Ambue scaling retrofit with graph algorithms

** 使用图形机器学习改进房地产投资组合的可持续性分析 ** 在升级英国的房地产存量，然后以一定的速度和规模进行可持续改造方面存在许多关键障碍。一个是确保准确了解其现状，以便对每一处房产进行及时、具有成本效益和有针对性的改造。另一个是在确定这些项目后获得私人融资来执行这些项目。注册提供商（“RP”）已经依赖不可靠、游戏化且越来越难以访问的数据来做出这些投资决策，而全面的现场调查既耗时又具有破坏性。这些数据通常用于通过SAP等方法进行确定性能源评估，这些方法在实际性能差距方面存在已知的局限性，并且初始数据的质量和相关的不确定性没有实质性量化。在此类库存评估的基础上制定改造策略会因原料数据的质量而传播未量化的项目风险，并增加了桌面评估项目在进行详细设计和采购时需要大量返工的可能性。这种风险可以通过仔细的项目库存原型和验证性调查来减轻，但这是资源密集型的，昂贵的，并可能减缓项目交付。该项目是在增强的细节建模，客户的物业组合的子集，包括开发定制的改造模式书，加强物业测量和调查，以显著提高这些物业的基线和预测数据的准确性。然后，我们将使用图形算法（机器学习）来推断结果，以将改进的分析和预测应用于整个投资组合。这将提高对数据和分析的信心，从而对预测和改造改进和结果有更好的信心。

**Improving property portfolio sustainability analysis with graph machine learning**There are a number of key hurdles in upgrading the United Kingdom's property stock, and then delivering sustainable retrofit at pace and scale. One is the securing of an accurate picture of its existing state so as to make timely, cost-effective and targeted retrofits to each property. Another is accessing private finance to execute these projects once they have been identified.Registered Providers ("RP's") have become dependent on unreliable, gamed and increasingly-inaccessible data to make these investment decisions, whilst blanket on-site surveys are time-consuming and disruptive. Often, this data is used to produce deterministic energy assessments via methods such as SAP, which have known limitations in terms of real-world performance gaps, and through which the quality of initial data and associated uncertainty is not materially quantified.Building retrofit strategies on the back of such stock assessment propagates unquantified project risk due to the quality of feedstock data, and increases the likelihood that desktop assessed projects will need to be significantly reworked upon reaching detailed design and procurement. This risk can be mitigated through careful archetyping of the project stock and confirmatory surveys, but this is resource intensive, expensive and can slow down project delivery.The project is to model in enhanced detail, a sub-set of a client's property portfolio, including developing a customised retrofit pattern book, with enhanced property measurements and surveys to significantly improve the accuracy of the baseline and forecast data for these properties. We will then use Graph Algorithms (machine learning) to extrapolate the findings to apply the improved analysis and forecasts, to the whole portfolio. This will enable greater confidence in the data and analysis, to in turn have better confidence in the forecasts and retrofit improvements and outcomes.