  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.
