Abstract
According to the Portuguese Roadmap for Carbon Neutrality 2050 (RNC2050), Portugal aims to achieve carbon neutrality by 2050. To achieve this goal, it is necessary to decrease the consumption of primary energy from non-renewable sources and increase the consumption of energy from renewable sources. Portugal has a high potential for energy production through solar energy, and the country has a large solar potential that can be used. Thus, this work focuses on the study of the reliability of charging electric vehicles through photovoltaic energy, being sized electric vehicles charging stations, with different topologies, for domestic consumption, for different types of user profiles. At the same time this study evaluated technically and economically the proposed solutions. The research concluded that this type of technology proves to be a viable solution, especially if storage systems do not need to be implemented, as the limited useful lifetime of batteries substantially increases investment amortization times. Key words. Photovoltaic Systems, Electric Vehicle, Charging Stations, Energy Efficiency, Techno-Economic Study.

Abstract
In recent years, there has been a significant growth in the use of technologies that rely on natural resources (wind, solar, etc.) as primary sources of energy. The generation originating from renewable sources brings an increased need for adaptation in power electrical systems. Predicting the amount of energy produced by these technologies is a complex task due to the uncertainty associated with natural resources. This uncertainty hinders decision-making, both at the system level and for consumers themselves who are increasingly using this type of technology for self-consumption. This study focuses on classifying solar intensity using imbalanced data, which means that some of the data categories are more prevalent than others. Oversampling techniques are be employed to increase the amount of data, thereby allowing for balanced training data and improving the performance of prediction models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
Water is essential at various stages of winemaking, from irrigation in the vineyard to cleaning equipment and facilities, controlling fermentation temperatures, and diluting grape juice if necessary. Additionally, water is used for sanitation purposes to ensure the quality and safety of the final product. This article provides an overview of the existing knowledge regarding the use of water in wineries throughout the winemaking process, water consumption values, effluent treatment, efficient use of water measures, and water reuse. Different assessment methods, including Water Footprint (WF) and Life Cycle Assessment(LCA), provide varied insights into water use impacts, emphasizing the importance of standardized methodologies for accurate assessment and sustainable practices. This research showed that the characterization of the vinification processes of each type of wine is fundamental for further analysis on the environmental impact of winemaking regarding water use. It was also observed that WF is affected by factors like climate, irrigation needs, and cleaning procedures. Thus, efficient water management in all the stages of wine production is crucial to reduce the overall WF. Water efficiency measures may involve the modification of the production processes, reusing and recycling water and the implementation of cleaner production practices and technological innovations, such as automated fermentation systems that reduce water needs. Furthermore, waste management in wineries emphasizes the importance of sustainable practices and technological innovations to mitigate environmental impacts and enhance resource efficiency.

Abstract
Motivated by the imperative of sustainable practices, the wine industry is increasingly adopting renewable energy technologies to address environmental concerns and ensure its long-term viability amidst rising fossil fuel costs and greenhouse gas emissions. Hybrid renewable energy systems (HRES) have emerged as a solution to improve energy efficiency and mitigate the variability of renewable resources, allowing for better system load factors, greater stability of power supply, and optimized use of infrastructure. Therefore, this study aims to design a HRES that integrates solar and wind energy to sustainably fed an irrigation system in a favorable technical-economic context. This research presents a Mixed Integer Linear Programming (MILP) model that optimizes the profit generated by a grid-connected HRES over 20 years and obtains the optimal system sizing. The study focuses on the farm Quinta do Vallado, Portugal, and examines two distinct Cases. Over 20 years, the implementation of the hybrid system has resulted in savings of approximately 61%. © 2024 IEEE.

Abstract
The increasing complexity of integrated energy systems with the electric power grid requires innovative control solutions for efficient management of smart buildings and distributed energy resources. Accurately predicting weather conditions and electricity demand is crucial to make such informed decisions. Machine learning has emerged as a powerful solution to enhance prediction accuracy by harnessing advanced algorithms, but often requires complex parameterizations and ongoing model updates. The Lawrence Berkeley National Laboratory's Autonomous Forecast Framework (AFF) was developed to greatly simplify this process, providing reliable and accurate forecasts with minimal user interaction, by automatically selecting the best model out of a library of candidate models. This work expands on the AFF by not only selecting the best model, but assembling a blend of multiple models into a hybrid forecast model. The validation within this work has shown that this combination of models outperformed the selected best model of the AFF 31%, while providing greater resilience to individual model's forecast error. © 2024 IEEE.

Abstract
The number of electric vehicles is increasing progressively for various reasons, including economic and environmental factors. There has also been a technological development regarding both the operation and charging of these vehicles. Therefore, it is very important to reinforce the charging infrastructure, which can be optimised through the application of computational tools. There are several approaches that should be considered when trying to find the best location for electric vehicles charging stations. In the literature, different methods are described that can be applied to address this specific issue, including optimisation methods and decision-making techniques such as multicriteria analysis. One of the possible limitations of these methods is that they may not consider all perspectives of the various entities involved, potentially resulting in solutions that do not fully represent the optimal outcome; nevertheless, they provide invaluable information that can be applied in the development of integrative models and potentially more comprehensive ones. This article presents a research and discussion on the most commonly used decision models for this issue, considering optimisation models and multi-criteria decision-making strategies for the adequate planning of EV charging station installation, taking into account the different perspectives of the involved entities. © 2024 IEEE.