Comparison of Weibull parameter estimation methods using LiDAR and mast wind data in an Indian offshore site: The Gulf of Khambhat

Gautam, Atul; Warudkar, Vilas; Bhagoria, J.L.

doi:10.1016/j.oceaneng.2022.112927

Cited by 9 publications

(2 citation statements)

References 45 publications

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“…Sharma P K et al 10 compared the effect of different probability distribution models on wind data fitting. Gautam et al 11 compared 11 Weibull parameter estimation methods and presented the applicable scope, advantages, and disadvantages of each method based on empirical analysis. Some scholars have also made comparisons between heuristic algorithms and numerical methods to estimate wind potential 12 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition

Jiang

2023

Sci Rep

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A model with high accuracy and strong generalization performance is conducive to preventing serious pollution incidents and improving the decision-making ability of urban planning. This paper proposes a new neural network structure based on seasonal–trend decomposition using locally weighted scatterplot smoothing (Loess) (STL) and a dependency matrix attention mechanism (DMAttention) based on cosine similarity to predict the concentration of air pollutants. This method uses STL for series decomposition, temporal convolution, a bidirectional long short-term memory network (TCN-BiLSTM) for feature learning of the decomposed series, and DMAttention for interdependent moment feature emphasizing. In this paper, the long short-term memory network (LSTM) and the gated recurrent unit network (GRU) are set as the baseline models to design experiments. At the same time, to test the generalization performance of the model, short-term forecasts in hours were performed using PM2.5, PM10, SO2, NO2, CO, and O3 data. The experimental results show that the model proposed in this paper is superior to the comparison model in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). The MAPE values of the 6 kinds of pollutants are 6.800%, 10.492%, 9.900%, 6.299%, 4.178%, and 7.304%, respectively. Compared with the baseline LSTM and GRU models, the average reduction is 49.111% and 43.212%, respectively.

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Section: Introductionmentioning

confidence: 99%

Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition

Jiang

2023

Sci Rep

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“…The three-parameter model includes a third parameter known as the threshold parameter, which contributes to understanding the lowest wind speed that can be expected in the region [20]. Several papers discussed the use of probability density functions in distributing the wind speed dataset, such as a study in Malaysia onshore [21], Indian Offshore [22], Palestine onshore [23], and a suburban area in Mongolia [24]. All of this literature compares those functions' fitting with the reference data.…”

Section: Introductionmentioning

confidence: 99%

Three-Parameter Weibull for Offshore Wind Speed Distribution in Malaysia

Aliashim Albani,

Siti Syazwani Abdul Ghani,

Mohd Zamri Ibrahim

et al. 2024

ARASET

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It is usual practice to employ the probability density function in order to ascertain the wind energy potential. In this paper, the three-parameter Weibull model was suggested to distribute the low wind speed data at a site off the coast of Kijal, Terengganu. The maximum likelihood approach was used to determine the value of the distribution parameters. The Matrix Laboratory (MATLAB), better known for its user-friendly programming language, is used to carry out all of the analysis. The three-parameter Weibull was compared with the two-parameter Weibull and Rayleigh using the R-squared and the root mean square error (RMSE). These three distributions statistically model hourly ERA-5 reanalysis wind speeds. The three-parameter Weibull has a good performance, since it appears between the best R2 closest to 1 and RMSE the closest to 0. The R2 for the three-parameter Weibull is 0.9898, the two-parameter Weibull is 0.9874, and Rayleigh is 0.5153. While the RMSE Three-parameter Weibull is 0.0070, the two-parameter Weibull is 0.0082, and Rayleigh is 0.0677.

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Assessment of heavy metal pollution and preschool children health risk in urban street dusts from different functional areas in a typical industrial and mining city, NW China

Zhang

Chen

et al. 2023

Environ Geochem Health

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Comparison of Weibull parameter estimation methods using LiDAR and mast wind data in an Indian offshore site: The Gulf of Khambhat

Cited by 9 publications

References 45 publications

Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition

Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition

Three-Parameter Weibull for Offshore Wind Speed Distribution in Malaysia

Assessment of heavy metal pollution and preschool children health risk in urban street dusts from different functional areas in a typical industrial and mining city, NW China

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