A New Hybrid Approach for Wind Speed Prediction Using Fast Block Least Mean Square Algorithm and Artificial Neural Network

Filik, Ümmühan Başaran

doi:10.1155/2016/8395751

Cited by 4 publications

(4 citation statements)

References 36 publications

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“…P t indicates the output of the max pooling layer at t time that encompasses the energy utilization dataset and additional parameters utilized as input to AE through the CBLSTM layer. i t ; f t , and 0 t indicates the input, forget, and output gates; correspondingly, h t denotes the hidden state of the BLSTM cell that was upgraded at each t step in backward and forward directions [32][33][34][35][36]. Fig.…”

Section: Level I: Cblstmae-based Prediction Processmentioning

confidence: 99%

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

Surendran¹,

Alotaibi²,

Subahi³

2023

Computer Systems Science and Engineering

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High precision and reliable wind speed forecasting have become a challenge for meteorologists. Convective events, namely, strong winds, thunderstorms, and tornadoes, along with large hail, are natural calamities that disturb daily life. For accurate prediction of wind speed and overcoming its uncertainty of change, several prediction approaches have been presented over the last few decades. As wind speed series have higher volatility and nonlinearity, it is urgent to present cutting-edge artificial intelligence (AI) technology. In this aspect, this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning (IWSP-CSODL) method. The presented IWSP-CSODL model estimates the wind speed using a hybrid deep learning and hyperparameter optimizer. In the presented IWSP-CSODL model, the prediction process is performed via a convolutional neural network (CNN) based long short-term memory with autoencoder (CBLSTMAE) model. To optimally modify the hyperparameters related to the CBLSTMAE model, the chicken swarm optimization (CSO) algorithm is utilized and thereby reduces the mean square error (MSE). The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct scenarios. The comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models.

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Section: Level I: Cblstmae-based Prediction Processmentioning

confidence: 99%

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

Surendran¹,

Alotaibi²,

Subahi³

2023

Computer Systems Science and Engineering

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“…The compression test data classifications were fixed and the data volume was large, which can be suitably predicted using artificial neural network (ANN). Filik (2016) developed a new hybrid approach for wind speed estimation using a fast block least mean square algorithm and an artificial neural network [9]; however, artificial neural networks have been found to have inherent faults such as weak error-tolerance and missing information [10].…”

Section: Introductionmentioning

confidence: 99%

Concrete Compression Test Data Estimation Based on a Wavelet Neural Network Model

Wang

et al. 2019

Mathematical Problems in Engineering

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Firstly, a genetic algorithm (GA) and simulated annealing (SA) optimized fuzzy c-means clustering algorithm (FCM) was proposed in this paper, which was developed to allow for a clustering analysis of the massive concrete cube specimen compression test data. Then, using an optimized error correction time series estimation method based on the wavelet neural network (WNN), a concrete cube specimen compressive strength test data estimation model was constructed. Taking the results of cluster analysis as data samples, the short-term accurate estimation of concrete quality was carried out. It was found that the mean absolute percentage error, e1, and the root mean square error, e2, for the samples were 6.03385% and 3.3682KN, indicating that the proposed method had higher estimation accuracy and was suitable for concrete compressive test data short-term quality estimations.

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“…Among regression methods in statistics, linear regression has the strengths of being simple and easy to calculate; thus, it is often adopted to establish models to derive equations between parameters and to make predictions. However, for high-dimensional, nonlinear problems, stable and accurate mathematical models are required, such as a neural network [9]. In addition, numerous studies have employed artificial intelligence to predict wind speed.…”

Section: Introductionmentioning

confidence: 99%

Regional Forecasting of Wind Speeds during Typhoon Landfall in Taiwan: A Case Study of Westward-Moving Typhoons

et al. 2018

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Abstract:Taiwan is located on a route where typhoons often strike. Each year, the strong winds accompanying typhoons are a substantial threat and cause significant damage. However, because the terrains of high mountains in Taiwan vary greatly, when a typhoon passes the Central Mountain Range (CMR), the wind speed of typhoons becomes difficult to predict. This research had two primary objectives: (1) to develop data-driven techniques and a powerful artificial neural network (ANN) model to predict the highly complex nonlinear wind systems in western Taiwan; and, (2) to investigate the accuracy of wind speed predictions at various locations and for various durations in western Taiwan when the track of westward typhoons is affected by the complex geographical shelters and disturbances of the CMR. This study developed a typhoon wind speed prediction model that evaluated various typhoon tracks (covering Type 2, Type 3, and Type 4 tracks, as defined by the Central Weather Bureau), and evaluated the prediction accuracy at Hsinchu, Wuqi, and Kaohsiung Stations in western Taiwan. Back propagation neural networks (BPNNs) were employed to establish wind speed prediction models, and a linear regression model was adopted as the benchmark to evaluate the strengths and weaknesses of the BPNNs. The results were as follows: (1) The BPNNs generally had favorable performance in predicting wind speeds and their performances were superior to linear regressions; (2) when absolute errors were adopted to evaluate the prediction performances, the predictions at Hsinchu Station were the most accurate, whereas those at Wuqi Station were the least accurate; however, when relative errors were adopted, the predictions at Hsinchu Station were again the most accurate, whereas those at Kaohsiung were the least accurate; and, (3) regarding the relative error rates for the maximum wind speed of Types 2, 3, and 4 typhoons, Wuqi, Kaohsiung, and Wuqi had the most accurate performance, respectively; as for maximum wind time error (ET M ) for Types 2, 3, and 4 typhoons, Kaohsiung, Wuqi, and Wuqi correspondingly performed the most favorably.

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A New Hybrid Approach for Wind Speed Prediction Using Fast Block Least Mean Square Algorithm and Artificial Neural Network

Cited by 4 publications

References 36 publications

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

Concrete Compression Test Data Estimation Based on a Wavelet Neural Network Model

Regional Forecasting of Wind Speeds during Typhoon Landfall in Taiwan: A Case Study of Westward-Moving Typhoons

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