Evaluation of neural network-based methodologies for wind speed forecasting

Samet, Haidar; Reisi, Mohammad; Marzbani, Fatemeh

doi:10.1016/j.compeleceng.2019.07.024

Cited by 15 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These methods are divided into (i) supervised learning where both inputs and output are provided, (ii) unsupervised learning where only the input data is provided, (iii) reinforcement learning represented by a mixture of supervised and unsupervised learning, and (iv) evolutionary learning, which is considered a biological approach to machine learning. On the other hand, wind forecasting methods are classified into four categories: physical methods, statistical methods, hybrid methods, and artificial intelligence methods [17,19,33]. Other authors, such as Sun et al [34] listed the persistence method or "Naïve Predictor", which assumes a correlation between the wind speed at a time "t + x" and the current wind speed at the time "t", where both speeds are assumed to be the same.…”

Section: Related Workmentioning

confidence: 99%

Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia

Brahimi

2019

Energies

View full text Add to dashboard Cite

Predicting wind speed for wind energy conversion systems (WECS) is an essential monitor, control, plan, and dispatch generated power and meets customer needs. The Kingdom of Saudi Arabia recently set ambitious targets in its national transformation program and Vision 2030 to move away from oil dependence and redirect oil and gas exploration efforts to other higher-value uses, chiefly meeting 10% of its energy demand through renewable energy sources. In this paper, we propose the use of the artificial neural networks (ANNs) method as a means of predicting daily wind speed in a number of locations in the Kingdom of Saudi Arabia based on multiple local meteorological measurement data provided by K.A.CARE. The suggested model is a feed-forward neural network model with the administered learning technique using a back-propagation algorithm. Results indicate that the best structure is obtained with thirty neurons in the hidden layers matching a minimum root mean square error (RMSE) and the highest correlation coefficient (R). A comparison between predicted and actual data from meteorological stations showed good agreement. A comparison between five machine learning algorithms, namely ANN, support vector machines (SVM), random tree, random forest, and RepTree revealed that random tree has low correlation and relatively high root mean square error. The significance of the present study relies on its ability to predict wind speeds, a necessary prerequisite to executing sustainable integration of wind power into Saudi Arabia’s electrical grid, assisting operators in efficiently managing generated power, and helping achieve the energy efficiency and production targets of Vision 2030.

show abstract

Section: Related Workmentioning

confidence: 99%

Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia

Brahimi

2019

Energies

View full text Add to dashboard Cite

show abstract

“…This ANN is characterized by the variable Z, which is the time delay. The Z factor works as a memory that provides current and previous input values (Santos, et al, 2022;Santos, et al, 2020;Samet, et al, 2019).…”

Section: Prediction Via Artificial Neural Networkmentioning

confidence: 99%

Rede neural artificial aplicada aos casos notificados de dengue cases em Maceió – Alagoas

Santos

Júnior

Barbosa

et al. 2022

RSD

View full text Add to dashboard Cite

A dengue é um dos graves problemas de saúde pública mundial. O Nordeste do Brasil (NEB) possui um clima e ambiente urbano ideal para a proliferação do mosquito Aedes (aegypti e albopictus), vetor da doença. O Estado de Alagoas, principalmente a sua capital, tem epidemias da doença de forma frequente. Portanto, o objetivo deste estudo é avaliar a aplicação de Rede Neural Artificial (RNA) nos casos notificados de dengue (CND) nas regiões administrativas (RA) de Maceió. As RAs são divididas em: RA1, RA2, RA3, RA4, RA5, RA6, RA7 e RA8. Os CND foram submetidos a RNA não linear autorregressiva (NAR) – (RNA-NAR). O período de estudo foi de 2011 a 2020. Os resultados obtidos de CND se destacaram em anos específicos (2012, 2013, 2017, 2018 e 2020), por outro lado houve superestimativas das previsões via RNA. Em algumas RAs houve subnotificações e, por isso interferiu nos resultados das previsões. A RNA-NAR foi validada, visto que a maioria das previsões apresentou correlação positiva e com resposta aos dados observados, exceto as RAs com subnotificações. O uso da RNA é adequado no alerta e previsão da donça, onde tal instrumento pode ser usado em ações preventivas de controle da doença.

show abstract

“…Increasing the penetration of wind power in the power system has been associated with many challenges. A main obstacle to the integration of wind power into the grid is its variability [5][6][7]. Assess and analyses as well as provide solutions for problems caused by wind farms, especially connected to the grid, require an appropriate and accurate model of wind generation systems.…”

Section: Introductionmentioning

confidence: 99%

Non stationary‐ARMA flicker model for squirrel cage induction generators based wind farms

Samet

Ketabipour

Bagheri

2021

IET Renewable Power Gen

Self Cite

View full text Add to dashboard Cite

Time varying nature of the wind power is studied previously considering different time intervals from seconds to days. However for power quality problems such as flicker, a model which considers the extremely fast variations is essential. Here by using large number of actual records, a time-varying model is proposed which considers the extremely short-time variations of wind active and reactive powers. The wind farm is modelled as a current source with time varying amplitude and phase which change every 0.01 s. Autoregressive moving-average (ARMA) models are utilized to model the variations and ARMA coefficients are calculated for every record. Same to the actual behaviour, the proposed model is non-stationary as the ARMA order and coefficients are different at every run of the model. The proposed model is confirmed through utilizing several applications which need the power time series with extremely short sampling intervals. The following studies are performed by using the actual data and then the proposed model: power spectral density of active and reactive power variations, instantaneous flicker, short term flicker (Pst), estimating the Pst using the maximum value of the instantaneous flicker, estimation of cumulative Pst for multiple wind turbines, and the impact of SVC on flicker mitigation. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Evaluation of neural network-based methodologies for wind speed forecasting

Cited by 15 publications

References 12 publications

Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia

Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia

Rede neural artificial aplicada aos casos notificados de dengue cases em Maceió – Alagoas

Non stationary‐ARMA flicker model for squirrel cage induction generators based wind farms

Contact Info

Product

Resources

About