基于大数据平台的SO_(2)排放GWO-N-BEATS预测算法

SO_(2) Emission Prediction by GWO-N-BEATS Algorithm based on Big Data Platform

为了更精确地预测SO_(2)排放质量浓度,解决非线性随机预测问题,提出了一种基于随机森林特征选择的GWO-N-BEATS算法。通过随机森林算法筛选输入参数的特征,使用灰狼优化算法对N-BEATS算法的超参数进行优化;与长短期记忆网络(Long Short-Term Memory, LSTM)、门控循环神经网络(Gated Recurrent Unit, GRU)以及N-BEATS算法对比分析,验证了GWO-N-BEATS算法的有效性。将本算法应用于某大型电网公司大数据平台,探索了复杂智能算法在大数据平台上开展污染物排放预测的可行性。研究结果表明,相较于长短期记忆网络、门控循环神经网络和N-BEATS方法,GWO-N-BEATS算法预测误差更小,其中平均绝对百分比误差MAPE为1.50%,相对均方误差RMSE为0.42,平均绝对误差MAE为0.33,决定系数R^(2)为0.97。

To predict SO_(2)emission mass concentration more accurately and to solve the nonlinear stochastic 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 optimized 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 algorithm 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 plaform.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 R^(2)is 0.97.