| 李一鸣,邓君华,李志新,程含渺,鲍进,易永仙.基于阈值调整的负荷辨识开集识别算法[J].电力需求侧管理,2026,28(3):52-58 |
| 基于阈值调整的负荷辨识开集识别算法 |
| Open-set recognition algorithm for load identification based on threshold adjustment |
| 投稿时间：2026-01-06 修订日期：2026-03-08 |
| DOI：10.3969/j.issn.1009-1831.2026.03.008 |
| 中文关键词: 负荷辨识 开集识别 深度学习 未知类识别 概率模型 |
| 英文关键词: load identification open-set recognition deep learning unknown class recognition probabilistic model |
| 基金项目:国家电网公司科技项目(5700-202418277A-1-1-ZN) |
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| 中文摘要: |
| 负荷辨识是电力系统规划、运行和管理的关键技术之一,对于智能电网的高效调度和稳定运行具有重要意义。传统的负荷辨识方法通常依赖于封闭集假设,然而,在实际应用中,未知电器的出现使得基于封闭集假设的算法难以准确识别。针对这一问题,提出了一种基于阈值调整的负荷辨识开集识别算法OpenAppliance。该算法结合深度学习与概率模型,通过对神经网络输出进行校准,提升对未知类别的检测能力,同时保持已知类别的辨识精度。首先将负荷数据转换为适合深度学习的图像形式,构建了基于卷积神经网络(convolutional neural network,CNN)的负荷辨识模型;其次,结合OpenAppliance算法进行后处理,以调整分类阈值并优化识别结果;最后,在BLUED负荷数据集上进行验证,并与现有的负荷辨识算法进行对比。研究结果表明,OpenAppliance算法能增强负荷辨识的泛化能力,有效提升了负荷辨识系统的准确性与鲁棒性。 |
| 英文摘要: |
| Load identification is one of the key technologies in power system planning, operation, and management, playing a crucial role in the efficient scheduling and stable operation of smart grids. Traditional load identification methods typically rely on the closed-set assumption. However, in practical applications, the presence of unknown appliances makes it difficult for algorithms based on this assumption to achieve accurate recognition. To address this issue, an open-set load identification algorithm, OpenAppliance, based on threshold adjustment is proposed. The proposed algorithm integrates deep learning and probabilistic models, calibrating the neural network outputs to enhance the detection capability for unknown categories while maintaining recognition accuracy for known categories. First, load data is transformed into an image format suitable for deep learning, and a CNN-based load identification model is constructed. Then, the OpenAppliance algorithm is applied for post-processing to adjust classification thresholds and optimize recognition results. Finally, the method is validated on the BLUED load dataset and compared with existing load identification algorithms. Experimental results demonstrate that the OpenAppliance algorithm enhances the generalization ability of load identification and significantly improves the accuracy and robustness of the load identification system. |
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