Abstract
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.
Highlights
Recent years have witnessed an increasing attention to energy management in the smart grid, which forms a twodirectional information flow between the supply side and demand side
We propose a state characteristic clustering (SCC) approach for demand side with stochastic behaviours, in which all data points collected by the nonintrusive load monitoring (NILM) module will form several clusters in a multidimensional characteristic space. e proposed state domain definition is different from the oftenadopted stable states of load events
As we mentioned in the previous section, event points in the dataset can be extracted by comparing to the sum of the minimum cumulative sum statistics. erefore, the bilateralCuSum (BC) method with known prechange and postchange distributions will be effective for multistate events [26]
Summary
Recent years have witnessed an increasing attention to energy management in the smart grid, which forms a twodirectional information flow between the supply side and demand side. Erefore, integrating the NILM algorithm into smart meters becomes an inevitable trend, in which utility companies can realize efficient demand-side management (DSM) based on the demand behaviour of energy consumers [5]. For the supply side, obtaining consumers’ multidimensional consumption information enables the precise prediction of stochastic energy demand [7], the optimization decision [8], and powersharing control of microgrids [9]. To this end, an original NILM framework was proposed in [10]. Among them, related results can be classified into event-based and statebased approaches. e latter case includes the hidden
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