## 1 Introduction
### Impact of Environmental Conditions on Renewable Energy Forecasting in the Literature  
Environmental conditions play a pivotal role in renewable energy production. Factors such as temperature, humidity, solar irradiance, and wind patterns introduce variability that complicate accurate forecasting. Studies emphasize the necessity of incorporating real-time environmental data into ML models to enhance prediction accuracy. For instance, the research demonstrates that integrating weather forecasts with ML algorithms significantly improves the reliability of solar energy predictions and highlights the effectiveness of these models in improving prediction accuracy [52]. Research on case studies on renewable energy predictions are worldwide:  
Mexico by 2050: A study by the International Renewable Energy Agency (IRENA) outlines Mexico's potential to achieve a 100% renewable energy system by 2050. The research emphasizes the importance of integrating solar and wind energy, considering the country's diverse climatic conditions. Advanced forecasting models are recommended to manage variability and ensure grid stability [53]. Latin America and the Caribbean by 2050: IRENA's report on the region highlights the feasibility of a renewable energy transition, focusing on the integration of hydropower, solar, and wind resources. The study discusses the need for robust forecasting tools to address environmental challenges such as seasonal variability and extreme weather events [53]. Spain: Research published in the journal Renewable Energy examines Spain's path to a 100% renewable energy system. The study utilizes machine learning models to predict energy production from solar and wind sources, accounting for environmental factors like temperature and wind speed. The findings suggest that accurate forecasting is crucial for effective energy management and policy planning [54]. Japan: A study in the journal Energy explores Japan's renewable energy prospects, emphasizing the role of advanced forecasting models in managing the country's unique environmental conditions, including typhoons and seasonal changes. The research highlights the integration of machine learning techniques to improve the accuracy of energy production predictions [55]. Island Case Studies (Canary and Galapagos Islands): Research on the Canary Islands investigates the challenges of achieving 100% renewable energy in isolated systems. The study emphasizes the importance of accurate forecasting models to manage the variability of renewable sources and ensure energy security [56]. Similarly, a study on the Galapagos Islands discusses the integration of renewable energy sources and the necessity of reliable prediction models to address environmental challenges unique to island ecosystems [57]. Southeast Asia by 2050: An analysis by IRENA focuses on Southeast Asia's renewable energy outlook, highlighting the region's potential for solar and wind energy. The report underscores the need for advanced forecasting tools to manage environmental factors such as monsoons and tropical storms, which significantly impact energy production [52]. In conclusion, the literature research shows that achieving 100% renewable energy by 2050 in various regions necessitates the development and implementation of advanced forecasting models that account for environmental conditions. Machine learning techniques offer a viable solution to enhance prediction accuracy, thereby facilitating the integration of renewable energy sources into existing power systems. Also, renewable energy generation prediction presents numerous challenges such as the following:  
Data Quality and Variability: Includes variable environmental conditions that impact data reliability. Intricacy of Energy Systems: The interplay of various variables, including meteorological conditions and energy consumption, complicates modeling. Integration with Smart Grids: Precise predictions are crucial for equilibrating energy supply and demand in real time.