SO2 Emission Prediction by GWO-N-BEATS Algorithm based on Big Data Platform
To predict SO2 emission mass concentration more accurately and to solve the nonlinear sto-chastic prediction problem,a novel grey wolf optimization(GWO)deep learning architecture N-BEATS algorithm based on random forest feature selection was proposed.The features of the input parameters were screened by the random forest algorithm,and the hyperparameters of the N-BEATS model were opti-mized using the GWO algorithm;the effectiveness of the proposed algorithm was verified by comparing it with long short-term memory network(LSTM),gated recurrent unit(GRU)and N-BEATS.The algo-rithm was applied to a large power grid company's big data platform to explore the feasibility of complex intelligent algorithms to carry out pollutant emission prediction on a big data platform.The results show that the GWO-N-BEATS algorithm has less error compared to LSTM,GRU and N-BEATS methods,where MAPE is 1.50%,RMSE is 0.42,MAE is 0.33,and R2 is 0.97.
random forestfeature selectiongrey wolf optimization(GWO)algorithmbig data plat-formN-BEATSSO2 prediction