在未来高渗透率风电场景下,超短期风电功率预测研究对于实现电力系统优化运行具有重要意义。为此,提出一种基于GWO-CNN-BiLSTM的超短期风电预测方法。首先,搭建基于卷积神经网络(convolutional neural network,CNN)与双向长短期记忆神...在未来高渗透率风电场景下,超短期风电功率预测研究对于实现电力系统优化运行具有重要意义。为此,提出一种基于GWO-CNN-BiLSTM的超短期风电预测方法。首先,搭建基于卷积神经网络(convolutional neural network,CNN)与双向长短期记忆神经网络(bidirectional long short term memory,BiLSTM)的组合模型,然后,为提升风电预测结果的精度,通过灰狼优化算法(grey wolf optimizer,GWO)对组合模型进行优化,使该组合模型参数能实时适应风电历史数据。最后,仿真结果验证了所提出方法的有效性和优越性。展开更多
Soil water erosion(SWE)is an important global hazard that affects food availability through soil degradation,a reduction in crop yield,and agricultural land abandonment.A map of soil erosion susceptibility is a first ...Soil water erosion(SWE)is an important global hazard that affects food availability through soil degradation,a reduction in crop yield,and agricultural land abandonment.A map of soil erosion susceptibility is a first and vital step in land management and soil conservation.Several machine learning(ML)algorithms optimized using the Grey Wolf Optimizer(GWO)metaheuristic algorithm can be used to accurately map SWE susceptibility.These optimized algorithms include Convolutional Neural Networks(CNN and CNN-GWO),Support Vector Machine(SVM and SVM-GWO),and Group Method of Data Handling(GMDH and GMDH-GWO).Results obtained using these algorithms can be compared with the well-known Revised Universal Soil Loss Equation(RUSLE)empirical model and Extreme Gradient Boosting(XGBoost)ML tree-based models.We apply these methods together with the frequency ratio(FR)model and the Information Gain Ratio(IGR)to determine the relationship between historical SWE data and controlling geo-environmental factors at 116 sites in the Noor-Rood watershed in northern Iran.Fourteen SWE geo-environmental factors are classified in topographical,hydro-climatic,land cover,and geological groups.We next divided the SWE sites into two datasets,one for model training(70%of the samples=81 locations)and the other for model validation(30%of the samples=35 locations).Finally the model-generated maps were evaluated using the Area under the Receiver Operating Characteristic(AU-ROC)curve.Our results show that elevation and rainfall erosivity have the greatest influence on SWE,while soil texture and hydrology are less important.The CNN-GWO model(AU-ROC=0.85)outperformed other models,specifically,and in order,SVR-GWO=GMDH-GWO(AUC=0.82),CNN=GMDH(AUC=0.81),SVR=XGBoost(AUC=0.80),and RULSE.Based on the RUSLE model,soil loss in the Noor-Rood watershed ranges from 0 to 2644 t ha^(-1)yr^(-1).展开更多
文摘在未来高渗透率风电场景下,超短期风电功率预测研究对于实现电力系统优化运行具有重要意义。为此,提出一种基于GWO-CNN-BiLSTM的超短期风电预测方法。首先,搭建基于卷积神经网络(convolutional neural network,CNN)与双向长短期记忆神经网络(bidirectional long short term memory,BiLSTM)的组合模型,然后,为提升风电预测结果的精度,通过灰狼优化算法(grey wolf optimizer,GWO)对组合模型进行优化,使该组合模型参数能实时适应风电历史数据。最后,仿真结果验证了所提出方法的有效性和优越性。
基金a grant from the Ferdowsi University of Mashhad(Grant No.FUM-140010163611).
文摘Soil water erosion(SWE)is an important global hazard that affects food availability through soil degradation,a reduction in crop yield,and agricultural land abandonment.A map of soil erosion susceptibility is a first and vital step in land management and soil conservation.Several machine learning(ML)algorithms optimized using the Grey Wolf Optimizer(GWO)metaheuristic algorithm can be used to accurately map SWE susceptibility.These optimized algorithms include Convolutional Neural Networks(CNN and CNN-GWO),Support Vector Machine(SVM and SVM-GWO),and Group Method of Data Handling(GMDH and GMDH-GWO).Results obtained using these algorithms can be compared with the well-known Revised Universal Soil Loss Equation(RUSLE)empirical model and Extreme Gradient Boosting(XGBoost)ML tree-based models.We apply these methods together with the frequency ratio(FR)model and the Information Gain Ratio(IGR)to determine the relationship between historical SWE data and controlling geo-environmental factors at 116 sites in the Noor-Rood watershed in northern Iran.Fourteen SWE geo-environmental factors are classified in topographical,hydro-climatic,land cover,and geological groups.We next divided the SWE sites into two datasets,one for model training(70%of the samples=81 locations)and the other for model validation(30%of the samples=35 locations).Finally the model-generated maps were evaluated using the Area under the Receiver Operating Characteristic(AU-ROC)curve.Our results show that elevation and rainfall erosivity have the greatest influence on SWE,while soil texture and hydrology are less important.The CNN-GWO model(AU-ROC=0.85)outperformed other models,specifically,and in order,SVR-GWO=GMDH-GWO(AUC=0.82),CNN=GMDH(AUC=0.81),SVR=XGBoost(AUC=0.80),and RULSE.Based on the RUSLE model,soil loss in the Noor-Rood watershed ranges from 0 to 2644 t ha^(-1)yr^(-1).
