Background: Forecasting renewable energy generation is crucial for improving the efficiency and reliability of power systems that integrate wind, solar, and other renewable sources. These energy sources are inherently variable, depending on changing weather patterns, which makes accurate forecasting a complex task. The ability to predict renewable energy production with high accuracy can help grid operators optimize energy storage, reduce the reliance on fossil fuels, and ensure grid stability. Forecasting methods vary significantly, ranging from physical models that rely on weather data, to statistical models and advanced machine learning techniques. Each method has its own strengths and limitations, and the choice of approach often depends on the specific requirements, such as time horizon, data availability, and computational resources. Methods: Various forecasting techniques are employed to predict renewable energy generation, including physical, statistical, machine learning (ML), and hybrid models. Physical models, such as Numerical Weather Prediction (NWP), are commonly used for short-term forecasting based on weather data. Statistical methods, such as ARIMA and SARIMA, are often applied to time series data in stable environments. Machine learning models, including neural networks (ANN, LSTM) and decision trees (XGBoost, LightGBM), are employed to model complex, non-linear relationships in large datasets. Hybrid models, which combine physical and machine learning methods, have also emerged to enhance predictive accuracy and adaptability. Results: The reviewed studies highlight the strengths and limitations of each approach. Physical models, such as NWP, perform well in short-term predictions but are limited in long-term forecasting and require high-quality input data. Statistical methods, including ARIMA and SARIMA, are effective in stable environments but struggle with non-linear patterns and abrupt changes. Machine learning models, especially neural networks (ANN, LSTM), show great promise in capturing non-linear relationships and long-term trends in renewable energy generation, but they require large amounts of data and are computationally intensive. Hybrid models have demonstrated superior accuracy by combining the strengths of physical and machine learning approaches. Conclusions: The integration of hybrid models offers a promising direction for improving renewable energy forecasting. While these models provide high accuracy and adaptability, they also come with significant challenges, such as high computational costs and the complexity of model integration. Future research should focus on optimizing hybrid methods for balancing accuracy and computational efficiency, exploring lightweight machine learning techniques, and enhancing explainable AI (XAI) approaches to increase model transparency. Additionally, the development of regionally adaptable models and standardized datasets will improve model reproducibility and accuracy across different geographic locations and climatic conditions.

This paper presents a methodology for energy management in a smart microgrid based on the efficiency of dispatchable generation sources and storage systems, with three different aims: elimination of power peaks; optimisation of the operation and performance of the microgrid; and reduction of energy consumption from the distribution network. The methodology is based on four steps: identification of elements of the microgrid, monitoring of the elements, characterization of the efficiency of the elements, and finally, microgrid energy management. A specific use case is shown at CEDER-CIEMAT (Centro para el Desarrollo de las Energías Renovables—Centro de Investi-gaciones Energéticas, Medioambientales y Tecnológicas), where consumption has been reduced during high tariff periods and power peaks have been eliminated, allowing an annual reduction of more than 25,000 kWh per year, which is equal to savings of more than 8500 €. It also allows the power contracted from the distribution company by CEDER (135 kW) not to be exceeded, which avoids penalties in the electricity bill.

As the utilization of renewable energy continues to grow, microgrids have played a vital role in their generation. Batteries have emerged as the most commonly utilized storage system to effectively store this energy. This paper proposes a novel approach to manage energy consumption at the Centre for the Development of Renewable Energy (CEDER) by leveraging both lithium-ion and lead-acid batteries. This computer-based management system controls the flow of energy that can be charged or discharged in 15-minute intervals. By optimizing the use of both battery types, the energy consumption of CEDER has been reduced by an estimated 90 to 200 kWh, depending on the specific case study.