Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

作者：Liu Chao; Akintayo Adedotun; Jiang Zhanhong; Henze Gregor P; Sarkar Soumik^*

来源：Applied Energy, 2018, 211: 1106-1122.

DOI：10.1016/j.apenergy.2017.12.026

• Non-intrusive load monitoring (NILM)
• Spatiotemporal pattern network (STPN)
• Multivariate time-series

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in "terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shown to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.

• 出版日期2018-2-1