[1]李艳波,刘妙阳,李林宜,等.高速公路服务区自洽能源系统的IWOA-LSSVM光伏功率短期预测[J].长安大学学报(自然科学版),2024,44(5):47-56.[doi:10.19721/j.cnki.1671-8879.2024.05.005]
　LI Yan-bo,LIU Miao-yang,LI Lin-yi,et al.Short-term prediction of photovoltaic power by IWOA-LSSVM for self-consistent energy system in highway service areas[J].Journal of Chang’an University (Natural Science Edition),2024,44(5):47-56.[doi:10.19721/j.cnki.1671-8879.2024.05.005]
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高速公路服务区自洽能源系统的IWOA-LSSVM光伏功率短期预测()
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长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
第44卷
期数:
2024年5期
页码:
47-56
栏目:
交通能源融合技术专题
出版日期:
2024-10-10

文章信息/Info

Title:
Short-term prediction of photovoltaic power by IWOA-LSSVM for self-consistent energy system in highway service areas
文章编号:
1671-8879(2024)05-0047-10
作者:
李艳波1; 刘妙阳2; 李林宜1; 王龙飞3; 陈俊硕1; 王秋才2; 刘珈汐4
(1. 长安大学 能源与电气工程学院,陕西 西安 710064; 2. 长安大学 电子与控制工程学院,陕西 西安 710064; 3. 大理大学 工程学院,云南 大理 671003; 4. 国能准能集团有限责任公司,内蒙古 鄂尔多斯 017000)
Author(s):
LI Yan-bo1;  LIU Miao-yang2;  LI Lin-yi1;  WANG Long-fei3;  CHEN Jun-shuo1;  WANG Qiu-cai2;  LIU Jia-xi4
(1. School of Energy and Electrical Engineering, Chang'an University, Xi'an 710064,Shaanxi, China; 2. School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, Shannxi, China; 3. School of Engineering, Dali University, Dali 671003, Yunnan, China; 4. Guoneng Zhunneng Group CO., LTD., Erdos 017000, Inner Mongolia, China)
关键词:
交通工程;  光伏功率预测;  鲸鱼算法;  自洽能源系统;  最小二乘支持向量机;  Lé; vy飞行
Keywords:
traffic engineering;  photovoltaic power prediction;  whale algorithm;  self-consistent energy systems;  least squares support vector machines;  Lé; vy flights
分类号:
U491.8
DOI:
10.19721/j.cnki.1671-8879.2024.05.005
文献标志码:
A
摘要:
为了推动高速公路绿色转型发展,提高交通领域可再生能源自洽率,针对高速公路场景下光伏发电功率预测精度不高的问题,提出一种改进鲸鱼优化算法(IWOA)优化最小二乘支持向量机(LSSVM)的预测模型(IWOA-LSSVM)。首先,对鲸鱼优化算法(WOA)进行改进,在初始化过程中加入混沌映射Tent和随机变量来增加初始化种群的多样性,提高发现全局最优解的概率; 在WOA中引入非线性收敛因子和自适应权重的方法、有效提高全局搜索和局部搜索的能力; 在螺旋更新阶段融合Lévy飞行策略,扩大搜索空间的覆盖范围,提升算法的全局优化能力,通过以上改进方法以优化LSSVM的模型参数,解决盲目选参问题。其次,构建IWOA-LSSVM,对收集到的历史数据进行分析并根据光伏电站实际环境使用Pearson相关系数评估关键气候影响因素,利用该方法得到模型初始参数。最后,通过对河北某高速公路服务区光伏电站的实际数据进行仿真试验。研究结果表明:在不同天气条件下,IWOA-LSSVM的预测精度均高于LSSVM; 晴天时,IWOA-LSSVM相比于LSSVM的均方根误差RMSE和平均绝对误差MAE分别下降了26.87%和25.92%; 阴天时,IWOA-LSSVM相比于LSSVM的RMSE和MAE分别下降了8%和6%; 雨天时,IWOA-LSSVM相比于LSSVM的RMSE和MAE下降最高,分别为49.6%和60%,模型预测精度提升最为显著; 该模型提高了光伏发电功率预测精度,提升了高速公路自洽能源系统光伏发电的经济效益。
Abstract:
In order to promote the development of highway green transformation and improve the self-referential rate of renewable energy in the transport sector, and to address the problem of low accuracy of PV power prediction in highway scenarios, an improved whale algorithm(IWOA)was proposed to optimize the prediction model for least squares support vector machine(LSSVM). Firstly, the traditional whale optimization algorithm was improved by adding the chaotic mapping Tent and random variables in the initialization process to increase the diversity of the initialized population, which improves the probability of discovering the globally optimal solution. A non-linear convergence factor and an adaptive weighting method were introduced into the whale optimization strategy to effectively improve the ability of both global and local searches, and a Lévy was integrated into the spiral updating phase to expand the search space. Flight strategy in the spiral updating stage, which expands the coverage of the search space and enhances the global optimization ability of the algorithm, through the above improvement methods in order to optimize the model parameters of the LSSVM and solve the problem of blind parameter selection. Secondly, the IWOA-LSSVM was constructed, and the collected historical data were analyzed and the key climate influencing factors were evaluated according to the actual environment of the PV plant using the Pearson correlation coefficient, which was used to obtain the initial parameters of the model. Finally, simulation experiments were carried out by simulating the actual data from a PV power plant in a highway service area in Hebei. The results show that the prediction accuracy of the IWOA-LSSVM is higher than that of the LSSVM model under different weather conditions, and the RMSE and MAE of the IWOA-LSSVM in a sunny day are reduced by 26.87% and 25.92%, respectively, compared to that of the LSSVM. In cloudy days, the RMSE and MAE of the IWOA-LSSVM decreased by 8% and 6%, respectively, compared to the LSSVM. Especially in rainy days, the IWOA-LSSVM had the highest percentage decreases in RMSE and MAE compared to the LSSVM, decreased by 49.6% and 60%, respectively, and the model's prediction accuracy was most significantly improved, and the model improved the PV the model improves the prediction accuracy of PV power generation and thus enhances the economic efficiency of PV power generation in highway self-consistent energy systems.4 tabs, 6 figs, 23 refs.

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备注/Memo

备注/Memo:
收稿日期:2024-05-21
基金项目:国家重点研发计划项目(2021YFB1600202)
作者简介:李艳波(1980-),男,黑龙江抚远人,副教授,工学博士,E-mail:ybl@chd.edu.cn。
通讯作者:王龙飞(1982-),男,黑龙江木兰人,副教授,工学博士,E-mail:wanglongfei@dali.edu.cn。
更新日期/Last Update: 2024-10-20