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

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 Data quality and in turn model accuracy may be compromised by operational noise [3] or out of date measurements, and data streams may also be offline altogether [4], but model predictions are still expected.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