基于遗传算法的光伏系统发电量预测
Photovoltaic power generation forecasting using genetic algorithm
李燕斌 1魏婷婷 1贾恒 1李淑新1
作者信息
- 1. 中原工学院自动化与电气工程学院,河南郑州 450007
- 折叠
摘要
为准确预测光伏系统的发电量,构建了用遗传算法优化的BP神经网络发电量短期预测模型(简称GA-BP模型).通过遗传算法的迭代,对BP神经网络的权值和阈值进行优化,以实现对算法的改进.对所选择国能日新光伏系统预测大赛的数据进行预处理、归一化,并将数据输入GA-BP模型,进行了实验.对比实验说明,GA-BP模型不管是在预测结果上还是在模型稳定性上都明显优于BP神经网络模型.
Abstract
This study proposes a Genetic Algorithm-optimized BP(GA-BP)neural network model for accurate short-term forecasting of photovoltaic(PV)power generation.Initially,the genetic algo-rithm iteratively optimizes the weights and thresholds of the BP neural network to identify optimal values,thereby enhancing the performance of the algorithm.Subsequently,preprocessing and nor-malization are applied to selected PV prediction data before inputting them into the model.Experi-mental results indicate that the GA-BP model outperforms the conventional BP model in terms of pre-dictive accuracy and stability.
关键词
光伏发电/发电量预测/遗传算法/BP神经网络Key words
photovoltaic power generation/power generation prediction/genetic algorithm/BP neu-ral network引用本文复制引用
出版年
2024
NETL
NSTL
万方数据