Energy Demand Analysis & Visualization
2025 · Data Science
Energy Demand Analysis & Visualization
Forecasting New Zealand's energy demand and emissions with time-series models.
Role
Independent Project
Industry
Data Science
Year
2025
Overview
An independent data-science project forecasting New Zealand's residential energy consumption and carbon emissions. I developed and tuned ARIMA and SARIMA time-series models, incorporating GDP and population as exogenous regressors to improve accuracy, and rigorously validated them with MAE, RMSE, and MAPE. The forecasts are served through a lightweight Flask API and brought to life with interactive Power BI and Python visualisations.
The challenge
Energy demand is driven by many interacting factors. Producing forecasts accurate and explainable enough to support real sustainability insights is genuinely hard.
My approach
I built and tuned ARIMA/SARIMA models with GDP and population as exogenous regressors, validated them with MAE/RMSE/MAPE, and exposed the results through a Flask API and clear visualisations.
The outcome
The project produced reliable forecasts and interactive dashboards that turn raw consumption data into trends anyone can explore — supporting energy-efficiency and sustainability decisions.
What I did
- ARIMA / SARIMA time-series forecasting
- GDP & population as exogenous regressors
- Accuracy measured with MAE, RMSE, MAPE
- Flask API serving live forecast data
- Power BI + Python (Matplotlib/Seaborn) dashboards
Built with
- Python
- ARIMA/SARIMA
- Flask
- Power BI
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