Multi-Unit Wind Power Prediction Based on LSTM and Particle Swarm Algorithm
Wu Zhenlong1, Mo Yipeng1, Wang Ronghua2, Fan Xinyu1, Liu Yanhong1, Guo Xiaolian3
1. Zhengzhou University, School of Electrical and Information Engineering; 2. Shandong Labor Vocational Technical College; 3. Zhejiang Special Equipment Science Research Institute
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Contents
01
Abstract
02
Chart Appreciation
03
Cite This Article
04
Author Introduction
Abstract
Wind energy is favored globally due to its widespread distribution, pollution-free, and renewable characteristics. However, the scheduling of wind energy is affected by various factors, including wind speed, atmospheric pressure, terrain, and altitude, which can cause significant disturbances to wind power output, leading to intermittency and instability. Therefore, predicting wind power output and minimizing uncertainty is necessary. Based on this, this paper proposes a multi-unit wind power prediction method based on Long Short-Term Memory (LSTM) networks. First, the Spearman correlation coefficient method is used for quantitative analysis of the data, followed by principal component analysis to reduce dimensionality and extract key information. The LSTM is used to predict wind power output in a single-step rolling manner, predicting only the next time step’s power value and adjusting inputs through a sliding window. To address the difficulty in tuning LSTM parameters, Particle Swarm Optimization (PSO) is used to optimize LSTM hyperparameters with the root mean square error as the optimization objective. For the multi-unit wind power prediction problem, this paper starts with the single unit, identifies the best-performing model in the single unit, and then extends the single unit wind power prediction to multi-units. Experimental results show that compared to other models, the proposed method reduces the root mean square error by 11.8% and the mean absolute error by 5.03%. This indicates that the LSTM optimized by PSO can achieve higher accuracy and better generalization ability.
Chart Appreciation
Figure 1: LSTM Internal Structure Diagram
Figure 1: LSTM internal structure diagram
Figure 2: Single-Step Rolling Prediction Method
Figure 2: Single-step rolling prediction method
Figure 3: Particle Swarm Algorithm Flow Chart
Figure 3: Particle swarm algorithm flow chart
Figure 4: PSO Iterative Process
Figure 4: PSO iterative process
Figure 5: PSO-LSTM Prediction Results of Unit 1
Figure 5: PSO-LSTM prediction results of unit 1
Figure 6: Principal Component Cumulative Variance Contribution Rate
Figure 6: Principal component cumulative variance contribution rate
Figure 7: Multi-Unit Wind Power Prediction Flow Chart
Figure 7: Multi-unit wind power prediction flow chart
Figure 8: Multi-Unit PSO-LSTM Prediction Results
Figure 8: Multi-unit PSO-LSTM prediction results
Cite This Article
Wu Zhenlong, Mo Yipeng, Wang Ronghua, et al. Multi-Unit Wind Power Prediction Based on Long Short-Term Memory and Particle Swarm Optimization[J]. Journal of Zhengzhou University (Engineering Science), 2024, 45(6):114-121.
WU Z L, MO Y P, WANG R H, et al. Multi-unit wind power prediction based on long short-term memory and particle swarm optimization[J]. Journal of Zhengzhou University (Engineering Science), 2024, 45(6):114-121.
Author Introduction
Associate Professor, School of Electrical and Information Engineering, Zhengzhou University, PhD, Master’s Supervisor.
Main Research and Teaching Experience: Obtained PhD from Tsinghua University in 2020, visiting scholar at the University of California, Merced, and Southern University of Science and Technology. Engaged in research and teaching in control theory and its application in energy systems, new energy power prediction, and consumption. Member of the Self-Resilient Control Professional Committee of the Chinese Society of Command and Control, member of the Chinese Association of Automation and the Chinese Society of Command and Control.
Research Achievements: Published over 40 academic papers in international journals, conferences, and domestic core journals, representative papers include: “Performance Analysis of Improved ADRCs for a Class of High-Order Processes with Verification on Main Steam Pressure Control”, “Modified Active Disturbance Rejection Control Design Based on Gain Scheduling for Selective Catalytic Reduction Denitrification Processes”, “Economic Operation of Islanded Micro-Grids via Modified Active Disturbance Rejection Control”, etc.; published 2 monographs; applied for over 3090 patents; received the second prize for scientific and technological progress in Henan Province, and multiple excellent paper awards at conferences. Led and completed projects such as the National Natural Science Foundation project, Henan Province Science and Technology Key Project, National Key Laboratory Open Fund of Power Systems, Zhengzhou University Professor Team Support for Enterprise Innovation-Driven Development Project, and Zhengzhou University Youth Talent Enterprise Cooperation Innovation Team Support Program, with multiple achievements applied in various types of units including 1000MW, 600MW, and 300MW.
Recruitment Direction and Application Requirements: Recruiting master’s students in Control Engineering.
Contact Information:[email protected]
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