Abstract: In the framework of a cyber-physical system, the amount of data in an energy internet needs to process is massive, and it is very difficult to extract knowledge and analyze the associated characteristics among data. Based on the mutual information （MI） theory, an information fusion structure with a‘data-characteristics-decision’three-layer framework is applied to the massive monitoring data of energy internet, and a multi-layer mode data fusion scheme is then presented. The MI method can measure the correlation between condition attributes and decision attributes and eliminate the redundant features, and then to extract rules and form knowledge. First, the MI method is used to determine the correlation degrees among massive monitored data, and extract the associated features in the procedure of data preprocessing. Then, the multi-layer feedforward neural network （MLFNN） is used for fusion of decision-making with massive data. The proposed method is then combined with the well-known MapReduce （MP） model in the field of parallel computing for large-scale data sets so as to figure out a "Mutual Information-Multiple-layer Feedforward Neural Network-MapReduce" （3M） methodological framework for the fusion of large amount of data. Finally, the output power forecasting of a wind farm is served for demonstrating the presented method, whose calculation results show that the proposed method is of better forecasting accuracy and computational efficiency, compared with the traditional variable selection method.

Key words: energy internet, big data, information fusion, mutual information（MI）