Title: The Importance of Environmental Factors in Forecasting Australian Power Demand Show Chinese title

Title: 环境因素在预测澳大利亚电力需求中的重要性

Abstract: We develop a time series model to forecast weekly peak power demand for three main states of Australia for a yearly time-scale, 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. Show Chinese abstract

Abstract: 我们开发了一个时间序列模型，用于预测澳大利亚三个主要州的年度时间尺度上的每周高峰电力需求，并展示了环境因素在提高预测准确性方面的重要作用。 更具体地说，我们构建了一个季节性自回归积分移动平均（SARIMA）模型，并通过引入外生环境变量（包括最高温度、最低温度和太阳辐射量）对其进行强化。 估计出的混合SARIMA-回归模型表现出3.41%的优秀平均绝对百分比误差（MAPE）。 此外，我们的分析表明了环境因素的重要性，显示了混合模型相较于仅包含电力需求变量的原始SARIMA模型，在MAPE上提高了46.3%。 为了展示我们模型的有效性，我们将结果与最先进的机器学习方法进行了比较。 结果显示，我们的模型优于后者的方法。