基于大数据平台的SO2排放GWO-N-BEATS预测算法
SO2 Emission Prediction by GWO-N-BEATS Algorithm based on Big Data Platform
曾庆华 1冉鹏 2董坤 3刘旭3
作者信息
- 1. 国网湖南省电力有限公司电力科学研究院高效清洁发电技术湖南省重点实验室,湖南长沙 410007
- 2. 华北电力大学河北省低碳高效发电技术重点实验室,河北保定 071003
- 3. 华北电力大学能源动力与机械工程学院,河北保定 071003
- 折叠
摘要
为了更精确地预测SO2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R2为0.97.
Abstract
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.
关键词
随机森林/特征选择/灰狼优化算法/大数据平台/N-BEATS/SO2预测Key words
random forest/feature selection/grey wolf optimization(GWO)algorithm/big data plat-form/N-BEATS/SO2 prediction引用本文复制引用
出版年
2024
NETL
NSTL
万方数据