Investigating the Energy Production through Sustainable Sources by Incorporating Multifarious Machine Learning Methodologies

Résumé fourni par la source

Artificial Intelligence (AI) has the potential to revolutionize the way we predict and manage energy generation from solar and wind sources. It can greatly enhance the accuracy and scalability of energy prediction in solar and wind power systems. The implementation of advanced machine learning techniques can optimize the performance of these renewable energy systems and predict the variability of energy production. AI-powered algorithms can analyze data from various sources such as weather forecasts and historical energy production data to predict energy generation with increased precision, allowing for optimization of the system's operation. Furthermore, AI can also predict energy demand and adjust energy production, accordingly, resulting in reduced energy waste and increased system efficiency. As the demand for renewable energy continues to rise, the integration of AI in this field becomes increasingly crucial in ensuring a sustainable and dependable energy future. In this research, we investigate the utilization of AI techniques for evaluating the generation of hydrogen from solar and wind power. The results of this study will provide insight into the potential of AI tools for forecasting hydrogen production from solar and wind energy. The amount of hydrogen produced from solar energy is up to 93.3 × 103kg/km2whereas estimated production from wind is 6.7 kg/day. The comparison of the performance of the different machine learning models used in the study will help to identify the most effective method for forecasting hydrogen production in this context. Additionally, the study aims to contribute to the growing body of knowledge on the application of AI in the field of renewable energy and its potential to improve the efficiency, scalability, and reliability of energy systems.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.