Electricity Activity Chain Extraction and Behavior Reasoning Based on Dynamic Bayesian Network Model
DOI:
https://doi.org/10.13052/spee1048-5236.4511Keywords:
Activity Chain, machine learning, Dynamic Bayesian Network, electricity behavior reasoningAbstract
In order to better understand household electricity consumption behavior and guide power companies to flexibly adjusting power resources, this study mapped the residential living habits into the electrical appliance activity chains, used the Dynamic Bayesian Network(DBN) model to construct the correlation between different electrical appliance activities, then extracted the key chains to infer the electrical appliance activity state and identify the characteristics of household electricity consumption behavior. The results show that the proposed method can significantly improve the accuracy of the electricity activity predicting, and realize the accurate cognition of household electricity consumption behavior to a certain extent.
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