Decomposition-Residuals Neural Networks: Hybrid system identification applied to electricity forecasting

COVID-19 causes issues in energy demand profile forecasting. Day-ahead energy forecasting systems struggle to provide accurate demand predictions. Decomposition-Residuals Deep Neural Networks (DR-DNN) is a two-layer hybrid architecture with: a decomposition and a nonlinear layer. Based on statistical tests, the decomposition layer applies robust signal decomposition and low-order system identification of input data streams into: trend, seasonal and residuals signals. Utilizing calendar information, temporal signals are added: sinusoidal day/night cycles, weekend/weekday, etc. The nonlinear layer can learn unknown nonlinear patterns using typical DNN models like Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). DR-DNN outperformed baseline, linear, and plain DNN models on next-day electricity forecasts within the COVID-19 era (September 2020-January 2021).

COVID-19对能源需求曲线预测造成了影响。日前能源预测系统难以提供准确的需求预测。分解-残差深度神经网络（Decomposition-Residuals Deep Neural Networks，DR-DNN）是一种双层混合架构，包含分解层和非线性层。基于统计检验，分解层对输入数据流进行鲁棒信号分解和低阶系统辨识，将其划分为趋势、季节和残差信号。利用日历信息，添加时间信号：正弦昼夜周期、周末/工作日等。非线性层可利用典型的深度神经网络（Deep Neural Networks，DNN）模型（如循环神经网络（Recurrent Neural Networks，RNN）和卷积神经网络（Convolutional Neural Networks，CNN））学习未知的非线性模式。DR-DNN在COVID-19时期（2020年9月-2021年1月）的次日电力预测中，性能优于基准模型、线性模型和普通DNN模型。