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Non-intrusive load monitoring using factorial hidden markov model based on adaptive density peak clustering
Highlights An adaptive power clustering process is proposed to determine the working states of an appliance by analyzing its active power sequences. .Compared with the traditional HMM-based models, the proposed model ADPC-FHMM could automatically determine the appliances' working states by using the results from the adaptive clustering. .The proposed method achieves a 10% and 12% improvement in the metric of F-measure on the REDD and AMPds datasets, comparing with the second best algorithm.
Abstract With the goal of achieving carbon neutrality, the technology of Non-Intrusive Load Monitoring (NILM) has gained widespread attention as an efficient energy-saving way. Hidden Markov Model (HMM) based methods are popular in the field of NILM, yet the traditional HMM-based methods need prior knowledge of the appliance’s working states. In this paper, we propose an Adaptive Density Peak Clustering (ADPC) algorithm that could automatically determine the working states of appliance based on its power consumption. Then we combine the ADPC and Factorial Hidden Markov Model (FHMM) to create an Adaptive Density Peak Clustering-Factorial Hidden Markov Model (ADPC-FHMM), which reduces the dependence of prior information and is more applicable in real world scenarios. Case studies are conducted on two publicly available datasets, and the results show that the proposed model outperforms its counterparts on the metrics of Accuracy, F-measure and MAE.
Non-intrusive load monitoring using factorial hidden markov model based on adaptive density peak clustering
Highlights An adaptive power clustering process is proposed to determine the working states of an appliance by analyzing its active power sequences. .Compared with the traditional HMM-based models, the proposed model ADPC-FHMM could automatically determine the appliances' working states by using the results from the adaptive clustering. .The proposed method achieves a 10% and 12% improvement in the metric of F-measure on the REDD and AMPds datasets, comparing with the second best algorithm.
Abstract With the goal of achieving carbon neutrality, the technology of Non-Intrusive Load Monitoring (NILM) has gained widespread attention as an efficient energy-saving way. Hidden Markov Model (HMM) based methods are popular in the field of NILM, yet the traditional HMM-based methods need prior knowledge of the appliance’s working states. In this paper, we propose an Adaptive Density Peak Clustering (ADPC) algorithm that could automatically determine the working states of appliance based on its power consumption. Then we combine the ADPC and Factorial Hidden Markov Model (FHMM) to create an Adaptive Density Peak Clustering-Factorial Hidden Markov Model (ADPC-FHMM), which reduces the dependence of prior information and is more applicable in real world scenarios. Case studies are conducted on two publicly available datasets, and the results show that the proposed model outperforms its counterparts on the metrics of Accuracy, F-measure and MAE.
Non-intrusive load monitoring using factorial hidden markov model based on adaptive density peak clustering
Wu, Zhao (author) / Wang, Chao (author) / Peng, Wenxiong (author) / Liu, Weihua (author) / Zhang, Huaiqing (author)
Energy and Buildings ; 244
2021-04-08
Article (Journal)
Electronic Resource
English
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