Multivariate regression models for prediction of wind speed

Arjun, N. N.; Prema, V.; Kumar, Dhirendra; Prashanth, P.; Preekshit, V. Sumantha; Rao, K. Uma

doi:10.1109/icdse.2014.6974632

Cited by 12 publications

(8 citation statements)

References 8 publications

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“…En [5] los autores presentan un método para predecir la velocidad del viento, en el cual la energía generada por el viento depende más eficientemente y ayuda a evitar tanto costos de sobreproducción como de subproducción. Esto puede ser logrado mediante métodos estadísticos en los cuales una gran cantidad de datos son recolectados y analizados en una relación funcional usando modelos de regresión multivariable.…”

Section: Lógica Difusa (Ld)unclassified

“…Principal Contribución Estado RLM N.N. Arjun et al [5] Evita la sobreproducción de energía. EP ST T. H. M. El-Fouly et al [3] Propone mejoras al modelo tradicional de caja gris EI ST E. Cadenas et al [6] Modelo NARX con un desempeño mejorado sobre el modelo de persistencia (11%) y sobre un modelo NAR (4%).…”

Section: Referenciaunclassified

“…En [5] consideraron la humedad relativa y presión atmosférica; en [6] la radiación solar, presión atmosférica y temperatura fueron los predictores. En las dos investigaciones mencionadas, la velocidad del viento es la variable dependiente.…”

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Revisión de literatura de modelos computacionales para la predicción de la velocidad del viento de 2004 a 2016

Tripp-Barba

2020

RITI

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Resumen: El pronóstico de la velocidad del viento es un tema ampliamente investigado en la actualidad. Un factor clave a tener en cuenta es su importancia en el diseño y el cálculo de estructuras para edificios de tamaño considerable que se construirán en zonas de huracanes de alto riesgo. Otra rama de esta línea de investigación incluye su utilidad en la predicción de energía eólica, que no es un tema para discutir en este artículo. Aquí se presenta una clasificación de modelos computacionales para el pronóstico de la velocidad del viento de acuerdo con la técnica matemática utilizada para el pronóstico y basada en la revisión de la literatura. Las técnicas utilizadas para clasificar son: regresión lineal multivariante, redes neuronales artificiales, series de tiempo, lógica difusa y el proceso gaussiano. A lo largo del desarrollo de esta investigación, las redes neuronales artificiales y las aplicaciones de lógica difusa se identificaron como futuras líneas de investigación combinadas con un trabajo eficiente de minería de datos.

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Section: Lógica Difusa (Ld)unclassified

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Revisión de literatura de modelos computacionales para la predicción de la velocidad del viento de 2004 a 2016

Tripp-Barba

2020

RITI

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“…The several ways to predict wind speed include (1) using geographic location and seasonality parameters as predictors, see Fadare [8] who used latitude, longitude and month number as the predictors of wind speed, (2) using meteorological parameters as predictors, see [9,10] in which air temperature, relative humidity and vapor pressure were used to predict monthly mean daily wind speed, and (3) using historical wind speed data as the predictors, see [11]. Multiple linear regression methods [12,13], stochastic regression methods [11] and arti cial intelligence methods [10] have been used to correlate these classes of predictors with wind speed. The following review is tailored within the scope of this work by focusing on the application of arti cial intelligence in time series modelling and forecasting of wind speed.…”

Section: Introductionmentioning

confidence: 99%

“…) k for k = 1, 2 … I p−1 , the weights are w (p) ki , the bias is θ (p) i , the weighted sum from the summer is n (p) i , the nonlinear activation function is g and the nodal output is x (p)i . As seen in Eq (12)…”

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confidence: 99%

A Novel Integration of Hodrick-Prescott Filter and Harmonic Analysis with Machine Learning Methods to Enhance Time Series Prediction Accuracy of Daily and Monthly Wind Speeds

Ozoegwu

2021

Preprint

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In this work, a new hybrid algorithm for modelling time series of daily and monthly wind speed is proposed. The method utilizes Hodrick-Prescott Filter (HPF) to decompose raw wind speed data into trend and cyclic components, and harmonic analysis (HA) is thereafter used to decompose the cyclic component into the periodic and stochastic sub-components. Machine learning (ML) methods are then used to model the time series of both the trend and stochastic components. The predicted wind speeds are finally summed from the individual predictions of the ML methods and harmonic analyses. To highlight the considerably higher predictive accuracy that results from the introduced data pre-treatments with HPF and HA, the proposed hybrid algorithm is compared against the traditional ML methods that are not subjected to the pre-treatments. The proposed hybrid algorithms are highly accurate relative to the traditional ML methods reflecting much higher coefficients of determination and correlation coefficients, and much lower error indices. Artificial neural networks (ANNs), linear regression with interactions (LRI), support vector machine (SVM), rational quadratic Gaussian process regression (RQGPR), fine regression trees (FRTs) and boosted ensembles of trees (BETs) are used as the illustrative machine learning methods. To guarantee both versatility and robustness, the methods are tested on example data drawn from both temperate and tropical conditions.

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Novel application of Relief Algorithm in cascaded artificial neural network to predict wind speed for wind power resource assessment in India

Malik

Yadav

Márquez

et al. 2022

Energy Strategy Reviews

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Multivariate regression models for prediction of wind speed

Cited by 12 publications

References 8 publications

Revisión de literatura de modelos computacionales para la predicción de la velocidad del viento de 2004 a 2016

Revisión de literatura de modelos computacionales para la predicción de la velocidad del viento de 2004 a 2016

A Novel Integration of Hodrick-Prescott Filter and Harmonic Analysis with Machine Learning Methods to Enhance Time Series Prediction Accuracy of Daily and Monthly Wind Speeds

Novel application of Relief Algorithm in cascaded artificial neural network to predict wind speed for wind power resource assessment in India

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