Advancing Renewable Energy Integration through Machine Learning-Assisted Wind Power Generation Prediction and Windmill Installation Optimization

Research Paper Title: Advancing Renewable Energy Integration through Machine Learning-Assisted Wind Power Generation Prediction and Windmill Installation Optimization Journal Name: International Journal of Computer Science Trends and Technology (IJCST) ISSN: 2347-8578 Volume 13, Issue 1, pp.19-25, February, 2025 This study aimed to compare the performance of different predictive models for forecasting power generation from weather-related variables. We evaluated five models: Linear Regression, Time Series Model (ARIMA), Random Forest, Gradient Boosting, and Neural Networks. Based on the results, we concluded that Neural Networks offered the most accurate predictions, outperforming all other models with the lowest MAE, RMSE, and MAPE, and the highest R² value. This highlights the model’s capacity to capture complex, nonlinear relationships within the data, which is critical in power generation forecasting.