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- W2897536392 abstract "Network-based wind speed forecasting has played an important role in the power system. The network parameters optimization is an important issue, and different optimization algorithms are believed to result in different forecasting accuracies. In this paper, six network parameters optimization algorithms, including Gradient descent, Momentum, AdaGrad, RMSprop, Adam, and Adadelta, are implemented and compared in the application of wind speed forecasting. As a case study, this paper uses a wind speed data obtained from Ningxia, China. The performance is evaluated by three metrics, namely, mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The experiment results show that, Adam algorithm and RMSprop algorithm achieve better forecasting accuracy and less training time than the other optimization algorithms. This study can be a guide to the selection of optimization algorithms on wind speed forecasting problems for researchers." @default.
- W2897536392 created "2018-10-26" @default.
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- W2897536392 date "2018-07-01" @default.
- W2897536392 modified "2023-09-24" @default.
- W2897536392 title "On Comparing Six Optimization Algorithms for Network-based Wind Speed Forecasting" @default.
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- W2897536392 doi "https://doi.org/10.23919/chicc.2018.8482567" @default.
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