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Improving Performance of Recurrent Neural Networks Using Simulated Annealing for Vertical Wind Speed Estimation

Energy Engineering 2023年第4期120卷 775-789页

作者： Shafiqur Rehman HilalH.Nuha Ali Al Shaikhi satria akbar Mohamed Mohandes Interdisciplinary Research Center for Renewable Energy and Power Systems(IRC-REPS) KFUPMDhahran31261Saudi Arabia HUMIC Engineering School of ComputingTelkom UniversityBandung40257Indonesia Electrical Engineering Department King Fahd University of Petroleum&Minerals(KFUPM)Dhahran31261Saudi Arabia

An accurate vertical wind speed(WS)data estimation is required to determine the potential for wind farm *** general,the vertical extrapolation of WS at different heights must consider different parameters fromdifferent locations,such as wind shear coefficient,roughness length,and atmospheric *** novelty presented in this article is the introduction of two steps optimization for the Recurrent Neural Networks(RNN)model to estimate WS at different heights using measurements from lower *** first optimization of the RNN is performed to minimize a differentiable cost function,namely,mean squared error(MSE),using the Broyden-Fletcher-Goldfarb-Shanno ***,the RNN is optimized to reduce a non-differentiable cost function using simulated annealing(RNN-SA),namely mean absolute error(MAE).Estimation ofWS vertically at 50 m height is done by training RNN-SA with the actualWS data a 10–40 m *** estimatedWS at height of 50 m and the measured WS at 10–40 heights are further used to train RNN-SA to obtain WS at 60 m *** procedure is repeated continuously until theWS is estimated at a height of 180 *** RNN-SA performance is compared with the standard RNN,Multilayer Perceptron(MLP),Support Vector Machine(SVM),and state of the art methods like convolutional neural networks(CNN)and long short-term memory(LSTM)networks to extrapolate theWS *** estimated values are also compared with realWS dataset acquired using LiDAR and tested using four error metrics namely,mean squared error(MSE),mean absolute percentage error(MAPE),mean bias error(MBE),and coefficient of determination(R2).The numerical experimental results show that the MSE values between the estimated and actualWS at 180mheight for the RNN-SA,RNN,MLP,and SVM methods are found to be 2.09,2.12,2.37,and 2.63,respectively.

关键词： Vertical wind speed estimation recurrent neural networks simulated annealing multilayer perceptron support vector machine

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