## 2 Literature Review  
ML algorithms have emerged as powerful tools in many domains, from load forecasting [2] and enabling the analysis of meteorological data to forecasting the output of RESs such as solar farms and wind turbines [3; 4]. By utilizing advanced predictive models and ML techniques, these algorithms can significantly enhance the accuracy and reliability of renewable energy predictions, thereby optimizing the placement and operation of RES. Moreover, ML can detect patterns in meteorological data, further boosting the overall performance and efficiency of renewable energy systems. As a result, ML plays a critical role in the ongoing transition to a more sustainable and efficient energy system. Recent studies have underscored the value of integrating ML with deep learning models to improve renewable energy forecasting. For instance, [PERSON] and [PERSON] [5] proposed an ensemble approach that combines deep learning models such as long short-term memory (LSTM) and gated recurrent unit (GRU) with statistical models, achieving notable improvements in forecasting accuracy. Similarly, [PERSON] et al. [6] have addressed the challenges of integrating solar power into electrical systems by introducing the TESDL approach, which utilizes deep learning to predict weather patterns, thereby enhancing prediction accuracy by 27% compared with traditional methods. Research has also demonstrated the benefits of using convolutional neural networks (CNNs) to capture complex weather patterns. [PERSON] et al. [7] reduced forecast error rates by employing deep CNNs, showcasing the potential of these models to enhance solar farm forecasting accuracy. [PERSON] et al. [8] explored the application of ML and deep learning in estimating solar energy production, finding that models such as random forest (RF) and artificial neural networks (ANNs) offer superior precision for real-time and short-term forecasting compared with traditional models. Recent advancements in hybrid deep learning models, such as those found in the work of [PERSON] et al. [9] and [PERSON] et al. [10], highlight the potential of combining different deep learning architectures for improved prediction stability and accuracy. These approaches leverage the strengths of individual models, such as ANN and GRU, to create robust forecasting systems that can adapt to varying meteorological conditions. Beyond solar energy, wind energy forecasting has also benefited from the application of ML and deep learning techniques. [PERSON] et al., 2022 [11] have presented a deep learning model that merges the LSTM and convolutional neural network (CNN) architectures for the accurate prediction of solar power production. The model aimed to improve the operation of solar power systems by providing more precise estimates of power generation, which is essential for delivering prime energy to consumers and ensuring the reliability of power plants' operations. Using real-world data from Rabat, Morocco, the model was analyzed, and it was determined that it outperformed traditional machine learning and single DL models in terms of prediction accuracy, precision, and stability as measured by error metrics such as MAE, MAPE, and RMSE. [PERSON] et al., 2022 [12], introduced a general physical model of a photovoltaic (PV) system with ML predictions for one- to three-day forecasting. The operating point of the PV is captured by the model using measurements of power, wind speed, and air temperature, which are recombined into physics-informed metrics. This enabled the models to understand the physical correlations between the many characteristics, hence simplifying the training process. The case study of a PV system in Denmark was utilized to test the claims by assessing the following five alternative ML techniques: RF, support vector machines (SVM), CNN, LSTM, and a CNN-LSTM hybrid. The findings demonstrate that the top predictors constantly use the recommended physics-based characteristics, and that the forecasting horizon has no effect on the outcomes.