Short-term Load Prediction of Cloud Computing Based on Fuzzy Information Granulation SVM
Jian Wang, Yuanyuan Zhang · Journal of Physics Conference Series · 2019
In order to predict the short-term load variation range and trend of cloud computing, this paper proposed a prediction model based on information granulation support vector machine (IGSVM). Taking the historical load value as a sample to do simulation training, through Gravitational Search Algorithm (GSA) to optimize the parameters of SVM, and make regression prediction to three parameters of triangular fuzzy particles, Low, R and Up, to obtain the variation range and trend of short-term load. The result is consistent with the actual situation, which verifies the validity of the model and provides the basis for actual operation and maintenance.