Forecasting enhancement of Wind Power Generation using Adversarial Networks : A Data Driven Approach

Wind energy is major contributor in the power system. However, the unpredictability of wind energy will have a substantial impact on the electrical grid, mostly because of the variable wind speed. Wind energy's consistency and dependability may cause instability, however the issue can be solved by scheduling generation and load. For that economical load dispatch planning is carried out by load dispatch centers. Wind power forecasts can be highly helpful for dispatch planning as well as selling and bidding in the energy market. Prediction can be done using different techniques like Numerical Weather Prediction (NWP), Artificial Intelligence and Machine Learning, time series analysis etc. Prediction using machine learning is incredibly accurate and quick compared to other techniques, particularly when employing Generative Adversarial Network. It is inspired by two-player zero-sum, where the Generator and Discriminator compete against each other. Gated recurrent Unit (GRU) based adversarial networks have fewer errors as compared to others.