Optimizing Wind Speed Estimation with Least Squares Generative Adversarial Networks: A Case Study of Pakistan

Wind energy has emerged as a key player in sustainable power generation. Accurate prediction of wind speed is critical for optimizing wind farm operations, energy output forecasting, and reducing dependency on traditional power sources. In this study, we propose a Least-Squares Generative Adversarial Network (LSGAN) for wind speed prediction in a specific region of Pakistan, utilizing data from Tandoghulamali, which includes features like wind speeds at various altitudes and wind direction. The model is designed to predict wind speed at 80 meters, a crucial target variable for wind turbine operations. The LSGAN model effectively leverages adversarial training, achieving high accuracy with a Mean Absolute Error (MAE) of 1.54 and a Root Mean Squared Error (RMSE) of 1.77. Through a detailed comparison of actual versus predicted values, the model demonstrates its ability to generalize well across unseen data, with stable performance during the adversarial learning process. This approach opens up new possibilities for applying advanced machine learning techniques in renewable energy forecasting, particularly in regions with sparse historical data.