基于Stacking多模型融合的电力负荷曲线识别  

Electric power load curve recognition based on stacking fusion of multiple models

作　　者：郑茵 刘常 黄力宇 温鑫 黄国华 刘斯亮 ZHENG Yin;LIU Chang;HUANG Liyu;WEN Xin;HUANG Guohua;LIU Siliang(Guangzhou Power Supply Bureau,Guangdong Power Grid Co.,Ltd.,Guangzhou 510000,China;Guangzhou Power Electrical Technology Co.,Ltd.,Guangzhou 510000,China)

机构地区：[1]广东电网有限责任公司广州供电局,广州510000 [2]广州市奔流电力科技有限公司,广州510000

出　　处：《电力需求侧管理》2024年第5期94-99,共6页Power Demand Side Management

基　　金：南方电网有限责任公司科技项目(GZHKJXM20210056(080036KK52210003))。

摘　　要：精准识别电力负荷曲线类型对于保障电网安全稳定运行和优化能源利用效率尤为重要。针对现有的时序数据识别方法在电力负荷曲线识别任务中准确率低、鲁棒性差等问题,提出了一种多模型融合集成学习的电网负荷曲线识别方法。首先,适应性改进了时序卷积网络、Transformer和Light GBM 3种基础模型,利用负荷曲线的局部、全局和结构信息预测负荷曲线类别;然后,通过Stacking集成学习自适应融合3种模型预测,用以进一步优化识别结果;此外,提出一种基于截断高斯分布的类内时段信号波动建模数据增广策略,旨在解决数据类别不平衡问题,提升识别模型的鲁棒性。仿真结果表明,相较于XG Boost、LSTM和MLP等方法,提出的方法对电网负荷曲线识别的准确率有显著的提高,满足了实际工程的需求。Identifying power load curves is essential to ensure the safety and energy efficiency of the power grid.However,existing algorithms for power load curve identification tasks often suffer from issues such as low recognition accuracy and robustness.To tackle these issues,a multi-model fusion ensemble learning method for power grid load curve recognition is proposed.Temporal convolutional network(TCN),transformer,and light GBM models are adaptively improved to predict load curve categories,considering three dimensions:local,global and structural features.Then,predictions through stacking ensemble learning(EL)to refine overall accuracy are adaptively fused.Additionally,a truncated Gaussian distribution(TGD)data augmentation strategy is introduced,which models intra-class signal fluctuations to alleviate data category imbalances,thereby enhancing the robustness of the recognition model.Through simulation analysis,compared with methods such as XG Boost,LSTM,and MLP,this approach shows a significant improvement in power load classification accuracy.

关 键 词：时序卷积网络 Light GBM TRANSFORMER 集成学习 负荷曲线识别 数据增广 

分 类 号：TM715[电气工程—电力系统及自动化] TP391[自动化与计算机技术—计算机应用技术]