Abstract
This paper deals with cost-effective strategies for controlling indoor temperature using different technologies, including inverter-based and thermostatic control systems. In this regard, the indoor temperature control model incorporates instant heat loss coefficient, heat transfer capability, and heat energy conversion coefficient. The decision variable is the power setpoint of the energy conversion system, which can be operated in both cooling and heating modes. The thermal system coefficients have been estimated based on historical data for energy consumption, indoor, and outdoor temperatures of the case study presented, which are the minimal datasets required for the coefficient estimation. The inverter-based model benefits from the quasi-continuous power consumption model, while the thermostatic model has a hysteresis functionality resulting in discrete power consumption with several turn-on and turn-off modes, which can be controlled by changing the thresholds. The flexible thermal range resulted in 4.715% and 6.235% cost reductions for thermostat-based and inverter-driven heat pumps, respectively. © 2025 Elsevier B.V., All rights reserved.

Abstract
Load forecasting is an asset for sustainable building energy management, as accurate predictions enable efficient energy consumption and con- tribute to decarbonisation efforts. However, data-driven models are often limited by dataset length and quality. This study investigates the effectiveness of transfer learning (TL) for load forecasting in office buildings, with the aim of addressing data scarcity issues and improving forecasting accuracy. The case study consists in a group of eight virtual buildings (VB) located in Porto, Portugal. VB A2 serves as pre-trained base model to transfer knowledge to the remaining VBs, which are analysed in varying degrees of data availability. Our findings indicate that TL can significantly reduce training time, for up to 87%, while maintaining accuracy levels comparable to those of models trained with full dataset, and exhibiting superior performance when com- pared to models trained with scarce data, with average RMSE reduction of 42.76%.

Abstract
As energy systems evolve, protecting and empowering consumers is vital, enabling participation in decentralized electricity markets and maximizing benefits from energy resources. The integration of Distributed Energy Resources (DER) and Renewable Energy Sources (RES) fosters new energy communities, shifting from centralized systems to distributed structures. Consumers can sell excess production to neighbors, increasing income, reducing bills, and advancing energy transition goals. This paper proposes a community-based peer-to-peer (P2P) energy market model that reduces costs while respecting network constraints. Using the Alternating Direction Method of Multipliers (ADMM), ensures privacy enhancement, decentralization, and scalability. The Relaxed Branch Flow Model (RBFM) manages constraints, and Electric Vehicles (EVs) reduce imports and costs through strategic discharging. Tested on a 33-bus distribution network, the ADMM-based approach aligns closely with a centralized benchmark, showing minor discrepancies while maintaining system reliability. This model underscores the potential of decentralized markets for consumer-centric, flexible, and efficient energy trading.

Abstract
The increasing integration of renewable energy sources and decentralized generation requires demand-side flexibility to improve grid stability and balance local energy flows. Local Flexibility Markets (LFMs) provide a framework for optimizing flexibility transactions within energy communities. This paper presents a model for quantifying and pricing residential resources flexibility, enabling prosumers to submit bids in an LFM managed by the Community Manager. The methodology relies on a linear optimization problem, where a Home Energy Management System first determines optimal consumption baselines. Then an iterative sensitivity analysis estimates upward, and downward flexibility bands and sets offer prices per resource. The market operates as two asymmetric voluntary pools, clearing flexibility offers and requests. Results show that Battery Energy Storage Systems and Electric Vehicles provide the most effective flexibility, significantly reducing energy costs. Future research should improve pricing mechanisms and scalability to support LFM adoption in different residential settings.

Abstract
Renewable energy resources are crucial for addressing global economic and environmental challenges. Energy communities, which unite consumers to pursue shared energy goals, present a promising solution for reducing energy costs and enhancing sustainability. This study analyzes the optimal sizing and operation of energy community resources, formulating the problem as mixed-integer linear programming (MILP) models. Two tools are employed: one for daily operation, calculating energy setpoints for community assets such as battery energy storage systems (BESS) and electric vehicles (EVs), and another for sizing photovoltaic (PV) panels and BESS capacities to minimize costs while optimizing local energy trades. Due to the high computational demands of MILP, three optimization methods are compared: deterministic, hybrid particle swarm optimization (PSO), and evolutionary PSO (EPSO). The hybrid PSO method handles binary and continuous variables efficiently, while EPSO introduces diversity to improve solution quality in complex scenarios. These metaheuristic approaches address the trade-off between solution accuracy and computational effort, providing reliable tools for decision-makers in energy communities.

Abstract
Recently, there has been growing attention on the decarbonisation of maritime transport, particularly regarding the landside operations at ports. This has spurred the development and implementation of strategies and policies aimed at enhancing the environmental performance of port activities. Among these strategies, the electrification of port infrastructure is emerging as a potential industry standard for the future. However, there remains a significant gap in understanding the patterns of electricity consumption in ports and how to forecast them accurately. To address this gap, this paper provides a review of the current literature on electricity demand in ports, examining practical applications, methodologies employed, and their key limitations. The findings indicate that, despite its importance in supporting the electrification process, electricity demand forecasting in ports has not received substantial attention in either industry or academic research, and there are no clearly established policies to support port authorities in obtaining the necessary data. Finally, the paper outlines potential directions for future research and how port authorities or local government agencies can contribute to these efforts.