A novel improved model for green building energy consumption prediction based on time-series analysis

Abstract

The development and popularization of renewable energy is necessary. The application of renewable energy technology in buildings is an important research direction. And the prediction of renewable energy consumption in this direction is an essential research content. In view of this, a buildings energy consumption prediction model of renewable energy based on time-series analysis and Support Vector Machine (SVM) is proposed. The performance test of this model shows that its loss value is as low as 1.5% in training set, and the loss value is 4.1% in test set. In addition, it shows the highest accuracy rate of 95.5% in the neural network accuracy test, which is significantly higher than the comparison of traditional algorithms. About the overall energy consumption prediction ability of the model, the experimental results showed that the lowest error of the energy consumption prediction model was 2.3%, the average relative error of the traditional SVM model in the same data set was 6.8%, and the chaotic time-series model was 4.1%. Compared with the traditional models currently used, the prediction ability of the energy consumption prediction model had been greatly improved, and it had the potential to be put into practical application.