State of the Art for Solar and Wind Energy-Forecasting Methods for Sustainable Grid Integration

Abstract 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.