首页|基于WT和黏菌算法的LSSVM短期风功率预测

基于WT和黏菌算法的LSSVM短期风功率预测

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针对风电出力存在随机性、波动性等问题，建立基于小波变换(WT)与黏菌算法(Slime mould algorithm，SMA)优化最小二乘支持向量机(LSSVM)关键参数的风功率预测模型。首先利用小波变换将风功率信号以及风速信号分解为多个不同频率的平稳的子序列，并提出采用一种黏菌优化算法优化LSSVM的参数，同时引入气象因素，包括风速、风向、温度、气压、湿度作为输入，分别建立模型来预测风电功率。通过将各个模型预测结果加和得到完整的风功率预测值。使用某风电厂数据进行仿真验证，实验结果表明，所提出的WT-SMA-LSSVM预测模型在短期风功率预测中具有更高的预测精度。
Short-Term Wind Power Prediction Using LSSVM Based on WT and Slime Mould Algorithm
The wind power prediction model based on the wavelet transform(WT)and the Slime mould algorithm(SMA)is developed to optimise the key parameters of the least squares support vector machine(LSSVM).A wavelet transform was used to decompose the wind power signal and the wind speed signal into several smooth subseries with different frequencies,and a slime mould algorithm was proposed to optimise the parameters of the LSSVM,while mete-orological factors,including wind speed,wind direction,temperature,air pressure and humidity,were introduced as in-puts to build separate models to predict wind power.The complete wind power prediction was obtained by summing the individual model predictions.The simulations were carried out using data from a wind power plant and the experi-mental results show that the WT-SMA-LSSVM prediction model proposed in this paper has higher prediction accuracy in short-term wind power prediction.

Wind power forecastingLeast squares support vector machineWavelet transformSlime mould algo-rithm

赵卿、高文华、石慧、董增寿

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太原科技大学电子学院,山西 太原 030024

风功率预测 最小二乘支持向量机 小波变换 黏菌算法

国家青年科学基金项目国家青年科学基金项目山西省重点研发计划项目山西省重点研发计划项目山西省重点研发计划项目山西省基础研究计划(自由探索类)面上项目山西省基础研究计划(自由探索类)面上项目山西省回国留学人员科研资助项目山西省回国留学人员科研资助项目山西省留学人员科技活动择优资助项目

6170329772071183202202100401002202202090301011202202150401005202103021232062022030212112052021-1352021-13420220029

2024

计算机仿真
中国航天科工集团公司第十七研究所

计算机仿真

CSTPCD
影响因子：0.518
ISSN：1006-9348
年,卷(期)：2024.41(7)
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