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Forecasts of Ontario's Monthly Energy Consumptions

• Investigating the relationship between temperature and monthly energy consumption in Ontario, modeling dynamic regression model with ARIMA.

Historical Trends

Temperature:

• Historical data shows an increase in temperature percentiles (10%, 50%, and 90%) over the past 30 years, with a strong upward trend and clear seasonality.

Energy Consumption:

• The decomposition of energy consumption data reveals a decreasing trend with strong seasonal components, indicating regular fluctuations throughout the year.

Methods

1. Temperature Forecasting:

• Utilized ARIMA(1,0,0)(2,1,2) model to forecast future temperatures based on ACF and PACF plots.
• Applied seasonal adjustments to address non-stationarity in the data.

2. Model Selection for Energy Consumption:

• ARIMA Model based solely on historical data.
• Dynamic Regression Model that includes temperature as an explanatory variable.

3. Model Evaluation:

• Compared performance using metrics such as AICc and BIC and conducted rolling-forecast-origin cross-validation to assess predictive accuracy.

Main Findings

• The dynamic regression model outperforms the ARIMA model by 1-2% in terms of error, highlighting temperature’s significance as a predictor for energy consumption in Ontario.
• Accurate forecasts for energy consumption can facilitate planning for maintenance and repairs of electricity-generating facilities.
• Future developments could explore optimization techniques using dynamic programming to enhance production efficiency and reduce costs based on available supply data.

Datasets & Sources

• Climate Canada
• Independent Electricity System Operator (IESO)