Machine Learning for Wind Power Prediction and Analysis

Problem: Predicting wind power production accurately is crucial for optimizing energy grid management and maximizing power generation from renewable sources. This requires a model that can capture the complex relationships between meteorological factors and wind power output.

Objective: Develop a machine learning model for accurate wind power prediction, enabling efficient grid management and increased utilization of wind power in renewable energy systems.

Data Acquisition: Collected historical wind power production data and corresponding meteorological data (wind speed, wind direction, temperature, etc.). Data collection involved partnering with operational wind farms and weather stations.

Data Preprocessing: Cleansed and preprocessed the data to handle missing values, outliers, and formatting inconsistencies. Engineered additional features based on domain knowledge and statistical analysis to enhance the model's performance.

Model Development: Implemented two machine learning models:

Model Training and Evaluation: Trained both models on the preprocessed data and evaluated their performance using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).

Model Selection and Deployment: Based on the evaluation results, the Gradient Boosted Trees Model was selected for operational deployment due to its superior accuracy. The model was integrated into the wind farm's control system for real-time wind power prediction.

The Gradient Boosted Trees Model achieved significantly higher accuracy than the Linear Model, with a lower error rate in wind power predictions.

The deployed model successfully predicts wind power production with high accuracy, enabling:

This project demonstrates the effectiveness of machine learning in improving wind power prediction and enhancing renewable energy utilization.

The successful deployment of the Gradient Boosted Trees Model paves the way for wider adoption of machine learning in wind farm management.

Future work includes: