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

TL;DR

This paper introduces a two-stage forecasting method combining piecewise linear regression and Dilated Causal CNN to improve daily electricity consumption predictions, especially on special dates like holidays.

Contribution

The study proposes a novel two-stage approach that decomposes consumption data and models residuals with advanced neural networks, enhancing accuracy over existing methods.

Findings

Higher forecasting accuracy demonstrated on experimental data.

Effective handling of holiday effects through decomposition and modeling.

Improved prediction for challenging dates like Spring Festival.

Abstract

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.