基于PCA-LSTM算法的非侵入式负荷辨识方法

了解用户负荷分布特征是智能电网建设的重要部分,非侵入式负荷监测(Non-Intrusive Load Monitoring, NILM)以其便捷、高效、成本低的优点被电力系统工作人员广泛认可。文中提出了一种基于长短期记忆网络的NILM方法,通过采集用户电力入口处的电流波形并进行数据处理,得到用户的负荷特征数据。使用主成分分析手段,减少负荷特征数量,提高运算效率。使用擅长处理连续数据的长短期记忆网络模型,在划分好的验证集与测试集上对模型优劣进行评价,以获得最优参数模型。预测实验结果显示,文中所设计的非侵入式负荷监测方法可以对包括小功率用电器在内的家用电器进行准确辨别。

Non-intrusive load monitoring method based on PCA-LSTM algorithm

Understanding the characteristics of user load distribution is an important part of smart grid construction. Non-Intrusive Load Monitoring (NILM) is widely recognized by power system workers for its advantages of convenience, efficiency and low cost. This paper proposes a NILM method based on long-term and short-term memory networks. By collecting the current waveform at the user''s power inlet and performing data processing, the user''s load characteristic data is obtained. Use principal component analysis to reduce the number of load features and improve operational efficiency. Use the long-short-term memory network model that is good at processing continuous data, the model is evaluated on the divided verification set and test set, in order to obtain the optimal parameter model. The prediction experiment results show that the non-intrusive load monitoring method designed in this paper can accurately identify household appliances including low-power appliances.