
1. 河北农业大学信息科学与技术学院
2. 中国电力科学研究院有限公司
Published:2025
移动端阅览
[J]. 2025, (1): 106-115.
随着高比例新能源和多元负荷的不断接入,配电网呈现出动态变化和交直流混合运行的特性,农业负荷类型的增加和负荷辨识问题对配电网智慧农业的实施提出了新挑战。提出了一种结合耦合特征和改进随机森林算法的非侵入式农业负荷辨识方法。针对智慧农业当中负荷类型的多样性,分析了卷帘机、抽水泵、水平循环风扇等典型农业负荷的暂态特征与稳态特征,并结合农村分布式光伏发电数据,通过形态相似性分析提取了具有时间序列相关性的农业负荷耦合特征。基于历史负荷数据和特征,构造了样本矩阵作为随机森林算法的输入,并通过改进Bootstrap抽样、引入最优特征子集构造分类回归树(classification and regression tree
CART)决策树,以及基于负荷种类进行重复投票机制,构建了改进随机森林算法的农业负荷辨识架构。建立了准确率评价指标,通过仿真分析验证所提方法的有效性,并分析了关键参数对算法性能的影响。仿真结果表明,所提方法的准确率达到了98.96%,在辨识效率上相较其他算法有明显提升。
Xiurong Zhang,Xueqian Fu,Yixun Xue,Xinyue Chang,Xiang Bai.A review on basic theory and technology of agricultural energy internet[J].IET Renewable Power Generation,2023.
Zhihua Zhao,Huanning Wang,Yan Huang,Hejun Yao,Nan Li,Hengyu Tan,Yuan Liu.Research on non-invasive load identification method based on VMD[J].Energy Reports,2023.
Yi Shu Hui,Wang Jian,Liu Jun Jie.Simultaneous Load Identification Method Based on Hybrid Features and Genetic Algorithm for Nonintrusive Load Monitoring[J].Mathematical Problems in Engineering,2022.
Jiang Yanmei,Liu Minsheng,Peng Hao,Bhuiyan Md Zakirul Alam.A reliable deep learning-based algorithm design for IoT load identification in smart grid[J].Ad Hoc Networks,2021.
Chen Chen,Gao Pinghang,Jiang Jiange,Wang Hao,Li Pu,Wan Shaohua.A deep learning based non-intrusive household load identification for smart grid in China[J].Computer Communications,2021.
Wu Haiping,Liu Hui.Non-intrusive load transient identification based on multivariate LSTM neural network and time series data augmentation[J].Sustainable Energy, Grids and Networks,2021.
Huang Liang,Chen Shijie,Ling Zaixun,Cui Yibo,Wang Qiong.Non-invasive load identification based on LSTM-BP neural network[J].Energy Reports,2021.
Guolong Liu,Jinjin Gu,Junhua Zhao,Fushuan Wen,Gaoqi Liang.Super Resolution Perception for Smart Meter Data[J].Information Sciences,2020.
Shi Yinghuan,Suk Heung-Il,Gao Yang,Lee Seong-Whan,Shen Dinggang.Leveraging Coupled Interaction for Multimodal Alzheimer's Disease Diagnosis.[J].IEEE transactions on neural networks and learning systems,2020.
Dhibi K.,Fezai R.,Mansouri M.,Trabelsi M.,Kouadri A.,Bouzara K.,Nounou H.,Nounou M..Reduced Kernel Random Forest Technique for Fault Detection and Classification in Grid-Tied PV Systems[J].IEEE Journal of Photovoltaics,2020.
Zhicong Chen,Fuchang Han,Lijun Wu,Jinling Yu,Shuying Cheng,Peijie Lin,Huihuang Chen.Random forest based intelligent fault diagnosis for PV arrays using array voltage and string currents[J].Energy Conversion and Management,2018.
Leo Breiman.Random Forests.[J].Machine Learning,2001.
陈仪坤,步丹璐.农业信息基础设施建设对涉农企业价值的影响——基于“宽带中国”战略的准自然实验[J].农业技术经济,2024(05).
张宇,毕凡,苏海博,王勇,刘俊翔,郑方晴,刁均伟.基于最大奇异值能量熵和随机森林的真空快速开关机械故障诊断[J].高压电器,2023(11).
张培霄,尹晓红,李少远,王新立.基于VMD-CNN-LSTM的农业大棚园区用电负荷短期预测[J].信息与控制,2024(02).
邵佳佳,陈小毅,戴媛媛,黄赞,吴晨,王琦,郭赟韬,徐维.基于随机森林的纯电动汽车公共充电需求研究[J].供用电,2023(09).
易仕琪,孔政敏,王帅,霍梓航.一种基于多分类器耦合的非侵入式负荷辨识方法[J].广东电力,2023(08).
严萌,于雅雯,王玲静,杜宇,武昕.基于多特征联合稀疏表达的SOM-K-means非侵入负荷辨识[J].电力建设,2023(05).
杜晓东,王立斌,刘科研,詹惠瑜.基于随机森林的配电网故障分布特征预测方法[J].电网与清洁能源,2023(01).
彭刘亚,解惠婷,冯伟栋.基于随机森林算法的砂土液化预测方法[J].物探与化探,2020(06).
郭红霞,陆进威,杨苹,刘泽健.非侵入式负荷监测关键技术问题研究综述[J].电力自动化设备,2021(01).
邓晓平,张桂青,魏庆来,彭伟,李成栋.非侵入式负荷监测综述[J].自动化学报,2022(03).
王守相,郭陆阳,陈海文,邓欣宇.基于特征融合与深度学习的非侵入式负荷辨识算法[J].电力系统自动化,2020(09).
郭陆阳,王守相,陈海文,杨海跃,韩建振.基于双向GRNN与时间序列翻译模型的非侵入式负荷分解算法[J].供用电,2019(10).
蔡秋娜,刘思捷,陆秋瑜.基于GMM聚类和SVM的用户负荷行业分类辨识方法[J].广东电力,2017(12).
高云,杨洪耕.基于暂态特征贴近度匹配的家用负荷识别[J].电力系统自动化,2013(09).
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