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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.
Huijuan Wang; Wenrong Yang; Tingyu Chen; Qingxin Yang. An Optimal Load Disaggregation Method Based on Power Consumption Pattern for Low Sampling Data. Sustainability 2019, 11, 251 .
AMA StyleHuijuan Wang, Wenrong Yang, Tingyu Chen, Qingxin Yang. An Optimal Load Disaggregation Method Based on Power Consumption Pattern for Low Sampling Data. Sustainability. 2019; 11 (1):251.
Chicago/Turabian StyleHuijuan Wang; Wenrong Yang; Tingyu Chen; Qingxin Yang. 2019. "An Optimal Load Disaggregation Method Based on Power Consumption Pattern for Low Sampling Data." Sustainability 11, no. 1: 251.
Non-intrusive load monitoring (NILM), monitoring single-appliance consumption level by decomposing the aggregated energy consumption, is a novel and economic technology that is beneficial to energy utilities and energy demand management strategies development. Hardware costs of high-frequency sampling and algorithm’s computational complexity hampered NILM large-scale application. However, low sampling data shows poor performance in event detection when multiple appliances are simultaneously turned on. In this paper, we contribute an iterative disaggregation approach that is based on appliance consumption pattern (ILDACP). Our approach combined Fuzzy C-means clustering algorithm, which provide an initial appliance operating status, and sub-sequence searching Dynamic Time Warping, which retrieves single energy consumption based on the typical power consumption pattern. Results show that the proposed approach is effective to accurately disaggregate power consumption, and is suitable for the situation where different appliances are simultaneously operated. Also, the approach has lower computational complexity than Hidden Markov Model method and it is easy to implement in the household without installing special equipment.
Huijuan Wang; Wenrong Yang. An Iterative Load Disaggregation Approach Based on Appliance Consumption Pattern. Applied Sciences 2018, 8, 542 .
AMA StyleHuijuan Wang, Wenrong Yang. An Iterative Load Disaggregation Approach Based on Appliance Consumption Pattern. Applied Sciences. 2018; 8 (4):542.
Chicago/Turabian StyleHuijuan Wang; Wenrong Yang. 2018. "An Iterative Load Disaggregation Approach Based on Appliance Consumption Pattern." Applied Sciences 8, no. 4: 542.