Performance of different hybrid algorithms for prediction of wind speed behavior

Mostafaeipour, Ali; Goli, Alireza; Rezaei, Mostafa; Qolipour, Mojtaba; Arabnia, Hamid-Reza; Goudarzi, Hossein; Behnam, Elham

doi:10.1177/0309524x19882431

Cited by 6 publications

(6 citation statements)

References 44 publications

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“…Three hybrid algorithms were presented in work by Ali Mostafaeipour et al ( 2021 ), and their efficacy in forecasting wind speed behavior in the Abadeh region was assessed. Three hybrid algorithms: Genetic algorithm (GA), Simulated Annealing algorithm (SAA), and Shuffling Frog-Leaping algorithm (SFLA) were proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Artificial Intelligence(AI) has been prominently used in various segments of society—ranging from analyzing wind speed behavior (Mostafaeipour et al, 2021 ) to prediction of marketing products(Goli et al, 2021 ). With the advancement of AI methods, medical practitioners are progressing more towards adapting and implementing AI methods in various medical fields.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: Periapical dental X-ray image classification using deep neural networks

et al. 2022

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This paper studies the problem of detection of dental diseases. Dental problems affect the vast majority of the world's population. Caries, RCT (Root Canal Treatment), Abscess, Bone Loss, and missing teeth are some of the most common dental conditions that affect people of all ages all over the world. Delayed or incorrect diagnosis may result in mistreatment, affecting not only an individual's oral health but also his or her overall health, thereby making it an important research area in medicine and engineering. We propose a pipelined Deep Neural Network (DNN) approach to detect healthy and non-healthy periapical dental X-ray images. Even a minor enhancement or improvement in existing techniques can go a long way in providing significant health benefits in the medical field. This paper has made a successful attempt to contribute a different type of pipelined approach using AlexNet in this regard. The approach is trained on a large dataset of 16,000 dental X-ray images, correctly identifying healthy and non-healthy X-ray images. We use an optimized Convolutional Neural Networks and three state-of-the-art DNN models, namely Res-Net-18, ResNet-34, and AlexNet for disease classification. In our study, the AlexNet model outperforms the other models with an accuracy of 0.852. The precision, recall and F1 scores of AlexNet also surpass the other models with a score of 0.850 across all metrics. The area under ROC curve also signifies that both the false-positive rate and false-negative rate are low. We conclude that even with a big data set and raw X-ray pictures, the AlexNet model generalizes effectively to previously unseen data and can aid in the diagnosis of a variety of dental diseases.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Periapical dental X-ray image classification using deep neural networks

et al. 2022

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“…These models can capture both nonlinear and linear patterns of the data and provide outstanding forecasting results than individual models. In recent years, Okumus and Dinler [31] [34] observed the prediction accuracy of different hybrid algorithms on wind speed behavior and concluded the hybrid ANN-SFLA model performed well for the monthly data of the city of Abadeh.…”

Section: A Backgroundmentioning

confidence: 99%

“…Thus, the individual use of a statistical time series or artificial intelligence models are inadequate to predict the series that contains both linear and nonlinear patterns [8]. However, to improve the prediction accuracy hybrid models have been established [31]- [34].…”

Section: A Backgroundmentioning

confidence: 99%

A New Hybrid ARAR and Neural Network Model for Multi-Step Ahead Wind Speed Forecasting in Three Regions of Pakistan

2020

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Wind is one of the most essential sources of clean, environmental friendly, socially constructive, economically beneficial, and renewable energy. To intuit the potential of this energy in a region the accurate wind speed modeling and forecasting are crucially important, even for planning, conversion of wind energy to electricity, energy trading, and reducing instability. However, accurate prediction is difficult due to intermittency and intrinsic complexity in wind speed data. This study aims to suggest a more appropriate model for accurate wind speed forecasting in the Jhimpir, Gharo, and Talhar, regions of Sindh, Pakistan. Therefore, the present study combined the Autoregressive-Autoregressive (ARAR) and Artificial Neural Network (ANN) models to propose a new hybrid ARAR-ANN model for better prediction by precisely capturing different patterns of the wind speed time-series data sets. The proposed hybrid model is efficient in modeling, reducing statistical errors, and forecasting the wind speed effectively. The performance of the proposed hybrid ARAR-ANN model is compared using three error-statistics and Nash-Sutcliffe efficiency-coefficient. The empirical results of the four performance indices fully demonstrated the superiority of the hybrid ARAR-ANN model than persistence model, ARAR, ANN and SVM. Indeed, the proposed model is an effective and feasible approach for wind speed forecasting.

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“…Previously several studies were undertaken to develop accurate time series models for wind speed (Mostafaeipour et al, 2019, Kushwah and Wadhvani, 2020) and solar irradiance (Amrouche and Pivert, 2014; Thapar, 2019) forecast applications. Blanchard and Samanta (2019) have applied a nonlinear autoregressive neural network with exogenous inputs for wind speed forecasting, proving that these models perform better than the baseline models.…”

Section: Introductionmentioning

confidence: 99%

Attention mechanism for developing wind speed and solar irradiance forecasting models

2020

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This article presents the Recurrent Neural Network (RNN) and its Attention mechanism to develop forecasting models for renewable energy applications. In this study, wind speed and solar irradiance forecasting models have been developed as these two factors play a significant role in renewable energy production. The irregular nature of wind poses the challenge of accurate wind speed prediction, while solar irradiance forecasting can aid in the planning and deployment of solar power plants. In this paper, six RNN techniques, namely RNN, GRU, LSTM, Content-based Attention, Luong Attention, and Self-Attention based RNN are considered for forecasting the future values of wind speed and solar irradiance in particular geographical locations. The aim is the identification of the advantages, comparison, and importance of different recurrent neural network methods for forecasting models. All models are developed on the datasets of the National Renewable Energy Laboratory (NREL) and NASA’s Prediction of Worldwide Energy Resource (POWER).

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Performance of different hybrid algorithms for prediction of wind speed behavior

Cited by 6 publications

References 44 publications

RETRACTED ARTICLE: Periapical dental X-ray image classification using deep neural networks

RETRACTED ARTICLE: Periapical dental X-ray image classification using deep neural networks

A New Hybrid ARAR and Neural Network Model for Multi-Step Ahead Wind Speed Forecasting in Three Regions of Pakistan

Attention mechanism for developing wind speed and solar irradiance forecasting models

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