采用机器学习技术的风电场噪声预测模型优化及验证  

Optimization and validation of wind farm noise prediction model using machine learning technology

作　　者：张丽梅 肖潇 张宇 ZHANG Limei;XIAO Xiao;ZHANG Yu(China Power Construction Kunming Survey and Design Institute Co.,Ltd.,Kunming 650000,China;Yunnan Water Resources and Hydropower Survey and Design Institute Co.,Ltd.,Kunming 650000,China)

机构地区：[1]中国电建集团昆明勘测设计研究院有限公司,云南昆明650000 [2]云南省水利水电勘测设计院有限公司,云南昆明650000

出　　处：《中国高新科技》2024年第24期138-140,共3页

摘　　要：利用机器学习技术对风电场噪声预测模型进行优化与验证。基于现有风电场噪声预测模型,利用Elman神经网络,综合考虑风速、风向和距离等因素,建立了风电场噪声预测模型。通过收集并处理风电场噪声数据,进行特征选择与提取,构建初始模型并进行训练与调整。经验证优化后的模型预测准确性显著提升,更能真实反映风电场的噪声分布情况。研究结果表明,采用机器学习技术的风电场噪声预测模型具有更高的拟合相关性和预测精度。Machine learning technology is used to optimize and validate the noise prediction model of wind farms.Based on the existing wind farm noise prediction model,an Elman neural network was used to comprehensively consider factors such as wind speed,wind direction,and distance to establish a wind farm noise prediction model.By collecting and processing noise data from wind farms,feature selection and extraction are carried out,and an initial model is constructed and trained and adjusted.After verification and optimization,the model's prediction accuracy has significantly improved,and it can more accurately reflect the noise distribution of wind farms.The research results indicate that the wind farm noise prediction model using machine learning technology has higher fitting correlation and prediction accuracy.

关 键 词：风电场噪声 机器学习 噪声预测 模型优化 

分 类 号：TM614[电气工程—电力系统及自动化]