Combined Prediction Energy Model at Software Architecture Level

Abstract

Accurate prediction of software energy consumption is of great significance for the sustainable development of the environment. In order to overcome the limitations of a single prediction method and further improve the prediction accuracy, a combined prediction energy model of adaboost algorithm and RBF (radial basis function) neural network at software architecture level is proposed. Firstly, three kinds of energy prediction models are established by polynomial regression, support vector machine and neural network respectively. Secondly, the RBF neural network is used to nonlinear combine the predicted values of the above three models. Finally, RBF integrated by adaboost algorithm is used as high-precision prediction of energy consumption. Experimental results show that the prediction accuracy of the combined prediction model is higher than that of the single model.