Work

Secondary Hybrid Decomposition Strategy for Wind Power Prediction Using Long Short-Term Memory With Crisscross Optimization

Year: 2025

Type: article

Abstract: This paper presents a hybrid forecasting model for a wind power named Secondary Hybrid Decomposition (SHD)-Long Short-Term Memory (LSTM) with Crisscross Optimization (CSO).The model integrates three key techniques to improve prediction accuracy. Firstly, the SHD mitigates the limitations of single-stage decomposition by employing a two-step Empirical Mode Decomposition (EMD) process. This improves the predictability of wind power series by decomposing it into its Intrinsic Mode Functions (IMFs) and residual components. Secondly, LSTM networks are utilized to forecast each subseries and residue generated by SHD, capturing temporal dependencies within wind power data effectively. Finally, the CSO is used to optimize the hyperparameter of individual LSTM networks, enhancing global search performance. Two sets of wind farm data are used to validate the accuracy of the model. A comparative study shows that the proposed SHD-LSTM-CSO model performs better than other hybrid models such as SHD-LSTM, EMD-LSTM, LSTM based on variable mode decomposition (VMD) (VMD-LSTM), Gray Worm Optimization-based backpropagation neural network (GWO-BPANN) and EMD-based Artificial Neural Network (EMD-ANN) methods.

Source: IEEE Access

DOI: 10.1109/access.2025.3597646

Authors: Yoseph Mekonnen Abebe, Habtamu Kassa Bayu, Tekalign Tesfaye Mengistu, Abera Tullu, Sunghun Jung

Institutions: Addis Ababa Science and Technology University, North Carolina State University, North Carolina Agricultural and Technical State University, Chosun University

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $2,075