Wind Speed Forecasting using Different Neural Network Algorithms

Kumar, Sudish; Sahay, Kishan Bhushan

doi:10.1109/iementech.2018.8465313

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…For the global parameter convergence, the estimation and maximization steps are repeated until the difference between the estimated likelihood of two consecutive iterations is below a certain threshold. The initial values are provided by the Levenberg-Marquardt algorithm (LMA) (Kumar & Sahay, 2018).…”

Section: Mixture Hybrid Weibull Parameters Estimationmentioning

confidence: 99%

Estimating mixture hybrid Weibull distribution parameters for wind energy application using Bayesian approach

Guenoupkati,

Salami,

Bokovi

et al. 2023

Int. J. Renew. Energy Dev.

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The Weibull distribution function is essential for planning and designing wind-farm implementation projects and wind-resource assessments. However, the Weibull distribution is limited for those areas with high frequencies of calm winds. One solution is to use the hybrid Weibull distribution. In fact, when the wind speed data present heterogeneous structures, it makes sense to group them into classes that describe the different wind regimes. However, the single use of the Weibull distribution presents fitting errors that should be minimized. In this context, mixture distributions represent an appropriate alternative for modelling wind-speed data. This approach was used to combine the distributions associated with different wind-speed classes by weighting the contribution of each of them. This study proposes an approach based on mixtures of Weibull distributions for modelling wind-speed data in the West Africa region. The study focused on mixture Weibull (WW-BAY) and mixture hybrid Weibull (WWH-BAY) distributions using Bayes' theorem to characterize the wind speed distribution over twelve years of recorded data at the Abuja, Accra, Cotonou, Lome, and Tambacounda sites in West Africa. The parameters of the models were computed using the expectation-maximization (E-M) algorithm. The parameters of the models were estimated using the expectation-maximization (E-M) algorithm. The initial values were provided by the Levenberg-Marquardt algorithm. The results obtained from the proposed models were compared with those from the classical Weibull distribution whose parameters are estimated by some numerical method such as the energy pattern factor, maximum likelihood, and the empirical Justus methods based on statistical criteria. It is found that the WWH-BAY model gives the best prediction of power density at the Cotonou and Lome sites, with relative percentage error values of 0.00351 and 0.01084. The energy pattern factor method presents the lowest errors at the Abuja site with a relative percentage error value of -0.54752, Accra with -0.55774, and WW-BAY performs well for the Tambacounda site with 0.19232. It is recommended that these models are useful for wind energy applications in the West African region.

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Section: Mixture Hybrid Weibull Parameters Estimationmentioning

confidence: 99%

Estimating mixture hybrid Weibull distribution parameters for wind energy application using Bayesian approach

Guenoupkati,

Salami,

Bokovi

et al. 2023

Int. J. Renew. Energy Dev.

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“…Some comparative studies between Bayesian and other methods for wind speed forecasting have been performed in literature. Kumar and Sahay (2018) showed that the BN regularisation algorithm is the best method for forecasting wind speed. The common theme in the wind forecasting applications are in the application for optimisation purposes and for averaging different distribution models.…”

Section: Wind Speed Forecastingmentioning

confidence: 99%

Bayesian Network Modelling for the Wind Energy Industry: An Overview

Adedipe¹,

Shafiee

Zio³

2020

Reliability Engineering & System Safety

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Wind energy farms are moving into deeper and more remote waters to benefit from availability of more space for the installation of wind turbines as well as higher wind speed for the production of electricity. Wind farm asset managers must ensure availability of adequate power supply as well as reliability of wind turbines throughout their lifetime. The operating conditions in deep water environments often change very rapidly and, therefore the decision metrics used in different phases of a wind energy project's lifecycle will have to be updated on a very frequent basis, to guarantee higher wind energy system reliability levels. For this reason, there is a crucial need for the wind energy industry to develop advanced computational tools/techniques that are capable of modelling the possible scenarios in (near) real-time and provide a prompt response to any changes in operational/environmental conditions. Bayesian network (BN) is a popular machine learning (ML) method used for system modelling and decision-making under uncertainty. This paper provides a systematic review and evaluation of existing research on the use of BN models in the wind energy sector. To conduct this literature review, all relevant databases from inception to date were searched, and a total of 70 sources (including journal publications, conference proceedings, PhD dissertations, industry reports, best practice documents and software user guides) which met the inclusion criteria were identified, excluding references used in other sections of the text for discussion. Our review findings reveal that the applications of BNs in the wind energy industry are quite diverse, ranging from wind power and weather forecasting to risk management, fault diagnosis and prognosis, structural analysis, reliability assessment, and maintenance planning and updating. Furthermore, a number of case studies are presented to illustrate the applicability of BNs in practice. Although the paper details information applicable to the wind energy industry, knowledge can be transferred to many other sectors.

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“…Doğaya verdiği zarar minimizasyonu ve kurulumunda güneş enerjisi tarlaları kadar yer kaplamaması dolayısıyla günümüzde rüzgar enerjisi, önemini daha da artırmıştır. Ancak rüzgar enerjisinin doğru planlama ile üretilmesi ve verimli kullanılıp dağıtılabilmesi için, rüzgar enerjisi üretim tahminlerinin de en doğru şekilde yapılması gerekmektedir [1]. Hur [5], çalışmasında iki farklı aşamadan oluşan rüzgar gücü tahmin şeması sunmaktadır.…”

Section: Introductionunclassified

Çukurova Bölgesi için Kısa Vadeli Yapay Zeka Tabanlı Rüzgar Güç Tahmini

TEKİN¹

2022

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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Son yıllarda hızla artan nüfus ve sanayi artışının yarattığı enerji ihtiyacı kısıtlı kaynaklarla karşılanamaz hale gelmiştir. Enerji üretimi ve tüketimi arasında oluşan farklılıklar, kısıtlı kaynakların yerini yenilenebilir enerjilerin gelişimine bırakmıştır. Sağlığımızı tehdit eden unsurları en aza indirmeyi planlayan Avrupa Yeşil Mütabakatı, 2030 yılına kadar dünya genelinde yenilenemez enerjilerin kullanımını minimum seviyelere indirecektir. Ayrıca iklim krizinin, sera gazı salınımını önemli ölçüde etkileyeceği ve doğaya zarar vereceği öngörülmektedir. Karbon emisyonunun sıfıra indirilebilmesi prensibinde, rüzgar gücü tahmini çalışmaları oldukça önemlidir. Ancak rüzgar enerjisinde yaşanan sıkıntı, üretiminin meterolojik şartlar doğrultusunda sürekli değişmesidir. Voltaj ve frekans değişiklerinin yarattığı enerji kararsızlığının önüne geçilebilmesi için denge şebekelerdeki üretim ve tüketimin sürekli olarak sağlanması gerekmektedir. Sistemlerin modelleme süresini ve doğruluğunu etkileyen rüzgar hızındaki doğrusal olmayan bu değişiklikler, enerjisi kayıplarının en aza indirilebilmesi için önemlidir. Bu çalışmada, Çukurova Bölgesi’nden elde edilen gerçek kısa vadeli rüzgar gücü verileri araştırma nesnesi olarak alınmış, MPE-MAPE tasarlanan tahmin modellerinin performans indekslerini karşılaştırmak için kullanılmıştır.

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Wind Speed Forecasting using Different Neural Network Algorithms

Cited by 5 publications

References 2 publications

Estimating mixture hybrid Weibull distribution parameters for wind energy application using Bayesian approach

Estimating mixture hybrid Weibull distribution parameters for wind energy application using Bayesian approach

Bayesian Network Modelling for the Wind Energy Industry: An Overview

Çukurova Bölgesi için Kısa Vadeli Yapay Zeka Tabanlı Rüzgar Güç Tahmini

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