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).