Renewable Energy & ML

Wind Energy Prediction

Advanced machine learning system for predicting wind turbine power output using comprehensive environmental data analysis. Integrates weather patterns, turbine specifications, and operational parameters to optimize renewable energy generation and support grid management decisions.

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Wind Energy Prediction System

Project Overview

This project develops a machine learning model to forecast wind turbine power output using historical environmental and temporal data over a three-year period. By employing regression techniques, the system aims to predict continuous values of power output based on features such as wind speed, temperature, pressure, and time-related variables.

Accurate power forecasting is crucial for efficient energy management, cost reduction, and optimizing operations in the energy sector. The project addresses the variability in environmental conditions that can lead to fluctuations in power generation, providing energy companies and grid operators with precise forecasts to balance supply and demand.

Business Question

How can we accurately predict power output using historical environmental and temporal data over a three-year period?

Data Question

Which features significantly influence power output, and how can we preprocess and model the data to achieve optimal predictive performance?

Key Objectives

  • Power Forecasting – Predict continuous power output values based on environmental conditions
  • Feature Analysis – Identify which variables significantly influence power generation
  • Model Optimization – Develop and evaluate multiple regression models for best performance
  • Operational Efficiency – Enable optimized energy production schedules and grid reliability

Data Analysis & Preprocessing

Comprehensive data preparation and exploratory analysis workflow

Data Overview

  • Train.csv - Training dataset containing historical records over three years
  • Test.csv - Testing dataset for evaluating model performance
  • column_info.csv - Descriptions of variables in the datasets

Key Variables

Temporal Features

  • Time (converted to datetime format)
  • Month (extracted from time)
  • Year (extracted from time)
  • TimeOfDay (hour of the day)

Environmental Variables

  • WS_10m (wind speed at 10m height)
  • WS_100m (wind speed at 100m height)
  • Temperature
  • Pressure

Data Quality Assessment

  • Zero null values found in datasets
  • Zero duplicate records identified
  • High data quality confirmed

Feature Engineering

  • Time feature transformation to datetime
  • Extraction of temporal components
  • Correlation analysis for feature selection
  • MinMaxScaler applied for normalization

Exploratory Data Analysis

Distribution Analysis

  • Histograms plotted for all features
  • Most features showed normal distribution
  • Suitable for regression modeling

Correlation Analysis

  • Strong correlation between wind speeds and power output
  • TimeOfDay showed positive correlation with power
  • DP_2m feature removed due to high correlation

Temporal Analysis

  • Power output patterns by time of day
  • Monthly power generation variations
  • Temporal dependencies identified

Wind Speed Analysis

  • Monthly average wind speeds analyzed
  • Higher wind speeds correlated with higher power
  • Wind speed at different heights compared

Model Training & Evaluation

Comprehensive regression model development and performance analysis

Data Splitting

The preprocessed data was split into training and testing sets with an 80-20 split to ensure proper model evaluation on unseen data and assess generalization capabilities.

Models Evaluated

  • Linear Regression
  • Lasso Regression
  • Ridge Regression
  • K-Nearest Neighbors (KNN) Regressor
  • Decision Tree Regressor
  • Random Forest Regressor
  • AdaBoost Regressor

Evaluation Metrics

  • Mean Absolute Error (MAE) - Measures average magnitude of prediction errors
  • R-squared (R²) Score - Indicates proportion of variance predictable from features
  • Training performance assessment
  • Test performance evaluation

Model Performance Results

Model Training MAE Training R² Test MAE Test R²
Linear Regression 0.1384 0.505355 0.1403 0.492294
Lasso Regression 0.2125 0.000000 0.2122 -0.000039
Ridge Regression 0.1384 0.505339 0.1403 0.492322
K-Nearest Neighbors 0.0538 0.897932 0.0731 0.824192
Decision Tree 0.0000 1.000000 0.1118 0.561457
Random Forest 0.0318 0.969111 0.0858 0.777471
AdaBoost 0.1494 0.498542 0.1500 0.492723

Best Model: K-Nearest Neighbors Regressor

Performance Highlights

  • Test R² Score: 82.42%
  • Test MAE: 0.0731
  • Mean Difference: 0.0012

Key Insights

  • Superior performance among all models tested
  • Local patterns and nearest neighbors significant for prediction
  • Minimal bias with highly accurate predictions

Key Insights & Business Impact

Feature analysis, model insights, and practical applications

Feature Importance Analysis

Most Influential Features

  • Wind Speeds (WS_10m, WS_100m) - Primary drivers of power output
  • TimeOfDay - Strong positive correlation with power generation
  • Month - Seasonal patterns affecting energy production
  • Temperature and Pressure - Environmental conditions impacting performance

Data Quality Insights

  • Zero null values and duplicates confirmed data integrity
  • Normal distribution of features suitable for regression
  • Strong correlation patterns identified through analysis
  • Successful multicollinearity reduction with feature removal

Model Performance Analysis

KNN Regressor Success Factors

  • Local patterns in feature space highly significant
  • Nearest neighbor relationships captured complex dependencies
  • Mean prediction difference of only 0.0012
  • Explains 82.42% of variance in power output

Model Comparison Insights

  • Linear models showed moderate but consistent performance
  • Lasso regression underperformed due to feature elimination
  • Decision Tree exhibited overfitting with perfect training scores
  • Random Forest provided good alternative but less accurate than KNN

Business Applications & Impact

Operational Benefits

  • Energy Production Optimization - Improved scheduling based on accurate forecasts
  • Grid Reliability - Better supply and demand balancing
  • Cost Reduction - Efficient resource allocation and planning
  • Informed Decision Making - Data-driven operational strategies

Strategic Advantages

  • Competitive Edge - Superior forecasting accuracy in energy sector
  • Risk Mitigation - Reduced uncertainty in power generation
  • Scalability - Model applicable to larger datasets and real-time data
  • Integration Ready - Prepared for deployment in operational systems

Future Enhancements

Model Improvements

  • KNN hyperparameter tuning for n_neighbors optimization
  • Cross-validation for optimal parameter selection
  • Random Forest hyperparameter optimization
  • Ensemble methods combining top-performing models

Deployment Considerations

  • Real-time prediction system integration
  • Continuous model monitoring and updating
  • Performance tracking in production environment
  • Adaptation for streaming data processing

Technology Stack

Tools and libraries used for wind turbine power prediction analysis

Data Processing & Analysis

  • Pandas - Data manipulation and time series processing
  • NumPy - Numerical computations and array operations
  • Datetime - Time feature transformation and extraction
  • MinMaxScaler - Feature normalization and scaling

Machine Learning

  • Scikit-learn - Machine learning algorithms and evaluation
  • Linear Regression - Baseline regression model
  • Lasso & Ridge Regression - Regularized regression techniques
  • K-Nearest Neighbors - Best performing regression model
  • Decision Tree Regressor - Tree-based modeling
  • Random Forest Regressor - Ensemble learning method
  • AdaBoost Regressor - Adaptive boosting algorithm

Model Evaluation

  • Mean Absolute Error (MAE) - Primary evaluation metric
  • R-squared (R²) Score - Variance explanation measure
  • Train-Test Split - 80-20 data splitting for validation
  • Residual Analysis - Prediction accuracy assessment

Data Visualization & Analysis

  • Matplotlib - Statistical plotting and visualization
  • Correlation Matrix - Feature relationship analysis
  • Histograms - Distribution analysis
  • Pair Plots - Feature relationship visualization
  • Temporal Analysis - Time-based pattern identification

Development Environment

  • Jupyter Notebook - Interactive development environment
  • Python - Core programming language
  • CSV Data Processing - Structured data handling

Project Implementation

Data Pipeline

  • Automated data loading from CSV files
  • Time feature extraction and transformation
  • Correlation-based feature selection
  • Standardized preprocessing workflow

Model Development

  • Systematic model comparison framework
  • Standardized evaluation metrics
  • Performance tracking and analysis
  • Ready for production deployment