The growing scale and heterogeneity of distributed energy systems has increased the complexity of their operational energy management. These systems must operate reliably under limited data and exogenous uncertainty while satisfying technical, social, and economic constraints. Classical optimization provides strong theoretical guarantees but often relies on deterministic models and point forecasts. Conversely, learning-based methods offer adaptability and scalability but may lack explicit safety handling and explainability, which are critical for deployment in safety- and flexibility-critical energy infrastructures.

Consequently, this thesis establishes an uncertainty-aware Energy Management System (EMS) architecture that preserves feasibility across planning and execution while enabling safety-constrained and human-centric intelligent control. Within a unified layered EMS structure, we address (i) exogenous uncertainty in generation and load, (ii) epistemic uncertainty arising from limited data, and (iii) exploration-related uncertainty during policy learning while supporting prosumer-in-the-loop operation. We first develop robust optimization methods with flexible and asymmetric uncertainty sets to obtain less conservative schedules while maintaining feasibility guarantees and improving economic performance. We further establish a safety-constrained reinforcement learning framework and benchmarking environment that enable systematic evaluation of performance–safety trade-offs. Finally, building on the EMS planning layer, we introduce an explainability and interaction layer in which schedules are complemented with solver-grounded explanations and what-if analyses, supporting human-in-the-loop flexibility decisions.

By jointly advancing robustness, data-efficient forecasting, safety-constrained learning, and explainable decision support, this thesis contributes an integrated and trustworthy decision-making paradigm for decentralized energy systems.

As decentralized energy resources proliferate, energy management system (EMS) design needs to handle stochastic uncertainty, hard operational constraints, and stakeholder-oriented decision support. These requirements are often addressed in isolation, resulting in a lack of integrated approaches that jointly provide constraint satisfaction, scalable operation, and transparent decision rationales.This thesis advances a unified EMS design perspective spanning three coupled dimensions: (i) robustness to multi-dimensional uncertainty, (ii) constraint-aware adaptive control, and (iii) explainable, deployable decision support for prosumer engagement. Specifically, the thesis develops an end-to-end EMS stack combining edge–cloud coordination, uncertainty-aware scheduling, safe reinforcement learning, and generative-AI explanations grounded in the underlying optimization model. Validated across microgrid, building, and energy-community case studies, the thesis establishes an empirically grounded pathway toward trustworthy, intelligent energy systems capable of accelerating the energy transition.

Accurate short-term load forecasting is essential for energy-efficient building operations, however, many buildings lack sufficient historical data to train reliable predictive models. This paper investigates the use of instance-based transfer learning to improve forecasting performance in data-scarce buildings by reusing models trained on similar, data-rich buildings. We cluster buildings of various types based on their energy consumption profiles using dynamic time warping and hierarchical clustering. Within each cluster, we fine-tune XGBoost and LSTM models pretrained on source buildings (with sufficient data) to forecast the consumption in target buildings (with limited data) with access to increasing percentage of their original limited data of 7, 14, 21, 28, ... , 245 days. Our evaluation compares the performance against models trained from scratch using the same limited data. Our results show that transfer learning significantly improves forecasting accuracy in highly data-constrained settings, reducing RMSE by up to 40%. However, as more training data becomes available, its benefits diminish, and baseline models eventually outperform transferred ones beyond an average threshold of 140–168 days.

Optimizing renewable energy usage in smart microgrids that contain photovoltaic production and battery storage is important due to the potential to reduce overall CO2 emissions and thus lead to more environmental friendly energy systems. However, due to the complex nature of energy management in smart grids and the volatile nature of energy production from PV systems, the problem is complex to solve. In this work we aim to optimize the energy in a microgrid comprising six houses using a digital twin based approach based on Deep Reinforcement Learning. We develop a Soft Actor-Critic (SAC) agent to address this intricate challenge, with the aim to simultaneously reduce emissions, maintain user comfort, while maximizing grid efficiency and resiliency to cope with spurious grid outages. We propose and evaluate different reward functions that guide the agent in finding its optimal policy. Furthermore, we discuss the implications of our results and outline potential future steps, envisioning ongoing refinement and advancements in our pursuit of optimal solutions for the complex interplay of severaal objectives in microgrid management.

Renewable Energy Communities (RECs), characterized by localized energy generation and consumption, are a key enabler for enhancing renewable energy utilization, cost-efficient planning and clean energy transition. However, optimizing RECs operations is challenging due to the complex interplay of different stakeholders with conflicting requirements. The complexity of managing such systems often leads to a lack of transparent and reliable decision-making, creating barriers for actors within the community. This paper explores the integration of Generative AI into Renewable Energy Communities (RECs) to enhance the transparency, explainability and accessibility of energy management systems (EMSs) that depend on solving optimization problems. We propose a novel framework, Chat-SGP, which uses generative AI to synthesize optimization modeling code to provide actionable, explainable insights for managing the REC operations. Our approach allows us to interact with the EMS through natural language queries, enhancing the system’s accessibility and user-friendliness. Our evaluation shows that using GPT-4 with in-context learning performs 96.72% accuracy on average in generating correct answers.

Climate change and its impact on global sustainability are critical challenges, demanding innovative solutions that combine cutting-edge technologies and scientific insights. Quantum machine learning (QML) has emerged as a promising paradigm that harnesses the power of quantum computing to address complex problems in various domains including climate change and sustainability. In this work, we survey existing literature that applies quantum machine learning to solve climate change and sustainability-related problems. We review promising QML methodologies that have the potential to accelerate decarbonization including energy systems, climate data forecasting, climate monitoring, and hazardous events predictions. We discuss the challenges and current limitations of quantum machine learning approaches and provide an overview of potential opportunities and future work to leverage QML-based methods in the important area of climate change research.

In this paper, we address the optimal operation of energy communities, under energy production and consumption uncertainties. In the nominal case, the operational problem is formulated as the maximization of the profit of the community over a given time horizon. Inspired by the regulation adopted in Italy since 2020, the profit includes an incentive for the self-consumption realized at the community level in each time period. In the presence of uncertainties, we use a robust formulation aiming at maximizing the worst profit achievable when energy production and consumption deviate from their nominal values. The model results in a robust scheduling policy of battery charging/discharging, guaranteeing feasible operation of the community in all scenarios of the uncertainty set. On the other hand, numerical results show that the nominal scheduling policy may suffer from a high constraint violation probability.

Energy management within microgrids under the presence of large number of renewables such as photovoltaics is complicated due to uncertainties involved. Randomness in energy production and consumption make both the prediction and optimality of exchanges challenging. In this paper, we evaluate the impact of uncertainties on optimality of different robust energy exchange strategies. To address the problem, we present AIROBE, a data-driven system that uses machine-learning-based predictions of energy supply and demand as input to calculate robust energy exchange schedules using a multiband robust optimization approach to protect from deviations. AIROBE allows the decision maker to tradeoff robustness with stability of the system and energy costs. Our evaluation shows, how AIROBE can deal effectively with asymmetric deviations and how better prediction methods can reduce both the operational cost while at the same time may lead to increased operational stability of the system.

Towards zero CO2 emissions society, large shares of renewable energy sources and storage systems are integrated into microgrids as part of the electrical grids for energy exchange aiming to effectively reduce the stress from the transmission grid. However, energy management within and across microgrids is complicated due to many uncertainties such as imprecise knowledge on energy production and demand, which makes energy optimization challenging. In this paper, we present an open architecture that uses machine learning algorithms at the edge to predict energy consumption and production for energy management in smart microgrids. Such predictions are aggregated across different prosumers at a centralized marketplace in the Cloud using Kafka Streams and OpenSource IoT platforms. Using pluggable optimization algorithms, different microgrids can implement different strategies for real-time optimal energy schedules. The proposed architecture is evaluated in terms of scalability and accuracy of predictions. Our heuristics can effectively optimize medium-sized microgrids.