Accurate prediction of different forecast horizons wind speed using a recursive radial basis function neural network

Madhiarasan, M.

doi:10.1186/s41601-020-00166-8

Cited by 46 publications

(27 citation statements)

References 14 publications

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“…The reason we choose ANN to extract features of meteorological parameters is that the input is relatively simple-with only two parameters, in this way, we suppose that a rather simple network is enough to extract the information needed. Besides, ANN has been widely used in many scenes with meteorological parameters as parts of the inputs (Matsumoto et al, 1993;Bhatt and Gandhi, 2019;Madhiarasan, 2020), and the satisfactory results has provided intuitions to our problem.…”

Section: Meteorological Parameters Feature Extraction Networkmentioning

confidence: 90%

Insulator Contamination Perception Based on Feature Fusion of Infrared Image and Meteorological Parameters

Wang

et al. 2021

Front. Energy Res.

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Polluted insulators seriously threaten the safe and stable operation of power grids, which attaches great significance to insulator contamination perception. Among the present methods, the non-contact approaches based on infrared images have gradually been widely used, as they are much more safe and are of low cost. However, the thermal effect of insulators is largely affected by meteorological conditions, which makes the infrared image-based methods less accurate. To solve the above problem, we take infrared image and meteorological parameters including humidity and temperature as input, and propose a feature fusion model to perceive insulator contamination in different weather conditions. Firstly, different feature extraction networks are used to perform feature extraction on the two types of data; secondly, the two features are concatenated to fuse together; thirdly, further feature extraction is performed and contamination is classified according to the pollution severity. Case studies show that the proposed method can better explore the relationship between humidity, temperature and pollution level of the insulators, thus can better separate the contamination grades and outperform the conventional infrared image based methods.

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Section: Meteorological Parameters Feature Extraction Networkmentioning

confidence: 90%

Insulator Contamination Perception Based on Feature Fusion of Infrared Image and Meteorological Parameters

Wang

et al. 2021

Front. Energy Res.

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“…The uncertainty of the forecast has drawn a great deal of global attention while its accuracy is also far from satisfactory [5,19,23,37,41]. According to a statistical report on domestic WPFEs in China, the root mean square errors are around 10%-20% for day-ahead forecasting [26].…”

Section: Introductionmentioning

confidence: 99%

Reliability sensitivity of wind power system considering correlation of forecast errors based on multivariate NSTPNT method

Dong

2021

Prot Control Mod Power Syst

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The impact of wind power forecast errors (WPFEs) on power system reliability can be quantified by a sensitivity model, which helps to determine the importance of different wind farms. However, the unknown distribution and correlation of WPFEs make it difficult to calculate the reliability sensitivity. The existing univariate non-standard third-order polynomial normal transformation (NSTPNT) expresses the reliability sensitivity of WPFEs by a normal random variable with explicit distribution, and is not suitable for multiple wind farms with correlated forecast errors. In this paper, the univariate NSTPNT method is extended to the multivariate by deriving the analytical expression of the correlation coefficients before and after the transformation, to establish the transformation between the WPFEs and a normal random vector (RV) with the specific correlation. A reliability sensitivity model to the WPFEs expressed to the normal RV is then proposed. The numerical results validate the accuracy of the proposed multivariate NSTPNT and the sensitivity model. The maximum relative error for using the sensitivity to approximate the change of reliability with distribution parameters of the WPFEs is less than 2.42%. The necessity of considering the correlation of WPFEs is analyzed. The maximum relative error of the sensitivity reaches 83% when the correlation is ignored.

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“…Determining the topology that does not match the needs caused overfitting or underfitting in neural networks. Several researchers have conducted research to determine the neural network topology in various ways: methods based solely on the number of input and output attributes ( Sartori & Antsaklis, 1991 ; Tamura & Tateishi, 1997 ), trial and error ( Blanchard & Samanta, 2020 ; Madhiarasan, 2020 ; Madhiarasan & Deepa, 2016 ; Madhiarasan & Deepa, 2017 ; Şen & Özcan, 2021 ) , and the rule of thumb ( Bakhashwain & Sagheer, 2021 ; Carballal et al, 2021 ; Rahman et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…The purpose of this research is to perform regression, so that the cumulative variance required is expected to be greater than classification, because the output domain for regression is continuous, while for classification is discrete. Then, topology performance of neural network in this research was compared with several other methods, namely: the Sartori method ( Sartori & Antsaklis, 1991 ), the Tamura and Tateishi method ( Tamura & Tateishi, 1997 ), the Madhiarasan and Deepa method( Madhiarasan & Deepa, 2017 ), the Madhiarasan method ( Madhiarasan, 2020 ), and the Mahdi method ( Mahdi, Yousif & Melhum, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction

Rachmatullah

Santoso

Surendro

2021

PeerJ Computer Science

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Artificial neural network (ANN) is one of the techniques in artificial intelligence, which has been widely applied in many fields for prediction purposes, including wind speed prediction. The aims of this research is to determine the topology of neural network that are used to predict wind speed. Topology determination means finding the hidden layers number and the hidden neurons number for corresponding hidden layer in the neural network. The difference between this research and previous research is that the objective function of this research is regression, while the objective function of previous research is classification. Determination of the topology of the neural network using principal component analysis (PCA) and K-means clustering. PCA is used to determine the hidden layers number, while clustering is used to determine the hidden neurons number for corresponding hidden layer. The selected topology is then used to predict wind speed. Then the performance of topology determination using PCA and clustering is then compared with several other methods. The results of the experiment show that the performance of the neural network topology determined using PCA and clustering has better performance than the other methods being compared. Performance is determined based on the RMSE value, the smaller the RMSE value, the better the neural network performance. In future research, it is necessary to apply a correlation or relationship between input attribute and output attribute and then analyzed, prior to conducting PCA and clustering analysis.

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Accurate prediction of different forecast horizons wind speed using a recursive radial basis function neural network

Cited by 46 publications

References 14 publications

Insulator Contamination Perception Based on Feature Fusion of Infrared Image and Meteorological Parameters

Insulator Contamination Perception Based on Feature Fusion of Infrared Image and Meteorological Parameters

Reliability sensitivity of wind power system considering correlation of forecast errors based on multivariate NSTPNT method

Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction

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