Machine Learning and Optimization Techniques for Wind Energy prediction: Challenges and their Solutions

Machine Learning and Optimization Techniques for Wind Energy prediction: Challenges and their Solutions

Green energy is the need of the hour in the present era because the world is facing drastic climate change. With global warming at its peak and fossil fuel reserves depleting at an extremely fast rate, the need of the hour is switch to renewable sources of energy and identify ways to expand global reserves to meet the rising power demands. Wind power has become a popular source of gen¬erating clean renewable energy and reduces economic vulnerability caused by traditional fuel prices. The inclusion of wind power in the existing electric scheme helps accelerate the transition to sustainable energy. In this regard, Internet of things (IoT) has a key role in creating a effective path for integrating a wind power plant with power system. Since, one of the major challenges in adapt¬ing wind energy plants is their highly unpredictable output nature due to different weather conditions. Hence forecasting the output of a wind power plant for either a short or long span of time is highly important. Wind power prediction is necessary for a stable power system and the optimal operation of the wind farm itself. Accurate forecasting of wind power ensures the energy demand is appropriately met. This paper gives a wide-ranging analysis and addresses specifically the implementation of machine learning schemes for wind power prediction as well as the utilization of various optimization approaches in this field and their contribution to substantial development and valuable insights for improving performance and predictability of wind power plants. Further the advantages and drawbacks are discussed categorically to give better insight to the reader.