基于EEMD-LSTM-WOA的风速预测混合模型

风能因其安全、可再生、环保等显著优势而受到世界各国的重视,为了准确预测风速时间序列,本文使用宁夏回族自治区麻黄山共17,376条风速数据,提出了一种基于集合经验模态分解(EEMD)、长短期记忆网络(LSTM)、鲸鱼优化算法(WOA)组成的混合风速预测模型,并且与BP神经网络、CEEMDAN-LSTM-PSO (完全集合经验模态分解–长短期记忆网络–粒子群优化算法)、EMD-LSTM-RIME (经验模态分解–长短期记忆网络–霜冰优化算法)等模型进行对比实验,结果表明本文提出的EEMD-LSTM-WOA模型有着更稳定、更准确的预测性能。之后对EEMD-LSTM-WOA模型进行消融试验,结果显示去掉EEMD分解后,RMSE和MAPE分别增加了203.97%和187.47%,表明EEMD极大提升了整个模型的准确性和稳定性;去掉鲸鱼优化算法后,模型的RMSE和MAPE分别增加了78.34%和74.93%,说明最优化方法对整个模型的准确性和稳定性也有较大的促进作用。Wind energy has garnered global attention due to its notable advantages, including safety, renewability, and environmental friendliness. To accurately predict wind speed time series, this paper utilizes 17,376 wind speed data points from Ma Huang Mountain in the Ningxia Hui Autonomous Region. We propose a hybrid wind speed prediction model that combines ensemble empirical mode decomposition (EEMD), long short-term memory network (LSTM), and whale optimization algorithm (WOA). Comparative experiments were conducted with models such as BP neural network, CEEMDAN-LSTM-PSO (complete ensemble empirical mode decomposition-long short-term memory network-particle swarm optimization), and EMD-LSTM-RIME (empirical mode decomposition-long short-term memory network-Rimoglio optimization algorithm). The results indicate that our proposed EEMD-LSTM-WOA model exhibits more stable and accurate prediction performance. Subsequently, ablation experiments were performed on the EEMD-LSTM-WOA model. The findings revealed that upon removing EEMD decomposition, RMSE and MAPE increased by 203.97% and 187.47%, respectively, highlighting the significant enhancement of EEMD in boosting the model’s accuracy and stability. Similarly, after eliminating the whale optimization algorithm, the RMSE and MAPE of the model rose by 78.34% and 74.93%, respectively, indicating that this optimization method significantly contributes to the model's accuracy and stability.

兰州市空气质量指数预测——基于LSTM的混合模型研究

本文旨在通过构建EEMD-GWO-LSTM混合模型,对兰州市空气质量指数(AQI)进行准确预测。兰州市作为中国西北地区重要的工业基地和交通枢纽,其空气质量受工业排放、交通污染及地理环境等因素影响,常年处于高污染等级。针对兰州市AQI监测数据突变性强的特点,文章首先对数据进行预处理,包括填补缺失值、归一化处理等,以提高数据质量。随后,采用集合经验模态分解(EEMD)对数据进行分解,提取出本征模态函数(IMF),并利用灰狼优化算法(GWO)对长短期记忆网络(LSTM)模型的超参数进行优化,以提高预测精度。实验结果表明,EEMD-GWO-LSTM混合模型在预测兰州市AQI时,相较于单一模型和其他混合模型,具有更低的均方根误差(RMSE)和更高的决定系数(R2),显示出更好的预测性能。最后,文章提出了增加监测站点、采用先进技术提高监测频率、跨区域合作及数据公开共享等建议,以促进兰州市空气质量的持续改善和预测模型的进一步优化。This paper aims to accurately predict the Air Quality Index (AQI) of Lanzhou City by constructing a mixed model of EEMD-GGO-LSTM. Lanzhou City is an important industrial base and transportation hub in northwest China. Its air quality is affected by industrial emissions, traffic pollution, and the geographical environment, and it always has a high pollution level. In view of the strong mutability of AQI monitoring data in Lanzhou City, the paper first preprocessed the data, including filling in missing values and normalization processing, so as to improve the data quality. Then, the data is decomposed by ensemble empirical Mode decomposition (EEMD), extracting the intrinsic mode function (IMF), and the hyperparameters of the long short-term memory network (LSTM) model are optimized by Grey Wolf optimization algorithm (GWO) to improve the prediction accuracy. The experimental results show that the EEMD-GGO-LSTM mixed model has a lower root-mean-square error (RMSE) and higher determination coefficient (R2) when predicting AQI in Lanzhou compared with the single model and other mixed models, showing better prediction performance. Finally, the paper puts forward some suggestions, such as increasing monitoring stations, using advanced technology to improve monitoring frequency, cross-regional cooperation and open data sharing, so as to promote the continuous improvement of Lanzhou air quality and further optimization of a prediction model.