Electric Load Forecasting by Hybrid Self-Recurrent Support Vector Regression Model With Variational Mode Decomposition and Improved Cuckoo Search Algorithm

• Main Authors: Zichen Zhang, Wei-Chiang Hong, Junchi Li
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Support vector regression; variational mode decomposition; self-recurrent mechanism; tent chaotic mapping function; out-bound-back mechanism; cuckoo search algorithm
• Online Access: https://ieeexplore.ieee.org/document/8960371/

Accurate electric load forecasting is critical not only in preventing wasting electricity production but also in facilitating the reasonable integration of clean energy resources. Hybridizing the variational mode decomposition (VMD) method, the chaotic mapping mechanism, and improved meta-heuristic algorithm with the support vector regression (SVR) model is crucial to preventing the premature problem and providing satisfactory forecasting accuracy. To solve the boundary handling problem of the cuckoo search (CS) algorithm in the cuckoo birds' searching processes, this investigation proposes a simple method, called the out-bound-back mechanism, to help those out-bounded cuckoo birds return to their previous (the most recent iteration) optimal location. The proposed self-recurrent (SR) mechanism, inspired from the combination of Jordan's and Elman's recurrent neural networks, is used to collect comprehensive and useful information from the training and testing data. Therefore, the self-recurrent mechanism is hybridized with the SVR-based model. Ultimately, this investigation presents the VMD-SR-SVRCBCS model, by hybridizing the VMD method, the SVR model with the self-recurrent mechanism, the Tent chaotic mapping function, the out-bound-back mechanism, and the cuckoo search algorithm. Two real-world datasets are used to demonstrate that the proposed model has greater forecasting accuracy than other models.