Predicting Electrical Energy Output by Using Granular Computing Based Neuro-Fuzzy Modeling Method

Accurate prediction of electrical energy output can save more cost and attain maximize profits, so it is quite important to establish a model to predict the electrical energy output of the plant. Granular Computing (GrC) is a new data mining method. By combining these objects with similar characteristics and selecting appropriate granularity, CrC can seek a better solution, in which the core information can be extracted while reducing redundant information and the computing complexity. In this paper, the GrC method is used to extract relational information and data characteristics from a complex multidimensional data set. The extracted information is further utilized to model an initial fuzzy system, whose parameters are optimized by using the fuzzy neural network learning methods. The proposed method can not only reduce the solving complexity, but also achieve the interpretability of fuzzy logic. Meanwhile, the accuracy of modeling can be improved due to the integrating of fuzzy neural networks. Finally, the proposed method is utilized to construct a predictive model of electrical power output of a power plant. The comparison via the predicting accuracy demonstrate the superiority of the proposed method.