Wind Power Generation Forecast Using Artificial Intelligence Techniques

Abstract

It is crucial to be able to forecast wind power generation with the greatest degree of precision because wind has a significant degree of instability and the energy generated cannot be conserved on a big scale due to expensive costs. This research compares the efficiency of wind energy predictions one hour in advance employing artificial intelligence based techniques. RNN and LSTM are the two DL approaches while Decision Tree Regression, Support Vector Regression, and Random Forest Tree are three ML algorithms which have been developed then compared among themselves based on MSE scores to determine the best performing model. Additionally, Time Series Analysis on MATLAB is also performed to get more detailed understanding of the data in sequence on regular intervals of time.