ARMA based approaches for forecasting the tuple of wind speed and direction

Erdem, Ergin; Shi, Jing

doi:10.1016/j.apenergy.2010.10.031

Cited by 781 publications

(290 citation statements)

References 37 publications

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“…At present, the prediction method of short-term wind speed is mainly based on historical data. These prediction methods usually use historical data, through some linear models include autoregressive moving average model (ARMA) [9,34], autoregressive integrated moving average model (ARIMA) [2]. The nonlinear model include SVM [8,12], LSSVM [36,39], artificial neural network (Elman neural network [44,45], echo state network [38], fuzzy neural network [6,30], RBF neural network [4,23], and etc to predict short-term wind speed.…”

Section: Review Of Short-term Wind Speed Predictionmentioning

confidence: 99%

Fuzzy Multi-Objective Optimization Algorithms for Solving Multi-Mode Automated Guided Vehicles by Considering Machine Break Time and Artificial Neural Network

Nabovati¹,

Haleh²,

Vahdani³

2018

NNW

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Abstract:As an improved algorithm of standard extreme learning machine, online sequential extreme learning machine achieves excellent classification and regression performance. However, online sequential extreme learning machine gives the same weight to the old and new training samples, and fails to highlight the importance of the new training samples. At the same time, the algorithm updates the network weights after obtaining the new training samples. This network weight updating mode lacks flexibility and increases unnecessary computation. This paper proposes an adaptive online sequential extreme learning machine with an effective sample updating mechanism. The new and old samples are given different weights. The effect of new training samples on the algorithm is further enhanced, which can further improve the regression prediction ability of extreme learning machine. At the same time, an improved artificial bee colony algorithm is proposed and used to optimize the parameters of the adaptive online sequential extreme learning machine. The stability and convergence property of proposed prediction method are proved. The actual collected short-term wind speed time series is used as the research object and verify the prediction perfromance of the proposed method. Multi step prediction simulation of short-term wind speed is performed out. Compared with other prediction methods, the simulation results show that the proposed approach has higher prediction accuracy and reliability performance, meanwhile improve the performance indicators.

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Section: Review Of Short-term Wind Speed Predictionmentioning

confidence: 99%

Fuzzy Multi-Objective Optimization Algorithms for Solving Multi-Mode Automated Guided Vehicles by Considering Machine Break Time and Artificial Neural Network

Nabovati¹,

Haleh²,

Vahdani³

2018

NNW

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“…On the other hand, predictions of other meteorological variables besides the wind speed are often added in models. In some works, researchers have proven that the combination of wind direction and wind speed is effective in reducing the error of prediction [18,19], temperature and pressure can also improve the performance of statistical models [20,21], and the spatial interdependency of different variables has been proven to be effective by some studies [22]. However, in many circumstances, especially under complicated weather conditions, these parameters still cannot offer enough information for bias estimation and sometimes even worsen the forecast results, which implies that some additional or more relevant parameters are needed to provide more complete information.…”

Section: Introductionmentioning

confidence: 99%

A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

Chu

Wang

et al. 2016

Energies

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Abstract:Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF) model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28-52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction.

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“…ARMA models have been used in diverse areas of applications such as speech [2], [3], seismology [4], video [5], image [6], etc. Particularly, they have been applied in energy and meteorological prediction studies of solar radiation [7], [8], electricity demand [9], [10] and wind speed [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear model selection for PARMA processes using RJMCMC

Karakuş

Kuruoğlu

Altınkaya

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Many prediction studies using real life measurements such as wind speed, power, electricity load and rainfall utilize linear autoregressive moving average (ARMA) based models due to their simplicity and general character. However, most of the real life applications exhibit nonlinear character and modelling them with linear time series may become problematic. Among nonlinear ARMA models, polynomial ARMA (PARMA) models belong to the class of linear-in-the-parameters. In this paper, we propose a reversible jump Markov chain Monte Carlo (RJMCMC) based complete model estimation method which estimates PARMA models with all their parameters including the nonlinearity degree. The proposed method is unique in the manner of estimating the nonlinearity degree and all other model orders and model coefficients at the same time. Moreover, in this paper, RJMCMC has been examined in an anomalous way by performing transitions between linear and nonlinear model spaces.

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ARMA based approaches for forecasting the tuple of wind speed and direction

Cited by 781 publications

References 37 publications

Fuzzy Multi-Objective Optimization Algorithms for Solving Multi-Mode Automated Guided Vehicles by Considering Machine Break Time and Artificial Neural Network

Fuzzy Multi-Objective Optimization Algorithms for Solving Multi-Mode Automated Guided Vehicles by Considering Machine Break Time and Artificial Neural Network

A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

Nonlinear model selection for PARMA processes using RJMCMC

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