Abstract:Background
Substation equipment temperature is difficult to achieve accurate prediction because of its typical seasonality, periodicity and instability, complex working environment and less available characteristic information.
Methods
To overcome these difficulties, a substation equipment temperature prediction method is proposed based on multivariate information fusion, convolutional neural network (CNN) and gated recurrent unite (GRU) in… Show more
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