Daily electricity consumption forecasting is a classical problem. Existing
forecasting algorithms tend to have decreased accuracy on special dates like
holidays. This study decomposes the daily electricity consumption series into
three components: trend, seasonal, and residual, and constructs a two-stage
prediction method using piecewise linear regression as a filter and Dilated
Causal CNN as a predictor. The specific steps involve setting breakpoints on
the time axis and fitting the piecewise linear regression model with one-hot
encoded information such as month, weekday, and holidays. For the challenging
prediction of the Spring Festival, distance is introduced as a variable using a
third-degree polynomial form in the model. The residual sequence obtained in
the previous step is modeled using Dilated Causal CNN, and the final prediction
of daily electricity consumption is the sum of the two-stage predictions.
Experimental results demonstrate that this method achieves higher accuracy
compared to existing approaches.

日常电力消耗预测是一个经典问题，本研究通过使用分段线性回归和扩张因果卷积神经网络构建两阶段预测方法，将每日电力消耗系列拆分为趋势、季节和残差三个组件，并引入距离变量来挑战传统节日预测，最终预测结果通过两阶段预测的求和得出，实验结果表明该方法较现有方法具有更高的准确性。