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 Data quality and in turn model accuracy may be compromised by operational noise [3] or out of date measurements, and data streams may also be offline altogether [4], but model predictions are still expected.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

电力和能源系统处于过渡状态，这是由于需要脱碳的发电来源，但在加热和运输方面的最终用途也是如此[1]。这些变化是在传统功率分配基础架构的背景下进行的，该基础架构不是为低碳技术（LCT）设计的，几乎没有或没有监测[2]。因此，这已经开始随着大规模数字化而改变，这将需要更先进的机器学习方法来预测或摘要生成，以根据数据做出操作和计划决策。但是，模型（例如，当代神经网络体系结构）变得越来越复杂，数据流处于较高的方面 - 做出的决策需要是合理的，而合理性必须基于对模型输出的理解。数据质量和模型准确性可能会因操作噪声[3]或过期测量值而损害，并且数据流也可能完全离线[4]，但仍然可以预期模型预测。 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预测），可再生能源（例如，减少限制），交易（例如不平衡预测）和网络（例如，支持拥堵管理）。博士学位的主要目的是研究如何表现出运营采用预测模型的障碍，并确定可以解决这些模型的支持决策支持工具。来自博士学位的预期输出包括：（1）与苏格兰力量定义和提供的问题案例研究相关的新模型； （2）使用ScottishPower业务提出的实际部署体系结构（例如Azure或AWS）提出的实际部署体系结构（例如Azure或AWS），用于苏格兰Powers IT系统上的新模型的软件演示器工具的部署； （3）支持Scottish Powers的数字枢纽倡议，以确定模型部署的策略以使其更广泛的业务受益。这些目标的基础将在以下领域：1。如何在其应用程序用例和人类可读术语而不是统计或数据科学术语的背景下解释模型预测。还将通过应用自然语言处理模型来研究这些解释的现有操作数据的能力，例如维护报告[2，5，6]或标准文档为这些解释生成上下文的能力。2。如果模型可以提供其他信息，例如置信度或与过去的情况关系，则对模型预测的信心如何传播到做出的决策以及如何改变这些决策[7]。 3.即使面对不完整的数据，如何通过缺少数据插补方法来确保模型预测。反过来，这将与目标1相关，因为估算的输入值不可避免地会更改模型输出。4。模型预测能力如何以成本或收益而不是错误度量来解释。