. The successful application of this approach will enable the local energy sector to be more quantitative and targeted in its planning and prioritising of resources.The successful transition to net zero local energy systems requires not only cross-sectoral data, advanced geospatial and machine learning models and techniques, but also, crucially extensive collaboration with a broad set of stakeholders to properly understand their needs.We have already taken some initial steps in this process. For example, with respect to the EVs, we are in dialogue with a local council who are currently looking for immediate support with respect to the analysis of EV charger types, optimised roll-out of EV charging points and the implications for the capacity of local energy networks.