Multi-Time-Scale Shapelet Based Feature Extraction for Non-Intrusive Load Monitoring

摘要

The performance of non-intrusive load monitoring (NILM) system is closely related to the uniqueness and comprehensiveness of the load features extracted from the equipment. However, existing feature extraction methods for NILM are difficult to extract interpretable intuitive features representing the uniqueness of load, and lack of comprehensive attention to the features on different time scales. Therefore, this paper proposes a multi-time-scale shapelet based feature extraction framework. First, shapelet, the most unique segment of a sequence, which can distinguish it from other sequences is introduced as a feature and multi-time-scale current shapelets are proposed to extract comprehensive characteristics of electric appliances and offer intuitive interpretability. Meanwhile, a shapelet transform is constructed, so that the essence of shapelet based NILM is transformed from sequence matching to a general classification problem. Next, an ensemble bagging classifier is proposed on multi-time scales to effectively balance the classification errors on each time scale and enhance the performance of NILM system. In addition, this framework can support small sample training to facilitate rapid deployment of NILM applications at the initial practical stage. The results show that this framework can effectively focus on the uniqueness of load on different time scales, provide interpretability for NILM, and achieve good classification effects.

出版物
IEEE Transactions on Smart Grid
于鹤洋
于鹤洋
博士研究生

于鹤洋,博士研究生在读,研究方向为电力系统灵活资源的深度感知和聚合调控、人工智能与物联网在电力系统中的应用。

徐崇钧
徐崇钧
硕士研究生
耿光超
耿光超
副教授 | 博士生导师

耿光超,工学博士,浙江大学电气工程学院副教授、博士生导师,电机工程学系副主任,电力系统自动化所副所长,电气工程学院特聘助理,IEEE高级会员。

江全元
江全元
教授 | 博士生导师

江全元,博士、浙江大学电气工程学院教授,博士生导师,浙江省重点实验室(海洋可再生能源电气装备与系统技术研究实验室)副主任,中国电工技术学会电力系统控制与保护专业委员会委员。