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Unsupervised Learning for Non-intrusive Load Monitoring in Smart Grid Based on Spiking Deep Neural Network
This paper investigates the intelligent load monitoring problem with applications to practical energy management scenarios in smart grids. As one of the critical components for paving the way to smart grids' success, an intelligent and feasible non-intrusive load monitoring (NILM) algorithm is urgently needed. However, most recent researches on NILM have not dealt with practical problems when applied to power grid, i.e., ① limited communication for slow-change systems; ② requirement of low-cost hardware at the users' side; and ③ inconvenience to adapt to new households. Therefore, a novel NILM algorithm based on biology-inspired spiking neural network (SNN) has been developed to overcome the existing challenges. To provide intelligence in NILM, the developed SNN features an unsupervised learning rule, i.e., spike-time dependent plasticity (STDP), which only requires the user to label one instance for each appliance while adapting to a new household. To upgrade the feasibility in NILM, the designed spiking neurons mimic the mechanism of human brain neurons that can be constructed by a resistor-capacitor (RC) circuit. In addition, a distributed computing system has been designed that divides the SNN into two parts, i.e., smart outlets and local servers. Since the information flows as sparse binary vectors among spiking neurons in the developed SNN-based NILM, the high-frequency data can be easily compressed as the spike times, and are sent to the local server with limited communication capability, whereas it is unable to handle the traditional NILM. Finally, a series of experiments are conducted using a benchmark public dataset. Meanwhile, the effectiveness of developed SNN-based NILM can be demonstrated through comparisons with other emerging NILM algorithms such as the convolutional neural networks.
Unsupervised Learning for Non-intrusive Load Monitoring in Smart Grid Based on Spiking Deep Neural Network
This paper investigates the intelligent load monitoring problem with applications to practical energy management scenarios in smart grids. As one of the critical components for paving the way to smart grids' success, an intelligent and feasible non-intrusive load monitoring (NILM) algorithm is urgently needed. However, most recent researches on NILM have not dealt with practical problems when applied to power grid, i.e., ① limited communication for slow-change systems; ② requirement of low-cost hardware at the users' side; and ③ inconvenience to adapt to new households. Therefore, a novel NILM algorithm based on biology-inspired spiking neural network (SNN) has been developed to overcome the existing challenges. To provide intelligence in NILM, the developed SNN features an unsupervised learning rule, i.e., spike-time dependent plasticity (STDP), which only requires the user to label one instance for each appliance while adapting to a new household. To upgrade the feasibility in NILM, the designed spiking neurons mimic the mechanism of human brain neurons that can be constructed by a resistor-capacitor (RC) circuit. In addition, a distributed computing system has been designed that divides the SNN into two parts, i.e., smart outlets and local servers. Since the information flows as sparse binary vectors among spiking neurons in the developed SNN-based NILM, the high-frequency data can be easily compressed as the spike times, and are sent to the local server with limited communication capability, whereas it is unable to handle the traditional NILM. Finally, a series of experiments are conducted using a benchmark public dataset. Meanwhile, the effectiveness of developed SNN-based NILM can be demonstrated through comparisons with other emerging NILM algorithms such as the convolutional neural networks.
Unsupervised Learning for Non-intrusive Load Monitoring in Smart Grid Based on Spiking Deep Neural Network
Zejian Zhou (Autor:in) / Yingmeng Xiang (Autor:in) / Hao Xu (Autor:in) / Yishen Wang (Autor:in) / Di Shi (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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