|Table of Contents|

Short-term prediction of photovoltaic power by IWOA-LSSVM for self-consistent energy system in highway service areas(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2024年5期
Page:
47-56
Research Field:
交通能源融合技术专题
Publishing date:

Info

Title:
Short-term prediction of photovoltaic power by IWOA-LSSVM for self-consistent energy system in highway service areas
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)
Keywords:
traffic engineering;  photovoltaic power prediction;  whale algorithm;  self-consistent energy systems;  least squares support vector machines;  Lé; vy flights
PACS:
U491.8
DOI:
10.19721/j.cnki.1671-8879.2024.05.005
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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Last Update: 2024-10-20