State Characteristic Clustering for Nonintrusive Load Monitoring with Stochastic Behaviours in Smart Grids
Integrating the nonintrusive load monitoring (NILM) technology into smart meters poses challenges in demand-side management (DSM) of the smart grid when capturing detailed power information and stochastic consumption behaviours, due to the difficulties in accurately detecting load operation states i...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/8839595 |
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| Summary: | Integrating the nonintrusive load monitoring (NILM) technology into smart meters poses challenges in demand-side management (DSM) of the smart grid when capturing detailed power information and stochastic consumption behaviours, due to the difficulties in accurately detecting load operation states in real household environments with the limited information available. In this paper, a state characteristic clustering (SCC) approach is presented for promoting the performance of event detection in NILM, which makes full use of multidimensional characteristic information. After identifying different types of state domains in an established multidimensional characteristic space, we design a sliding window difference search method (SWDS) to extract their initial clustering centre. Meanwhile, the mean-shift updating and iterating procedures are conducted to find the potential terminal stable state according to the probability density function. The above control strategy considers the transient events and stable states in a time-series dataset simultaneously, which thus allows the exact state of complex events to be obtained in a fluctuating environment. Moreover, a multisegment computing scheme is applied for fast computing in the state characteristic clustering process. Experiments of three different cases on both our real household dataset and REDD public dataset are provided to reveal the higher performance of the proposed SCC approach over the existing related methods. |
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| ISSN: | 1076-2787 1099-0526 |