Intelligent big data mining based on multi-modal Markov decision for ubiquitous Internet of Things in electricity
Aiming at the problem of complex structure,diverse and uncertain data of the ubiquitous Internet of Things in electricity,an intelligent big data mining method based on multi-modal Markov decision for ubiquitous Internet of Things in electricity was proposed.This method constructed a Markov decision algorithm based on maximum entropy for the fault diagnosis and load forecast of ubiquitous Internet of Things,featuring small demand for labeled samples and high confidence.This method extracted power grid data characteristics by combining electrical quantity information and switch quantity information,and could make full use of multi-modal data samples.Simulation analysis and experimental results show that the as-proposed method can effectively identify grid faults including information distortion in comparison with traditional methods,and can effectively improve the accuracy of grid fault diagnosis and the precision of grid load forecasting.
power gridubiquitous Internet of Things in electricityMarkov decisionmaximum entropyfault diagnosisload forecastingelectrical quantityswitch quantity
万方数据
维普
