Abstract
Non-intrusive load monitoring (NILM) is crucial because it helps monitor the operating status of electrical appliances online; detailed power consumption data regarding the appliances can then be obtained. However, the identification of resistive appliances that have similar features in a power grid is still a major problem. In this study, the reconstructed image of a voltage-current (VI) trajectory is used as input data for a convolutional neural network (CNN) to classify the appliances, particularly resistive appliances. Two dataset PLAID and IDOUC are introduced to verify the performance of the proposed method. According to the results, the excellent performance of the reconstructed VI image method for the identification of the household appliances with similar waveform is validated by comparing it with the other two methods.
Highlights
According to statistics, the electricity consumption recorded in China was 2,199.4 billion kWh in the first quarter of 2018
Compared with intrusive load monitoring (ILM), the non-intrusive load monitoring (NILM) method is more attractive, as the former can discern devices from the aggregated data acquired from a single point of measurement [2]
Du et al [4] presented a Finite State Machine (FSM) method that converted a long-term load manipulation waveform into a quantized state sequence to extract the Root Mean Square (RMS) of the current and time point associated with each state
Summary
The electricity consumption recorded in China was 2,199.4 billion kWh in the first quarter of 2018. Load operation leads to an extended data sample time, making it more complex to implement Several methods, such as event detection [5], unique signature definition [6], neural-network-based signature recognition, [7] and convolutional neural network [8], are proposed to disaggregate energy and classify appliances based on state event (steady-state and transient) [9] or voltage–current (VI) image [10]. A type of reconstructed VI image is proposed based on the particle swarm optimization algorithm This method creates new features according to the inherent characteristics of each appliance while preserving the original features. The performance of this novel approach is tested using two datasets
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