Wind energy forecasting using Decomposition Technique and Machine Learning and Designing Multi-Task EMS

Mitigating climate change is one of the main benefits of using renewable energy sources. Due to its affordability and environmental friendliness, wind energy has so received more attention. Wind, however, is highly sporadic in nature because it depends on environmental factors. Because of this dependence, applying and planning for wind energy is a difficult task. Therefore, it is necessary to develop an adaptable, reliable model that takes those aspects into account and aids in lowering forecasting mistakes. In this work, wind energy as a renewable source for the production of power has been chosen, but as we know that wind is intermittent in nature and due to this intermittency, it becomes a challenging task to perform high precision forecasting of wind power. The primary goal is to anticipate the amount of wind power in the next few hours or days, regardless of the wind direction. Different models have different errors and efficiencies but we have chosen the best accurate model which performs the forecasting with less error and high accuracy. The accurate wind energy forecasting is necessary so as to meet the load demand at any time otherwise there will be imbalance in the frequency and the voltage.