Novel grey models for the trend forecast of Taiwan waste gas apparatus

This paper proposes a novel optimiser to refine the prediction accuracy of grey models (GM). This design consists of a dynamic genetic algorithm (GA) controller that uses a weighting optimiser with the exponentially weighted moving average (EWMA) method to search for an optimal background value of GM optimisation (EGM). The contributions of our research are that these two new EWMA-GM models can formulate a dynamic prediction sequence using a novel GA mechanism, which can then be used to effectively predict Taiwan's industry pollution. Finally, a case study is provided to illustrate the proposed approach. Our simulations are designed by four different GM models (GM, EGM, RGM, and REGM). Comparisons for predicting the greenhouse outputs of Taiwan were performed. The simulation outcomes show that EGM and REGM models perform better than GM and RGM models. The REGM model performed the best and efficiently improved the accuracy of the GM(1,1) model. This study can reveal Taiwan's pollution management status and provide a beneficial option for the presentation of environmental strategies.