An efficient online sequential extreme learning machine model based on feature selection and parameter optimization using cuckoo search algorithm for multi-step wind speed forecasting

Shetty, Rashmi P.; Sathyabhama, A.; Pai, P. Srinivasa

doi:10.1007/s00500-020-05222-x

Cited by 24 publications

(9 citation statements)

References 47 publications

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“…In 2009, Yang and Deb introduced CS algorithm, which is based on cuckoo bird’s obligate brood parasitic nature and Levy flight behavior of animals. This algorithm is fast and efficient in exploring the optimal solution, has less chances of being trapped in local minima, because of the Levy flight mechanism to generate new solutions, and is a popular nature inspired optimization algorithm [ 16 – 18 ].…”

Section: Methodsmentioning

confidence: 99%

Forecasting of COVID 19 Cases in Karnataka State using Artificial Neural Network (ANN)

Shetty¹,

Pai²

2021

J. Inst. Eng. India Ser. B

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COVID-19 is a pandemic that has caused lot of deaths and infections in the last 2 months and is showing an increasing trend not only in the number of infections and deaths, but also in the recovery rate. Accurate prediction models are very much essential to make proper forecasts and take necessary actions. This study demonstrates the capability of Multilayer Perceptron (MLP), an Artificial Neural network (ANN) model for forecasting the number of infected cases in the state of Karnataka in India. It is trained using a fast training algorithm namely, Extreme Learning machine to reduce the training time required. The parameters required for the forecasting model have been selected using partial autocorrelation function, which is a conventional method, and its performance has been compared with parameters selected using cuckoo search (CS) algorithm, which is a very popular metaheuristic optimization algorithm. The testing of the forecasting model has been done, and comparison between the two parameter selection methods as well as with MLP with conventional backpropagation has been carried out. Use of CS algorithm has resulted in a better forecasting performance based on mean absolute percentage error (MAPE), with a value of 6.62% on training data and 7.03% on the test data. Further to check the efficacy of the model, the data of COVID-19 cases of Hungary from 4 March to 19 April 2020 have been used, which resulted in a MAPE of 1.55%, thereby establishing the robustness of the proposed ANN model for forecasting COVID-19 cases for the state of Karnataka.

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Section: Methodsmentioning

confidence: 99%

Forecasting of COVID 19 Cases in Karnataka State using Artificial Neural Network (ANN)

Shetty¹,

Pai²

2021

J. Inst. Eng. India Ser. B

Self Cite

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“…A new data‐driven ensemble method for numerical wind speed forecasting has been proposed in a work by Zhao et al 40 A new multistep forecasting strategy is proposed based on a wavelet decomposition pre‐processing module, nonlinear autoregressive artificial neural network and nonlinear autoregressive exogenous artificial neural network composite prediction module, and support vector machine classifier post‐processing module 41 . On reviewing the previous studies made in multi‐step wind speed forecasting as presented above and also in the works 42–67 carried out by numerous researchers in this field, the following limitations are inferred and are as elucidated below. Occurrences of global and local optima problems, leading to the saturated learning process of the machine learning algorithms 14–23,27–34 …”

Section: Introductionmentioning

confidence: 99%

“…[24][25][26] • Difficult to maintain the stability of the algorithms. [61][62][63][64][65] • More operations are required with few algorithms employed. [28][29][30][31][32][33][34][35][36][37][38][39][40] • Premature and delayed convergence due to the global stuck of the algorithms.…”

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confidence: 99%

“…[28][29][30][31][32][33][34][35][36][37][38][39][40] • Premature and delayed convergence due to the global stuck of the algorithms. [64][65][66][67] • Loss of information and henceforth lacks in prediction accuracy. 14,23,24,49 • Stagnation and scalability issues.…”

mentioning

confidence: 99%

“…14,23,24,49 • Stagnation and scalability issues. [61][62][63][64][65] • Post-processing elapses more time. [59][60][61][62] Additionally, in the past years forecasting of wind speed has been done with the deep learning neural models with temporal convolutional technique along with hybrid decomposition approaches.…”

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confidence: 99%

See 2 more Smart Citations

Multi‐step wind speed and wind power forecasting using variational momentum factor and deep learning based intelligent neural network models

Nachimuthu

Banerjee

Karuppaiah

2021

Concurrency and Computation

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Deep learning based novel intelligent neural network models are developed in this research study and employed for performing multi‐step wind speed and wind power forecasting for the data pertaining to certain wind farms. It has always been tedious to predict wind speed and wind power accurately due the existence of non‐linearity in the wind farm data and as well previous traditional and heuristic techniques has their own merits and demerits in performing the prediction process. This research study intends to handle the prevailing non‐linearity of the wind farm data and as well perform prediction of the parameters in a better manner with increased accuracy rate. The prediction study facilitates the renewable energy community to install the wind mills in the locations with higher accuracy rate and thereby power production gets increased extravagantly. The intelligent neural network developed in this article includes the ELMAN and spiking neuronal models with incorporated deep learning procedure and varying momentum factor criterion to achieve minimal error and better accuracy rate. Multi‐step forecasting is carried for 10‐min ahead and the numerical simulation executed with the proposed intelligent non‐linear forecasting techniques. The attained results confirm the superiority of the developed models over other techniques from previous works.

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Wind Energy, Its Application, Challenges, and Potential Environmental Impact

Nazir¹,

Wang²,

Bilal³

et al. 2021

Handbook of Climate Change Mitigation and Adaptation

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An efficient online sequential extreme learning machine model based on feature selection and parameter optimization using cuckoo search algorithm for multi-step wind speed forecasting

Cited by 24 publications

References 47 publications

Forecasting of COVID 19 Cases in Karnataka State using Artificial Neural Network (ANN)

Forecasting of COVID 19 Cases in Karnataka State using Artificial Neural Network (ANN)

Multi‐step wind speed and wind power forecasting using variational momentum factor and deep learning based intelligent neural network models

Wind Energy, Its Application, Challenges, and Potential Environmental Impact

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