Journal Article

The Importance of Environmental Factors in Forecasting Australian Power Demand

Perkins, Terry, Ganim, Benjamin, Eshragh Ali, Bandara Kasun

2022

Overview

We develop a time series model to forecast weekly peak power demand for three main states of Australia for a yearly timescale, and show the crucial role of environmental factors in improving the forecasts. More precisely, we construct a seasonal autoregressive integrated moving average (SARIMA) model and reinforce it by employing the exogenous environmental variables including, maximum temperature, minimum temperature, and solar exposure. The estimated hybrid SARIMA-regression model exhibits an excellent mean absolute percentage error (MAPE) of 3.41%. Moreover, our analysis demonstrates the importance of the environmental factors by showing a remarkable improvement of 46.3% in MAPE for the hybrid model over the crude SARIMA model which merely includes the power demand variables. In order to illustrate the efficacy of our model, we compare our outcome with the state-of-the-art machine learning methods in forecasting. The results reveal that our model outperforms the latter approach.

• Publisher: Springer Nature B.V
• Subject: Economic forecasting / Electric power demand / Electricity / Energy / Environmental factors / Forecasting / Learning algorithms / Machine learning / Peak load / Regression analysis / Regression models / Time series