Prediction Method for Carbon Emissions of Power on User Side
In order to help the power industry apportion the responsibility of carbon emissions and optimize the carbon emission reduction strategy,the prediction of carbon emissions related to power on the user side was studied.According to the method of predicting the power consumption first and then the electric carbon conversion,the carbon emission related to the user side can be predicted.The power consumption prediction of users is the core.A multi-modal embedded recurrent neural network was designed to model the short-term dependence of power consumption sequence considering the multiple influencing factors of power consumption;A historical attention mechanism was pro-posed to capture the periodicity factors in the power consumption sequence by considering the periodicity of users'power consumption habits.The experimental results show that the performance of the proposed method is significantly better than that of some commonly used power consumption prediction methods,which is helpful for the accurate pre-diction of power-related carbon emissions on the user side.
User sideCarbon emissionPower consumption predictionRecurrent neural networkAttention mech-anismPeriodicity
NETL
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