energy management teams

Abstract: The rising cost and demand for energy, together with environmental sustainability goals, create major challenges for energy management. Energy Consumption Forecasting (ECF) supports planning by predicting future consumption, but Machine Learning (ML) models for ECF often depend on expert-driven Feature Engineering (FE). This thesis addresses that dependence through three contributions. First, it establishes and evaluates a comprehensive FE pipeline for ECF and investigates domain-specific features. Second, it introduces AutoEnergy, a domain-tailored automated FE algorithm that generates interpretable features from timestamps and lagged consumption and integrates with AutoML for end-to-end ECF modelling. Across eighteen real-world energy datasets spanning residential, commercial, industrial, renewable, and grid domains, AutoEnergy reduces forecasting error by 19.52%-84.72% relative to baseline AutoML and established automated FE methods, while running 1.31-4.41 times faster, with gains varying by dataset. Third, AutoEnergy is integrated with Decision-Focused Learning (DFL) for a Battery Energy Storage System problem, jointly forecasting electricity prices and demand while optimising charging and discharging decisions. On a real-world UK property dataset, this approach reduces operating costs by 22.9%-56.5% compared with the same DFL models without automated FE. Overall, the results show that domain-specific automated FE can reduce reliance on manual feature design, improve forecasting accuracy, and translate predictive gains into measurable operational benefits in energy management.

Abstract: Neurosymbolic research typically assumes a pre-existing symbolic specification, leaving the upstream challenge of acquiring and formalizing requirements and constraints largely unaddressed. We present an architecture that fills this gap by using an OWL configuration ontology to mediate between neural constraint sources and downstream consumers. In this framework, LLM assistants elicit soft stakeholder preferences, while hardware specifications define hard physical and engineering limits. The ontology unifies these heterogeneous inputs, leverages description logic to identify unsatisfiability, and generates symbolic explanations that enable LLMs to interactively renegotiate terms with users. Any remaining conflicts are resolved downstream via priority-based relaxation. We illustrate our approach on a microgrid use case from the FLEXI project and argue its generalizability to multi-stakeholder domains where constraint acquisition is distributed across human and automated sources of unequal authority.

Abstract: Electrified chemical industries with operational flexibility can reduce operating costs by shifting production and distribution decisions in response to time-varying electricity prices. However, chemical plants operate within process networks where coordinated demand response can exploit flexibility across multiple stakeholders. Centralized coordination requires access to stakeholders' local scheduling models and proprietary operational data, often incompatible with data-privacy requirements. Distributed optimization with an independent central coordinator (ICC) avoids direct model sharing, but iterative exchange of coupling variables can still reveal private model parameters. We propose a privacy-preserving distributed coordination framework for coordinated demand response in industrial networks. The framework integrates secure aggregation with an ICC-based alternating direction method of multipliers (ADMM) algorithm, so plant-level messages are numerically masked and become useful to the ICC only after aggregation. We test the framework on a multi-plant industrial gas network in which three air-separation units jointly schedule production and shipments to shared customer regions. To support stable participation, we incorporate a two-phase revenue-sharing mechanism that reallocates savings so every plant improves relative to its decentralized status quo. In a 31-day rolling-horizon simulation with synthetic data representing heterogeneous electricity prices and demand, the coordinated policy reduces total network cost by 19.77% relative to decentralized operation and achieves a full-month cost within 3.08% of a centralized social-welfare-maximization benchmark. We further quantify a conservative worst-case collusion mode, showing how unmasked iterates and auxiliary information can expose private objective parameters.