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

Mid-Long Term Daily Electricity Consumption Forecasting Based on Piecewise Linear Regression and Dilated Causal CNN