LAUSR

While the demand for quality power supply is increasing, the traditional power grid cannot meet with the users’ demand. Smart grid has become a new solution to quality power supply for its improved information sharing and intelligent power distribution. In the smart grid, the power load forecasting is the key to power distribution. So, this paper proposed a multiobjective deep belief ensemble network based on ensemble empirical mode decomposition (EEMD) to realize the high diversity and accuracy of the forecasting model. This method decomposes the data based on EEMD to reduce the complexity of the data and classifies the data with sample entropy, using a multiobjective evolutionary algorithm based on decomposition optimization algorithm to optimize network parameters with accuracy and diversity as objective functions. The comparison of this method with other intelligent prediction methods such as deep belief network (DBN) and deep belief network based on ensemble empirical mode decomposition (EEMD-DBN) on multiple datasets shows that this method can guarantee accuracy and has good generalization characteristics.