Prediction of wind speed with non-linear autoregressive (NAR) neural networks

Karasu, Seçkin; Altan, Aytaç; Saraç, Zehra; Hacıoğlu, Rıfat

doi:10.1109/siu.2017.7960507

Cited by 31 publications

(12 citation statements)

References 10 publications

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“…The wind turbine industry is already benefiting from the wind forecast for wind farm planning, operation, and grid integration [34]. Numerous forecasting techniques for wind and wave are present in the literature, ranging from long-term (3 days -1 week or more) to short-term(few seconds -30 minutes) prediction horizons [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50]. However, the controller response time for FOWT falls in the short-term prediction horizon category [51,52].…”

Section: Floating Platform and Associated Problemsmentioning

confidence: 99%

A synthesis of feasible control methods for floating offshore wind turbine system dynamics

Shah

Meng

et al. 2021

Renewable and Sustainable Energy Reviews

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Section: Floating Platform and Associated Problemsmentioning

confidence: 99%

A synthesis of feasible control methods for floating offshore wind turbine system dynamics

Shah

Meng

et al. 2021

Renewable and Sustainable Energy Reviews

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“…e NAR neural network is used to predict the dynamic change of wheel diameter and therefore to predict the wheel wear high-speed trains. In this [16] study, the wind speed prediction model is created. Using the one-minute time series, the prediction of the next wind speed is performed with the NAR neural network model.…”

Section: Related Workmentioning

confidence: 99%

Blood Glucose Level Prediction of Diabetic Type 1 Patients Using Nonlinear Autoregressive Neural Networks

Asad

Qamar

Abbas

2021

Journal of Healthcare Engineering

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Diabetes type 1 is a chronic disease which is increasing at an alarming rate throughout the world. Studies reveal that the complications associated with diabetes can be reduced by proper management of the disease by continuously monitoring and forecasting the blood glucose level of patients. Objective. The prior prediction of blood glucose level is necessary to overcome the lag time for insulin absorption in diabetic type 1 patients. Method. In this research, we use continuous glucose monitoring (CGM) data to predict future blood glucose level using the previous data points. We compare two neural network techniques. We apply the optimal feedforward neural network and then propose optimal nonlinear autoregressive neural networks for blood glucose prediction 15–30 minutes earlier for diabetic type 1 patients. We validate the proposed model with 2 virtual subjects using their 24-hour blood glucose level data. These two case studies have been compiled from AIDA, i.e., the freeware mathematical diabetes simulator. Results. In the prediction horizon (PH) of 15 and 30 minutes, improved results have been shown for minimal inputs for blood glucose level of a particular subject. Root mean square error (RMSE) is used for performance calculation. For the optimal feedforward neural network, the RMSE is 0.9984 and 3.78 ml/dl, and for the optimal nonlinear autoregressive neural network, it reduces the RMSE to 0.60 and 1.12 ml/dl for 15 min and 30 min prediction horizons, respectively, for subject 1. Similarly, for subject 2 for the optimal feedforward neural network, RMSE is 1.43 and 3.51 ml/dl which is improved using the optimal autoregressive neural network to 0.7911 and 1.6756 ml/dl for 15 min and 30 min prediction horizons, respectively. Validation. We further validate our proposed model using UCI machine learning datasets (Abalone and Servo), and it shows improved results on that as well. Conclusion and Future Work. The proposed optimal nonlinear autoregressive neural network model performs better than the feedforward neural network model for these time series data. In the future, we intend to investigate a greater collection of AIDA scenarios and data that are real and influence other factors of BGLs.

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“…Sinir ağı tabanlı sınıflandırma yöntemi, kendi kendini ayarlayabilme, öğrenebilme yeteneğine sahip, veri ilişkilerini bilmesine gerek olmadan farklı modellere uygulanabilme gibi ayırt edici özelliklere sahiptir [4]. Giriş ve çıkış arasında bir gösterimin olmadığı, örüntü tanıma, sınıflandırma [18], regresyon [19,20] analizlerinde kullanılmaktadır.…”

Section: Yapay Sinir Ağları (Ysa) (Artificial Neuralunclassified

Güç Kalitesi Bozulmalarının 2 Boyutlu Ayrık Dalgacık Dönüşümü ve Torbalama Karar Ağaçları Yöntemi ile Sınıflandırılması

Karasu¹,

Saraç²

2018

Journal of Polytechnic

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Prediction of wind speed with non-linear autoregressive (NAR) neural networks

Cited by 31 publications

References 10 publications

A synthesis of feasible control methods for floating offshore wind turbine system dynamics

A synthesis of feasible control methods for floating offshore wind turbine system dynamics

Blood Glucose Level Prediction of Diabetic Type 1 Patients Using Nonlinear Autoregressive Neural Networks

Güç Kalitesi Bozulmalarının 2 Boyutlu Ayrık Dalgacık Dönüşümü ve Torbalama Karar Ağaçları Yöntemi ile Sınıflandırılması

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