PREDICTIVE ANALYTICS ❘ Machine Learning

Predictive Energy Forecasting

Explored how machine learning techniques can support operational forecasting by evaluating predictive models for estimating power plant energy output.

Project Overview

This project explored how predictive modeling techniques could be applied to estimate the electrical energy output of a Combined Cycle Power Plant using operational input variables. The objective was to identify a practical and interpretable modeling approach that could support forecasting and operational decision-making.

To establish a reliable baseline model, I evaluated multiple linear regression as an initial approach due to the presence of multiple input variables influencing a single numerical outcome. The exercise focused not only on model accuracy, but also on understanding feature relationships, evaluating model fit, and interpreting results in a business-relevant way.

Problem

Power generation systems rely on accurate forecasting to optimize operational efficiency and resource planning. However, predicting electrical energy output involves analyzing multiple operational variables that may collectively influence performance.a practical and interpretable modeling approach that could support forecasting and operational decision-making.

The challenge was to identify an interpretable predictive modeling approach capable of estimating energy output while balancing simplicity, explainability, and baseline performance evaluation.

Creative Process

• Problem Framing
  • Defined the prediction goal and identified the relationship between operational inputs and energy output
  • Evaluated modeling approaches suitable for numerical prediction problems
• Model Selection
  • Selected Multiple Linear Regression as the initial benchmark model
  • Chose the approach due to:
    • multiple predictor variables
    • a single numerical outcome variable
    • strong interpretability for early-stage analysis
• Model Evaluation
  • Compared model performance using:
    • R-squared to assess overall fit
    • Mean Absolute Error (MAE) to evaluate prediction accuracy
  • Evaluated how different feature combinations influenced predictive performance
• Insight Interpretation
  • Analyzed feature coefficients to understand variable influence on energy output
  • Compared full-feature models against simplified single-feature approaches
  • Assessed tradeoffs between model simplicity and predictive accuracy

Key Insights

• Multiple Linear Regression provided a strong baseline model for predicting energy output
• Models using all available operational features performed better than single-feature approaches
• Feature selection significantly influenced predictive performance and model fit
• Interpretable models can provide valuable operational insights even before introducing more advanced machine learning techniques

Key Takeaway

This project reinforced the importance of starting with interpretable baseline models before pursuing more complex machine learning solutions. Rather than optimizing immediately for sophistication, the exercise emphasized structured experimentation, model evaluation, and understanding the relationship between inputs and outcomes.

It also highlighted how analytical thinking and predictive modeling can support operational forecasting and data-informed decision-making which are skills that are increasingly valuable in modern product management.

Assets

Excel files containing the dataset, model outputs, and performance metrics are available upon request for those interested in exploring the data and analysis further. Click here →