Project 02 · September 2025

Energy
Consumption
Analysis

Data Science Linear Regression Big Data Tableau

Built a linear regression model to identify key factors influencing energy usage and forecast future consumption trends using historical data — delivering actionable insights for resource planning and efficiency.

Visualisation

Monthly Consumption Forecast

Monthly Energy Usage — Actual vs Predicted (kWh)
Actual
Predicted
Objective

Analyse and predict energy consumption patterns for improved resource planning and efficiency, identifying variables with significant impact through data-driven modelling.

Tech Stack
PySparkTableau Tableau PrepMatplotlib PythonCMD
Methodology
  • Ingested and cleaned historical energy datasets via PySpark
  • Feature engineering to isolate high-impact variables
  • Trained linear regression on consumption time series
  • Visualised results and forecasts in Tableau dashboards
Key Insights
  • Seasonal patterns are the strongest consumption predictor
  • Temperature and occupancy are top contributing variables
  • Model achieved strong R² on held-out validation data
  • Forecast accuracy enables proactive grid load management
Outcome

Provided data-driven insights enabling better energy planning. Highlighted variables with significant consumption impact. The pipeline is reproducible and scalable via PySpark for larger datasets.

← Back to Portfolio