Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data

Deo, Ravinesh C.; Ghorbani, Mohammad Ali; Samadianfard, Saeed; Maraseni, Tek; Bilgili, Mehmet; Biazar, Seyed Mostafa

doi:10.1016/j.renene.2017.09.078

Cited by 126 publications

(46 citation statements)

References 64 publications

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“…Moreover, there is no straightforward way of splitting training and testing data. For instance, the study of Kurup and Dudani (2014) utilized a total of 63% of their data for model development, whereas Qasem et al, (2019) utilized 67% of data and Deo et al (2018), Samadianfard et al (2018), and Samadianfard et al (2019a,b) used 70% andZounemat-Kermani et al, (2019) implemented 80% of entire data to develop their models. Consequently, to create models for wind speed prediction, 70% of the data (2534 data) is applied for training, and 30% of them (1077 data) is utilized for the testing phase.…”

Section: Resultsmentioning

confidence: 99%

“…Also, rising alertness of the ecological effects of greenhouse gas releases has encouraged an impressive rise in renewable energy. Therefore, to encounter the energy request and the problems of greenhouse gas releases, it is essential to concentrate on substitute renewable energies (Deo et al 2018, hoolohan et al 2018, and Marchal et al 2011. Although the wind supply in most parts of the world is plentiful, its unpredictable and irregular nature lead to some problems such as acquiring a safe and persistent supply of electricity.…”

Section: Introductionmentioning

confidence: 99%

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Wind Speed Prediction Using a Hybrid Model of the Multi-Layer Perceptron and Whale Optimization Algorithm

Samadianfard¹,

Hashemi²,

Kargar³

et al. 2020

Preprint

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Wind power as a renewable source of energy, has numerous economic, environmental and social benefits. In order to enhance and control the renewable wind power, it is vital to utilize models that predict wind speed with high accuracy. Due to neglecting of requirement and significance of data preprocessing and disregarding the inadequacy of using a single predicting model, many traditional models have poor performance in wind speed prediction. In the current study, for predicting wind speed at target stations in the north of Iran, the combination of a multi-layer perceptron model (MLP) with the Whale Optimization Algorithm (WOA) used to build new method (MLP-WOA) with a limited set of data (2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014). Then, the MLP-WOA model was utilized at each of the ten target stations, with the nine stations for training and tenth station for testing (namely: Astara, Bandar-E-Anzali, Rasht, Manjil, Jirandeh, Talesh, Kiyashahr, Lahijan, Masuleh and Deylaman) to increase the accuracy of the subsequent hybrid model. Capability of the hybrid model in wind speed forecasting at each target station was compared with the MLP model without the WOA optimizer. To determine definite results, numerous statistical performances were utilized. For all ten target stations, the MLP-WOA model had precise outcomes than the standalone MLP model. The hybrid model had acceptable performances with lower amounts of the RMSE, SI and RE parameters and higher values of NSE, WI and KGE parameters. It was concluded that WOA optimization algorithm can improve prediction accuracy of MLP model and may be recommended for accurate wind speed prediction.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wind Speed Prediction Using a Hybrid Model of the Multi-Layer Perceptron and Whale Optimization Algorithm

Samadianfard¹,

Hashemi²,

Kargar³

et al. 2020

Preprint

Self Cite

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“…By means of the regression model or neural network, researchers map the time series to the wind power of the future moment, so as to make the prediction. The commonly used methods are SVR (Chen & Yu, 2014), kNN (Becker & Thrn, 2017), Multilayer Perceptron Network (MLP) (Deo et al, 2018;Marvuglia & Messineo, 2012) and Long and Short Term Memory Neural Network ( LSTM ) (Qu et al, 2016), etc., among which SVR and kNN are the representatives.…”

Section: Machine Learning Methods In Wppmentioning

confidence: 99%

Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space

Yu¹,

Liu²,

Li³

et al. 2019

Applied Energy

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Wind power prediction is of vital importance in wind power utilization. There have been a lot of researches based on the time series of the wind power or speed, but In fact, these time series cannot express the temporal and spatial changes of wind, which fundamentally hinders the advance of wind power prediction. In this paper, a new kind of feature that can describe the process of temporal and spatial variation is proposed, namely, Spatio-Temporal Features. We first map the data collected at each moment from the wind turbine to the plane to form the state map, namely, the scene, according to the relative positions. The scene time series over a period of time is a multi-channel image, i.e. the Spatio-Temporal Features. Based on the Spatio-Temporal Features, the deep convolutional network is applied to predict the wind power, achieving a far better accuracy than the existing methods. Compared with the starge-of-the-art method, the mean-square error (M SE) in our method is reduced by 49.83%, and the average time cost for training models can be shortened by a factor of more than 150.

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“…There is no basic and technical way of separating training and testing data. For example, the study by Kurup and Dudani [32] used 63% of total data for model development, whereas Pal [33] used 69%, Samadianfard et al [20,21,34] used 67% of total data, and Deo et al [35] and Samadianfard et al [36] used 70% of the total data to develop their models. Thus, to develop the studied models, the data are divided into training (70%) and testing (30%).…”

Section: Study Areamentioning

confidence: 99%

Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

Samadianfard

Jarhan

Salwana

et al. 2019

Water

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Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow prediction. This paper aims to identify models with higher accuracy, robustness, and generalization ability by inspecting the accuracy of a number of machine learning models. The proposed models for river flow include support vector regression (SVR), a hybrid of SVR with a fruit fly optimization algorithm (FOA) (so-called FOASVR), and an M5 model tree (M5). Additionally, the influence of periodicity (π) on the forecasting enactment was examined. To assess the performance of the proposed models, different statistical meters were implemented, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Bayesian information criterion (BIC). Results showed that the FOASVR with RMSE (4.36 and 6.33 m 3 /s), MAE (2.40 and 3.71 m 3 /s) and R (0.82 and 0.81) values had the best performance in forecasting river flows at Babarud and Vaniar stations, respectively. Also, regarding BIC parameters, Q t−1 and π were selected as parsimonious inputs for predicting river flow one month ahead. Overall findings indicated that, although both the FOASVR and M5 predicted the river flows in suitable accordance with observed river flows, the performance of the FOASVR was moderately better than the M5 and periodicity noticeably increased the performance of the models; consequently, FOASVR can be suggested as the most accurate method for forecasting river flows.

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Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data

Cited by 126 publications

References 64 publications

Wind Speed Prediction Using a Hybrid Model of the Multi-Layer Perceptron and Whale Optimization Algorithm

Wind Speed Prediction Using a Hybrid Model of the Multi-Layer Perceptron and Whale Optimization Algorithm

Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space

Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

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