Abstract
As the world's population grows, there is a need to find new sources of energy that are more sustainable. Photovoltaic (PV) energy is one of the renewable energy sources (RES) expected to have the greatest margin for growth in the near future. Given their intermittency, RES bring uncertainty and instability to the management of the power system, therefore it is essential to predict their behavior for different time frames. This paper aims to find the most effective forecasting method for PV energy production that could be applied to different time frames. PV energy production is directly dependent on solar radiation and temperature. Several forecasting approaches are proposed in this paper. A multiple linear regression (MLR) model is proposed to predict the monthly energy production based on the climatic parameters of the previous year. Different approaches are proposed based on first predicting the temperature and radiation and then applying the PV mathematical models to predict the produced energy. Three methods are proposed to predict the climatic parameters: using the average values, the additive decomposition, or the Holt-Winters method. Comparing the errors of the four proposed forecasting methods, the best model is the Holt-Winters, which presents smaller errors for radiation, temperature, and produced energy. This method is close to additive decomposition. © 2023 IEEE.

Abstract
Renewable energy communities (REC) are an increasingly interesting solution for all energy market stakeholders. In RECs consumers and producers come together to form energy cooperatives with a strong incorporation of renewables in order to make the market and energy trading more advantageous for both sides. This growing trend has been followed by several studies aimed at understanding which are the best models for energy sharing within the community. This paper proposes different models of energy sharing within the community and evaluates their efficiency. Energy sharing can be based on constant coefficients or variable coefficients based on the net consumption of the self-consumers. This study proposes a new methodology based on a hybrid model. The results show the advantages and challenges of the individual energy-sharing models, showing that up to 41% of the energy imports from the grid can be reduced. © 2023 IEEE.

Abstract
In recent years, as a result of population growth and the strong demand for energy resources, there has been an increase in greenhouse gas emissions. Thus, it is necessary to find solutions to reduce these emissions. This will make the use of electric vehicles (EV) more attractive and reduce the high dependency on internal combustion vehicles. However, the integration of electric vehicles will pose some challenges. For example, it will be necessary to increase the number of fast electric vehicle charging stations (FEVCS) to make electric mobility more attractive. Due to the high power levels involved in these systems, there are voltage drops that affect the voltage profile of some nodes of the distribution networks. This paper presents a methodology based on a genetic algorithm (GA) that is used to find the optimal location of charging stations that cause the minimum impact on the grid voltage profile. Two case studies are considered to evaluate the behavior of the distribution grid with different numbers of EV charging stations connected. From the results obtained, it can be concluded that the GA provides an efficient way to find the best charging station locations, ensuring that the grid voltage profile is within the regulatory limits and that the value of losses is minimized.

Abstract
Several sectors, such as agriculture and renewable energy systems, rely heavily on weather variables that are characterized by intermittent patterns. Many studies use regression and deep learning methods for weather forecasting to deal with this variability. This research employs regression models to estimate missing historical data and three different time horizons, incorporating long short-term memory (LSTM) to forecast short- to medium-term weather conditions at Quinta de Santa B & aacute;rbara in the Douro region. Additionally, a genetic algorithm (GA) is used to optimize the LSTM hyperparameters. The results obtained show that the proposed optimized LSTM effectively reduced the evaluation metrics across different time horizons. The obtained results underscore the importance of accurate weather forecasting in making important decisions in various sectors.

Abstract
Socioeconomic growth and population increase are driving a constant global demand for energy. Renewable energy is emerging as a leading solution to minimise the use of fossil fuels. However, renewable resources are characterised by significant intermittency and unpredictability, which impact their energy production and integration into the power grid. Forecasting models are increasingly being developed to address these challenges and have become crucial as renewable energy sources are integrated in energy systems. In this paper, a comparative analysis of forecasting methods for renewable energy production is developed, focusing on photovoltaic and wind power. A review of state-of-the-art techniques is conducted to synthesise and categorise different forecasting models, taking into account climatic variables, optimisation algorithms, pre-processing techniques, and various forecasting horizons. By integrating diverse techniques such as optimisation algorithms and pre-processing methods and carefully selecting the forecast horizon, it is possible to highlight the accuracy and stability of forecasts. Overall, the ongoing development and refinement of forecasting methods are crucial to achieve a sustainable and reliable energy future.

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%.