The Technical Optimized Way of Using the AI And its Strategies to Model the Energy Management System in Renewable Systems

Although wind energy (form of renewable energy systems) has garnered a lot of attention recently, its indirect and unpredictable nature makes it difficult to dispatch and prepare for electric power networks. Accurate wind power forecast is necessary to maintain power system balance. This study compares the prediction accuracy of the linear regression, k-nearest neighbour, and decision tree techniques. Compared to the k-nearest neighbour regression methodology, which yields lower coefficient of determination values, the decision tree regression method shows lower mean absolute error levels. Furthermore, the influence of meteorological factors on wind power projections is evaluated; the primary factors are air temperature, barometric pressure, wind speed, and direction. All things considered, this study offers helpful information that can help with wind.