Support vector machines for wind speed prediction

Mohandes, M.; Halawani, T.O.; Rehman, Shafiqur; Hussain, Arshad

doi:10.1016/j.renene.2003.11.009

Cited by 649 publications

(244 citation statements)

References 5 publications

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“…To forecast the weather of a particular region where wind plants are installed and operated, first, the resolution of weather model has to be high and geographical data that are applicable to resolution has to be built. Especially, since the wind that is the energy source of wind power gets influenced largely by the geography, detailed geographical data has to be built in order to get accurate wind prediction [2]. In this study, we set 3 prediction areas in Mt.…”

Section: A Process Of Initial Meteorological Model and Geographical Mapmentioning

confidence: 99%

The Prediction of Wind Power Generation: A Case Study in South Korea

Kim¹,

Shin²,

Park³

et al. 2017

JOCET

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Abstract-It is difficult to predict the power generation amount of wind turbines because it is depending on weather condition. However it is important to predict wind power generation amount accurately, because it is possible to maximize the utilization of wind turbines, and also establish more effective operation plan of grid. The purpose of this study is to increase the accuracy of prediction of the power generation amount. To increase accuracy, we built the 3 dimensional weather research and forecasting model that is the most applicable to our developing district environment. In case of short-term prediction within 6 hours, to overcome the weakness that cannot reflect the regional characteristics for particular areas enough, we predicted weather information considering statistical characteristics such as weather pattern change information of wind plants. Using the result of weather prediction as a basic data, we computed the power generation amount by plants and wind generators. In the proposed approaches, inaccuracies are analyzed based on the combination of physical model and statistical model. The result of the analysis showed 15.7% (within 24 hours), and 17.5% (within 48 hours) error rate.Index Term-Short and midterm prediction of wind power, wind power generation forecasting system, distributed generation.

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Section: A Process Of Initial Meteorological Model and Geographical Mapmentioning

confidence: 99%

The Prediction of Wind Power Generation: A Case Study in South Korea

Kim¹,

Shin²,

Park³

et al. 2017

JOCET

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“…ANNs have broad applications in many fields such as mathematics, engineering, medicine, economics, meteorology, psychology, and neurology. This method learns from given examples by constructing an input-output mapping in order to achieve estimations [19]. This clearly implies that, input data and corresponding output values are required to train and test a neural network [20].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Yapay Sinir Ağları ile Atmosferik Parametreler Kullanılarak Türkiye için Güneş Radyasyonu Modellemesi

Şenkal¹

2016

Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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“…However, in many practical cases, the use of wind speed observation records is restricted by two factors, one is gaps in the wind speed records caused by instrument failure or the destruction of the meteorological mast, the other is lack of long-term wind speed records. To overcome these drawbacks, one or more reference sites have been chosen and the relationship between the target and reference sites have been constructed using statistical methods (Measure-Correlate-Predict (MCP) methods [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]), or physical methods (for example, Wind Atlas Analysis and Application Program (WAsP) [18][19][20]). As per references [13,20], statistical methods tend to provide higher accuracy and are therefore used widely in practical engineering.…”

Section: Introductionmentioning

confidence: 99%

“…MCP methods based on linear regression have been validated in some practical applications [8,9]. Support vector machine (SVM) model was also used for correlation, Mohandes et al [10] introduced the SVM for wind speed prediction and compared it with the multilayer perceptron (MLP) neural networks, the results proved that the SVM model had less root mean square errors than the MLP model. Ji et al [11] did further research on SVM, a support vector classifier was utilized to estimate the forecasting error and lower mean square error and mean absolute percentage error than traditional SVM method were obtained.…”

Section: Introductionmentioning

confidence: 99%

A New MCP Method of Wind Speed Temporal Interpolation and Extrapolation Considering Wind Speed Mixed Uncertainty

Liu

Zhou

2017

Energies

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Abstract:In this paper, a missing wind speed data temporal interpolation and extrapolation method in the wind energy industry was investigated. Given that traditional methods have previously ignored part of mixed uncertainty of wind speed, a concrete granular computing method is constructed and a new Measure-Correlate-Predict (MCP) method of wind speed data temporal interpolation and extrapolation considering all mixed uncertainties is proposed, based on granular computing theory by adopting the cloud model method, support vector regression method, artificial neural network, genetic algorithm, and fuzzy c-means clustering algorithm as tools. The importance of considering mixed wind speed uncertainty and the suitability of using granular computing method are illustrated, and wind speed mixed uncertainty analysis is implemented, then, recommended values and estimation tools for wind speed measurement uncertainty and combined uncertainty are provided. An interpolation case of two practical meteorological sites in central Southern China was used to implement and validate the method proposed in this paper. The following conclusions are reached: (a) by using the method proposed in this paper, mixed uncertainty of wind speed can be considered, comparing to other MCP methods used for purposes of comparison, a better estimation of the wind speed is provided, and most evaluation metrics employed in this analysis were superior to other methods, that is to say, the accuracy of the wind resource assessment improved, and the risks of wind farm construction were reduced; (b) granular computing method is suitable for the issue of wind speed data interpolation and extrapolation considering wind speed mixed uncertainty; (c) mixed uncertainty of wind speed can be divided into three levels, and recommended values of granularity are minimum interval of records, 0.3-0.8 m/s, and 1-3 m/s, respectively.

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Support vector machines for wind speed prediction

Cited by 649 publications

References 5 publications

The Prediction of Wind Power Generation: A Case Study in South Korea

The Prediction of Wind Power Generation: A Case Study in South Korea

Yapay Sinir Ağları ile Atmosferik Parametreler Kullanılarak Türkiye için Güneş Radyasyonu Modellemesi

A New MCP Method of Wind Speed Temporal Interpolation and Extrapolation Considering Wind Speed Mixed Uncertainty

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