Improving Renewable Energy Production Systems Using Artificial Intelligence

Abstract

This article examines the use of artificial intelligence (AI) into the optimization of renewable energy production systems, primarily focusing on solar energy. In light of the growing demand for renewable energy sources and the difficulties associated with their intermittent production, IA appears to be a promising approach to increase these systems' reliability and efficiency. A thorough analysis of the effect of the IA on the effectiveness of renewable energy generation systems is presented. It investigates the machine learning approaches used to forecast energy output based on historical and meteorological data and suggests intelligent storage and management strategies. In order to determine the most effective solutions, a comparison of the performance of several optimization algorithms is conducted. The anticipated studies show a significant increase in the efficiency of renewable energy production systems, a decrease in production costs, and an improvement in the accuracy of energy output forecasts.