Renewable Energy Management System (REMS) has been catching the world's attention due to its significant contribution to developing environmentally friendly energy sources. One of the sources for generating renewable energy is wind energy. However, the wind power output is highly volatile and intermittent owing to the characteristics of wind energy. Large-scale deployment of wind power impacts the reliability of the entire power system. Predicting adequate wind power encourages wind energy use, increases the power supply's efficiency, and ensures reliable power grid service.
This research thus focuses on predicting wind power generation using a multi-stages prediction approach. Positively influencing power generation and wind speed is the fundamental idea to predict wind speed, and the results would be used to power generation prediction input. In addition, most of the research is focused on minimizing the MAPE (Mean Absolute Percentage Error); however, there is still a weakness depending on the reliability of the prediction model based on the error measurement.
This research's findings are proposed scenarios to find a framework for predicting the wind power generation focused on the short-term time horizon. The error for wind speed prediction is precisely accurate since the error target is below 8%. The granularity of the prediction data is influences the chance of prediction to get error values. Also, for the power generation, and the overall prediction is 5.99% MAPE. The results show that real-time prediction for both different granularities is more accurate in predicting the weather variable (wind speed). Also, proposing the pragmatic ways of analyzing the prediction error. The power generation and wind speed have a striking correlation, such as cut-in class, relative class, and cut-out class, error analysis to determine the reliability of the prediction model proposed for different classes.

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