Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine

Band, Shahab S.; Ghazvinei, Pezhman Taherei; Yusof, Khamaruzaman B. Wan; Ahmadi, Mohammad Hossein; Nabipour, Narjes; Chau, Kwok-wing

doi:10.1002/ese3.849

Cited by 5 publications

(5 citation statements)

References 58 publications

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“…The effect of the directions of the earth's surface on the radiation received is determined by the product of the cosine of the angle of impact; the angle of collision of AngIn θ,α , between the retaining surface and the sky with the zenith angle G z and the azimuth angle G o , relative to the center is calculated with Eq. ( 10) [28].…”

Section: Solar Irradiationmentioning

confidence: 99%

Optimal Location to Use Solar Energy in an Urban Situation

Dwijendra¹,

Patra²,

Kumar³

et al. 2023

Computers, Materials &Amp; Continua

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This study conducted in Lima, Peru, a combination of spatial decision making system and machine learning was utilized to identify potential solar power plant construction sites within the city. Sundial measurements of solar radiation, precipitation, temperature, and altitude were collected for the study. Gene Expression Programming (GEP), which is based on the evolution of intelligent models, and Artificial Neural Networks (ANN) were both utilized in this investigation, and the results obtained from each were compared. Eighty percent of the data was utilized during the training phase, while the remaining twenty percent was utilized during the testing phase. On the basis of the findings, it was determined that the GEP is the most suitable network for predicting the location. The test state's Nash-Sutcliffe efficiency (NSE) was 0.90, and its root-mean-square error (RMSE) was 0.04. Following the generation of the final map based on the results of the GEP model, it was determined that 9.2% of the province's study area is suitable for the construction of photovoltaic solar power plants, while 53.5% is acceptable and 37.3% is unsuitable. The ANN model reveals that only 1.7% of the study area is suitable for the construction of photovoltaic solar power plants, while 66.8% is acceptable and 31.5% is unsuitable.

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Section: Solar Irradiationmentioning

confidence: 99%

Optimal Location to Use Solar Energy in an Urban Situation

Dwijendra¹,

Patra²,

Kumar³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…At present, data‐driven artificial neural network and machine learning have been used in nonlinear fitting filed 10‐13,16,17 . Tian Z proposed LSSVM for wind power prediction 14 .…”

Section: Introductionmentioning

confidence: 99%

“…In addition, traditional filtering methods such as mean filtering and wavelet domain denoizing used in the existing statistical prediction models have limited effect to eliminate the noise component of PV power, which restrict further improvement of the prediction accuracy. [8][9][10][11][12][13] At present, data-driven artificial neural network and machine learning have been used in nonlinear fitting filed. [10][11][12][13]16,17 Tian Z proposed LSSVM for wind power prediction.…”

Section: Introductionmentioning

confidence: 99%

“…[8][9][10][11][12][13] At present, data-driven artificial neural network and machine learning have been used in nonlinear fitting filed. [10][11][12][13]16,17 Tian Z proposed LSSVM for wind power prediction. 14 Ahmad et al used GPR to predict solar and wind power.…”

Section: Introductionmentioning

confidence: 99%

“…However, strong disturbance such as nonstationarity of sequences and outliers of the sequence will reduce the prediction accuracy of traditional statistical model. In addition, traditional filtering methods such as mean filtering and wavelet domain denoizing used in the existing statistical prediction models have limited effect to eliminate the noise component of PV power, which restrict further improvement of the prediction accuracy 8‐13 …”

Section: Introductionmentioning

confidence: 99%

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Prediction of short‐term photovoltaic power via codec neural network and mode decomposition based deep learning approach

Jia

et al. 2021

Energy Science & Engineering

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Accurate photovoltaic (PV) power prediction is of great significance for the stable operation of PV system, but the PV power sequence is nonstationary, so it is difficult to establish the prediction model effectively by a simple neural network. In this study, the MCVMD‐MI‐SWATS‐Codec (multidimensional constraints variational mode decomposition‐mixed initialization‐switching from Adam to stochastic gradient descent‐codec) that is based on the idea of deep model fusion is proposed to predict PV power generation. MCVMD method with parameter K determined by multidimensional constraint criterion is used to decompose the PV power data, and the frequency of each component sequence is analyzed after decomposition to explore the physical characteristics and application value of each component frequency. Then, a hybrid ResNet‐LSTM (residual network‐long‐ and short‐term memory) model based on codec mechanism integrates input data with different dimensions, such as weather conditions and historical IMF (intrinsic mode function), into dense vectors with the same dimension. The experimental data of polysilicon PV array in the Australian desert environment are used to test the proposed fusion neural network model and the other six competitive models. The results show that MCVMD algorithm is significantly helpful in decomposing the nonstationary data to improve the prediction accuracy, and MCVMD‐MI‐SWATS‐Codec model has high prediction accuracy and robustness in both stable and unstable weather conditions.

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Influence of blade number on performance of multistage hydraulic turbine in turbine mode

Zhang

et al. 2022

Energy Science & Engineering

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Compared with the traditional reverse multistage pump as hydraulic turbine, hydraulic turbine in turbine mode (T‐type turbine) with low specific speed has higher single‐stage energy head, which can effectively reduce stages and increase the stability when it is used to recycle high residual pressure liquid. Blade number of runner is an important design parameter, which has great influence on the performance of T‐type hydraulic turbine and need to be further studied. Therefore, two‐stage T‐type turbine models with different blade numbers are established through numerical simulation. Then, the accuracy of numerical simulation is verified by test. Finally, the performance and pressure fluctuating characteristics of turbine with different blade numbers are obtained. The results show that: (a) Taking same blade numbers in both stages (traditional design method), the turbine efficiency can be higher with the blade number of primary runner and secondary runner are 14, but resulting the superposition of pulsation in secondary runner; (b) Taking different blade numbers in both stages, respectively, the superposition of pulsation can be effectively avoided, which also leads to the increase in efficiency, and up to 81.169%; (c) The method of using different number of blades in different stages can replace the traditional design method and effectively improve the hydraulic performance of T‐type hydraulic turbine.

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Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine

Cited by 5 publications

References 58 publications

Optimal Location to Use Solar Energy in an Urban Situation

Optimal Location to Use Solar Energy in an Urban Situation

Prediction of short‐term photovoltaic power via codec neural network and mode decomposition based deep learning approach

Influence of blade number on performance of multistage hydraulic turbine in turbine mode

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