Sample data selection method for improving the prediction accuracy of the heating energy consumption

Back propagation neural network (BPNN) models and multiple linear regression (MLR) models are widely used to predict heating energy consumption. To improve the prediction accuracies for the BPNN and MLR models, we propose a novel sample data selection method (SDSM) combining the similar days selection with the virtual samples generation. First, a grey correlation method integrated with an entropy weight method is given to optimize the similar days selection. Then virtual samples are generated by Gaussian distribution function based on the similar samples. Finally, a new sample set (including similar and virtual samples) is obtained, and then, it is regarded as the input variable for the BPNN and MLR models. The results show that training errors and prediction errors are obviously reduced in the developed BPNN model. Although the prediction accuracy of the developed MLR model is improved by different degrees, the coefficient of determination obtained in the regression fitting process is poor. It is proved that the novel SDSM is applicable only for the BPNN model, but not for the MLR model.