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

稳健、值得信赖且可解释的低碳电力和能源预测模型

• 批准号: 2889082
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889082
• 关键词: Robust; Trustworthy; Explainable; Predictive; Models

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 Data quality and in turn model accuracy may be compromised by operational noise [3] or out of date measurements, and data streams may also be offline altogether [4], but model predictions are still expected.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.

电力和能源系统正处于转型状态，这是由于发电来源及其最终用途在供暖和运输方面脱碳的需要所致[1]。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，并且很少或没有监控 [2]。因此，随着大规模数字化，这种情况已经开始发生变化，这将需要更先进的机器学习方法来进行预测或摘要生成，以根据数据做出运营和规划决策。然而，模型（例如，当代神经网络架构）变得越来越复杂，数据流的维度也越来越高——做出的决策需要合理化，而这种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，并且数据流也可能完全离线 [4]，但模型预测仍然是预期的。在非机器学习级别提供模型的可解释性和可解释性，为运营最终用户提供了快速、主动地询问模型输出的设施，最终使他们能够信任或放弃预测，以利于其业务。本博士将研究和开发这些工具将支持采用新的预测分析来支持低碳电力和能源系统 - 使 ScottishPower 的多个业务部门受益：零售（例如需求预测）、可再生能源（例如最大限度地减少弃电）、贸易（例如不平衡预测）和网络（例如支持拥塞管理）。该博士学位的主要目标是研究预测模型在操作上采用的障碍如何显现，并确定可以解决这些障碍的支持性决策支持工具。博士的预期产出包括：（1）由 ScottishPower 定义和提供的与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 业务提出的真实部署架构（例如 Azure 或 AWS）进行最终用户评估； (3) 支持 ScottishPower 的数字中心计划，确定模型部署策略，以使其更广泛的业务受益。支持这些目标的研究将集中在以下领域：1．如何在应用程序用例的背景下并以人类可读的术语而不是统计或数据科学术语来解释模型预测。还将通过应用自然语言处理模型来研究利用现有操作数据（例如维护报告 [2,5,6] 或标准文档）来生成这些解释的上下文的能力。2。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信息（例如置信度或与过去场景的关系），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，即使面对不完整的数据，也保证每次都能进行模型预测。这反过来又与目标 1 相关，因为估算的输入值将不可避免地改变模型输出。4。如何根据成本或收益而不是误差指标来解释模型的预测能力。