基于分段线性回归和Dilated Causal CNN的中长期日用电量预测
Mid-Long Term Daily Electricity Consumption Forecasting Based on Piecewise Linear Regression and Dilated Causal CNN
AI总结：
针对节假日等特殊日期用电量预测精度下降问题，提出基于分段线性回归与Dilated Causal CNN的两阶段预测方法，显著提升了中长期日用电量预测准确率。
摘要：
日用电量预测是一个经典问题。现有的预测算法在节假日等特殊日期的准确率往往会下降。本研究将日用电量序列分解为三个分量：趋势、季节性和残差，并构建了一种两阶段预测方法，使用分段线性回归作为滤波器，Dilated Causal CNN作为预测器。具体步骤包括在时间轴上设置断点，并使用月份、工作日和节假日等独热编码信息拟合分段线性回归模型。对于春节这一具有挑战性的预测，模型以三次多项式形式引入距离作为变量。使用Dilated Causal CNN对前一步获得的残差序列进行建模，日用电量的最终预测值为两阶段预测结果之和。实验结果表明，与现有方法相比，该方法实现了更高的准确率。
英文摘要：
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.
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