Explore complete Python projects that apply machine-learning models to energy production, consumption, efficiency, forecasting, and renewable-energy systems.
Use data to understand and predict energy systems
Energy systems generate large amounts of operational, environmental, and consumption data. Machine-learning models can use these observations to estimate power output, forecast demand, evaluate efficiency, detect unusual behavior, and support more informed engineering decisions.
Combined-Cycle Power Plant Machine-Learning Project
Learn how environmental and operational measurements can be used to predict the electrical power output of a combined-cycle power plant with Python and machine-learning regression models.
- Understand the CCPP prediction problem
- Explore the dataset and input variables
- Prepare data for regression modeling
- Train and evaluate machine-learning models
- Compare predictions using regression metrics
Explore machine learning across the energy sector
The Energy Sector collection is divided into four main application areas. New datasets, benchmark studies, tutorials, and complete Python projects will be added to these collections.
Power Generation and Power Plants
Predict electrical output, analyze operational variables, compare regression models, and investigate the performance of power generation systems.
- Combined-cycle power plants
- Electrical power-output prediction
- Power-generation efficiency
- Operational data analysis
Energy-Consumption Forecasting
Use historical consumption, weather, calendar, and operational data to estimate future energy demand across different time horizons.
- Short-term load forecasting
- Household energy consumption
- Industrial energy demand
- Time-series feature engineering
Building Energy-Efficiency Analysis
Model heating and cooling requirements, compare building designs, identify important variables, and estimate energy-performance indicators.
- Heating-load prediction
- Cooling-load prediction
- Building design variables
- Energy-efficiency classification
Renewable-Energy Prediction
Apply machine learning to solar, wind, and other renewable-energy data for generation forecasting, resource assessment, and system planning.
- Solar-power forecasting
- Wind-power prediction
- Weather-based energy models
- Renewable resource assessment
From raw energy data to evaluated predictions
Each energy project follows a transparent workflow that connects the practical energy problem with data preparation, machine-learning model development, evaluation, and interpretation.
Define the energy problem
Identify the energy system, prediction target, inputs, and practical objective.
Prepare the dataset
Explore variables, handle data-quality issues, and create reproducible training and test sets.
Train and compare models
Evaluate different algorithms using appropriate validation procedures and performance metrics.
Interpret the results
Analyze predictions, errors, important variables, limitations, and practical conclusions.
Begin with power plant output prediction
Open the Combined-Cycle Power Plant project and follow a complete machine-learning workflow for predicting electrical power output with Python.
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