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
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
Electricity generation from wind energy is one of the main drivers of decarbonization in energy systems. However, installing wind farm facilities may have beneficial and harmful impacts on the habitat of living beings. This study reviews the literature based on economic analysis to identify the main externalities related to the installation of wind farms and the economic methodologies used to assess these externalities, filling an existent literature gap. A systematic literature review followed the Preferred Reporting Items on Systematic Reviews and Meta-analysis standards. A total of 33 studies were identified, most of them carried out in Europe. The studies cover 24 years, between 1998 and 2022. The externalities associated with wind electricity generation are classified into three categories: the impact on well-being, the impact of wind turbines, and the impacts of avoided externalities. Most studies (24 out of 33) determine economic values by stated preference methods through choice experiments, discrete choice experiments, and contingent valuation. Revealed preference methods were identified in 5 studies using hedonic pricing and travel cost techniques. The challenges and limitations of this analysis in terms of externalities identification and their assessment are also discussed, concluding that additional updated review studies are needed since the latest ones were published in 2016 and 2017. Moreover, it gives insights to policymakers and academics on a more complete approach they can use to evaluate the impacts of decarbonization, which, apart from the technological view, also considers and estimates the socio-economic and environmental perspectives.

Abstract
Energy communities (EC) and collective self-consumption (CSC) systems can make a significant contribution to reducing dependence on fossil fuels and energy costs. They create mechanisms for the active participation of end-consumers in the energy system by becoming self-producers of renewable electricity and adapting their energy behavior to the needs of the system. CSC also alleviates energy poverty by reducing the energy costs of vulnerable members. The CSC is still in its early stages, and regulation is being developed in several countries along with pilot projects to test different rules and incentives. This chapter discusses the most relevant common definitions of CSC and EC so far, as well as the main challenges in relation to energy sharing rules and the management of EC and CSC. © 2025 Elsevier B.V., All rights reserved.

Abstract
Portugal's electricity generation relies heavily on renewable sources, which accounted for over half of the country's production in recent years. The Portuguese government has set ambitious renewable energy targets for 2030. The R3EA project (https://r3ea.web.ua.pt/pt/projeto) evaluates the impact of new investments in solar and wind energy capacity in the Centro Region of Portugal, focusing on the costs and benefits of externalities. This study examines Portugal's electricity market outcomes in terms of prices, generation mix, and emissions for different wind and solar capacities, using the National Energy and Climate Plans (NECP) of Portugal and Spain as the reference scenario. The electricity markets of both countries are modelled together, reflecting the integrated Iberian market with significant interconnections. The NECP scenario results in lower market prices and emissions, but less significantly than scenarios with lower demand and higher renewable energy share. In all scenarios, increasing renewable energy sources drives market prices down from over 200/MWh in 2022 to under 100/MWh during peak hours in 2030. Demand is the main driver of emissions, as higher demand leads to more reliance on fossil fuel plants. Lower demand scenarios in 2030 show 20 % fewer CO2 emissions per TWh than higher demand ones.

Abstract
The Renewable Energy Communities (RECs) and self-consumption frameworks defined in Directive (EU) 2023/2413 and Directive (EU) 2024/1711 are currently being integrated into national regulations across EU member states, adapting legislation to incorporate these new entities. These regulations establish key principles for individual and collective self-consumption, outlining operational rules such as proximity constraints, electricity sharing mechanisms, surplus electricity management, grid tariffs, and various organizational aspects, including asset sizing, licensing, metering, data exchange, and role definitions. This study introduces a model tailored to optimize investment and energy-sharing decisions within RECs, enabling multiple members to invest in solar photovoltaic (PV) and wind generation assets. The model determines the optimal generation capacity each REC member should install for each technology and calculates the energy shared between members in each period, considering site-specific constraints on renewable deployment. A case study with a four-member REC is used to showcase the model's functionality, with simulation results underscoring the benefits of CSC over ISC.