摘要
风电功率预测对于确保风能可靠地接入电网起着关键作用。本文提出一种随机森林(RF)-卷积神经网络(CNN)混合模型,用于短期风电功率预测。该模型融合RF的集成技术、随机选择属性与CNN捕获风电时空特征的优势,增强预测的准确性和稳健性。首先,通过分析决策树与CNN的类比等效性,明确RF与CNN结合的理论依据;然后,构建包含方均根误差(RMSE)、决定系数和Spearman相关系数的风电功率预测模型评估指标体系;最后,基于欧洲地区风电场的3个开源数据集进行模型有效性验证。结果表明:与其他5种模型相比,RF-CNN模型表现最优,验证了该模型进行风电功率预测的有效性和准确性。
Wind power prediction plays a crucial role in ensuring the reliable integration of wind energy into the grid.This study proposes a novel hybrid model combining random forest(RF)and convolutional neural network(CNN),referred to as the RF-CNN model,specifically designed for short-term wind power prediction.The model integrates the advantages of RF integration technology,random selection of attributes,and CNN capturing the spatiotemporal characteristics of wind power,to enhance prediction accuracy and robustness.Firstly,by analyzing the analog equivalence between decision trees and CNNs,the theoretical basis for combining RF and CNN is established.Next,an evaluation system for wind power prediction models that includes root mean square error(RMSE),determination coefficient,and Spearman correlation coefficient is introduced.Finally,validatinos are conducted using three open-source wind power datasets from European wind farms.The results demonstrate that,compared to other five models,the RF-CNN model outperforms in all three datasets,thus confirming the model’s effectiveness and accuracy for wind power prediction.
作者
李桓
滕云雷
LI Huan;TENG Yunlei(State Grid Shandong Electric Power Company Linyi Power Supply Company,Linyi,Shandong 276000)
出处
《电气技术》
2025年第5期27-33,38,共8页
Electrical Engineering
关键词
风电功率预测
随机森林
卷积神经网络
混合模型
误差指标
wind power prediction
random forest
convolutional neural network
hybrid model
error index
