Robust, Trustworthy and Explainable Predictive Models for Low Carbon Power and Energy

电力和能源系统正处于过渡状态，这是由于需要对发电来源以及其在供暖和运输方面的最终用途进行脱碳。这些变化是在传统配电基础设施的背景下发生的，这些基础设施不是为低碳技术（LCT）设计的，并且很少或根本没有监控[2]。因此，随着大规模数字化的发展，这一点已经开始改变，这将需要更先进的机器学习方法来进行预测或汇总，以便根据数据做出运营和规划决策。然而，模型（例如，现代神经网络架构）变得越来越复杂，数据流的维度也越来越高--需要对所做的决策进行合理化，而合理化需要基于对模型输出的理解。数据质量和模型准确性可能会受到操作噪声[3]或过时测量的影响，数据流也可能完全离线[4]，但模型预测仍然是预期的。在非ML级别提供模型的可解释性和可解释性，为操作最终用户提供了快速和主动询问模型输出的设施，最终允许他们信任或放弃预测，以利于他们的业务。这个博士将调查和开发工具，将支持采用新的预测分析，以支持低碳电力和能源系统-ScottishPower的多个业务部门都受益：零售（例如需求预测）、可再生能源（例如最小化限电）、交易（例如不平衡预测）和网络（例如支持拥塞管理）。博士的主要目的是研究预测模型的操作采用的障碍，并确定可以解决这些问题的支持决策支持工具。博士研究的预期成果包括：（1）与ScottishPower定义和提供的问题案例研究相关的新模型;（2）在ScottishPower的IT系统上部署与新模型相关的软件演示工具，旨在利用ScottishPower业务部门提出的真实的部署架构进行最终用户评估（例如Azure或AWS）;（3）支持ScottishPower的Digital Hub计划，以确定模型部署战略，从而使其更广泛的业务受益。支持这些目标的研究将在以下领域进行：1.模型预测如何在其应用用例的上下文中以人类可读的术语而不是统计或数据科学术语进行解释。利用现有运营数据（如维护报告[2，5，6]或标准文档）为这些解释生成上下文的能力也将通过自然语言处理模型的应用进行研究。模型预测的置信度如何传播到决策中，以及如果模型可以提供额外的信息（如置信水平或与过去情景的关系），这些决策可能会如何改变[7]。3.如何通过缺失数据插补方法保证每次都能进行模型预测，即使面对不完整的数据。这反过来又与目标1有关，因为估算的输入值将不可避免地改变模型的输出。模型预测能力如何从成本或收益而不是误差度量的角度来解释。

Power and energy systems are in a state of transition, invoked by the need to decarbonise both the sources of power generation, but also its end use in terms of heating and transportation [1]. These changes are taking place against a backdrop of legacy power distribution infrastructure which was not designed for Low Carbon Technologies (LCT) and featured little or no monitoring [2]. Consequently, this has started to change with large scale digitalisation which will require more advanced machine learning methods for prediction or summary generation to make operational and planning decisions based on data. However, models (e.g., contemporary Neural Network architectures) are becoming ever more complex and data streams are in higher dimensions - decisions made need to be justified, and that justification needs to be based on the understanding of model output. Data quality and in turn model accuracy may be compromised by operational noise [3] or out of date measurements, and data streams may also be offline altogether [4], but model predictions are still expected. Providing model explainability and interpretability at a non-ML level provides the operational end-user with the facility to interrogate model outputs rapidly and initiatively, ultimately permitting them to trust or discard the predictions to the benefit of their business.This PhD will investigate and develop tools that will support the adoption of new predictive analytics to support a low carbon power and energy system - with benefit across multiple business units in ScottishPower: retail (e.g. demand forecasting), renewables (e.g. minimising curtailment), trading (e.g. imbalance forecasting) and networks (e.g. supporting congestion management). The main aims of the PhD will be to look at how the barriers to operational adoption of predictive models manifest and identify the supporting decision support tools that can resolve these. The envisaged outputs from the PhD include: (1) new models associated with problem case-study to be defined and provided by ScottishPower; (2) the deployment of software demonstrator tools related to the new models on ScottishPower's IT system intended for end-user evaluation utilising the real deployment architecture proposed by ScottishPower's business (e.g. Azure or AWS); (3) support ScottishPower's Digital Hub initiative in determining a strategy for model deployment to the benefit of their wider business.The research underpinning these objectives will be in the following areas:1. How model predictions can be explained in the context of their application use case and in human readable terms rather than statistical or data science terms. The ability to harness existing operational data such as maintenance reports [2, 5, 6] or standards documents to generate context for these explanations will also be investigated through the application of Natural Language Processing models.2. How confidence in model predictions propagates through to decisions made and how these decisions might be altered if models could provide additional information such as confidence level or relation to past scenarios [7]. 3. How to guarantee model predictions are undertaken every time even in the face of incomplete data through missing data imputation methods. This will in turn, relate to objective 1 as imputed input values will inevitably alter model outputs.4. How model predictive capability can be interpreted in terms of cost or benefit rather than an error metric.