Affiliations: [a] Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, China
| [b]
Hunan Provincial Key Laboratory for Control Technology of Distributed Electric Propulsion Aircraft, Hunan University of Arts and Science, Changde, China
| [c]
Hunan University, Changsha, China
Correspondence:
[*]
Corresponding author. Jianqi Li, Hunan Provincial Key Laboratory for Control Technology of Distributed Electric Propulsion Aircraft, Hunan University of Arts and Science, Changde, China. Email: [email protected].
Abstract: The prediction of power demand for unmanned aerial vehicles (UAV) is an essential basis to ensure the rational distribution of the energy system and stable economic flight. In order to accurately predict the demand power of oil-electric hybrid UAV, a method based on variational mode decomposition (VMD) and Sparrow Search Algorithm (SSA) is proposed to optimize the hybrid prediction model composed of long-short term memory (LSTM) and Least Squares Support Vector Machine (LSSVM). Firstly, perform VMD decomposition on the raw demand power data and use the sample entropy method to classify the feature-distinct mode components into high-frequency and low-frequency categories. Then, each modality component was separately input into the mixed model for rolling prediction. The LSSVM model and LSTM model were used to process low-frequency and high-frequency components, respectively. Finally, the predicted values for each modal component are linearly combined to obtain the final predicted value for power demand. Compared with the current models, the prediction model constructed in this paper stands out for its superior ability to track the changing trends of power demand and achieve the highest level of prediction accuracy.
Keywords: UAV demand power, variational mode decomposition, sparrow search algorithm, long-short term memory, long-short term memory
