Abstract
In recent years, Smart Grids have been developing globally. Since smart meters only acquire low-frequency data, non-intrusive load monitoring technology using the signature extracted from high-frequency data needs an additional measurement device to be installed, so it is not suitable for promotion to the smart grid environment. However, methods using low-frequency features are poorly-suited when several appliances are switched on at the same time, or devices with similar power values are used. In response to these problems, this paper proposes a load disaggregation method based on the power consumption patterns of appliances, combining an improved mathematical optimization model and optimized bird swarm algorithm (OBSA) for load disaggregation. Experiments show that the method can effectively identify the operating states of appliances, and deal with situations in which multiple instruments have similar power characteristics or are simultaneously switching. The performance comparison proves that the improved model is more efficient than the traditional active and reactive power (PQ) optimization model in load disaggregation performance and computation time, and also verifies the robustness of the proposed method and the convergence of OBSA. As an inexpensive method without extra measurement hardware installed, the process is suitable for large-scale applications in smart grids.
Highlights
Since the energy crisis and environmental problems caused by greenhouse emissions have become critical issues [1,2], smart grids have been developing globally
Non-intrusive load monitoring (NILM) technology, called load disaggregation, derives the electricity detail of each appliance from the total power consumption measured at the service entry by using pattern recognition technologies and machine learning algorithms
Aiming at the problems existing in the previous research of the non-intrusive load monitoring (NILM) methods, this paper proposed a load disaggregation method based on a power consumption pattern for low sampling data
Summary
Since the energy crisis and environmental problems caused by greenhouse emissions have become critical issues [1,2], smart grids have been developing globally. Non-intrusive load monitoring (NILM) technology, called load disaggregation, derives the electricity detail of each appliance from the total power consumption measured at the service entry by using pattern recognition technologies and machine learning algorithms. The NILM methods that utilize high-frequency signatures (frequency higher than 1 kHz) have achieved excellent performance in load identification. Since low-frequency signatures are readily available from smart meters, the load disaggregation methods using low-frequency features are an essential development trend of NILM. Aiming at the problems existing in the previous research of the NILM methods, this paper proposed a load disaggregation method based on a power consumption pattern for low sampling data. The proposed method exploited the power consumption patterns signature of appliances during operation, and presented an improved load decomposition model.
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