A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting

Electric energy forecasting domain attracts researchers due to its key role in saving energy resources, where mainstream existing models are based on Gradient Boosting Regression (GBR), Artificial Neural Networks (ANNs), Extreme Learning Machine (ELM) and Support Vector Machine (SVM). These models encounter high-level of non-linearity between input data and output predictions and limited adoptability in real-world scenarios. Meanwhile, energy forecasting domain demands more robustness, higher prediction accuracy and generalization ability for real-world implementation. In this paper, we achieve the mentioned tasks by developing a hybrid sequential learning-based energy forecasting model that employs…

• NR
  National Research Foundation
• NR
  National Research Foundation of Korea

  Award: 2019M3F2A1073179
• MO
  Ministry of Science and ICT, South Korea