Pattern-Based Wind Speed Prediction Based on Generalized Principal Component Analysis

Hu, Qinghua; Su, Pengyu; Yu, Daren; Liu, Jinfu

doi:10.1109/tste.2013.2295402

Cited by 76 publications

(37 citation statements)

References 33 publications

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“…Santamaría-Bonfil et al [10] and Hu et al [50] reached the same conclusion for wind speed forecasting. More specifically, their models are based on time series with SVM and analogue methods, respectively.…”

Section: Discussionsupporting

confidence: 52%

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Sensitivity Studies for a Hybrid Numerical–Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)

et al. 2019

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This study evaluates the performance of statistical models applied to the output of numerical models for short-term (1-24 h) hourly wind forecasts at three locations in the Basque Country. The target variables are horizontal wind components and the maximum wind gust at 3 h intervals. Statistical approaches such as persistence, analogues, linear regression, and random forest (RF) are used. The verification statistics used are coefficient of determination (R 2 ) and root mean square error (RMSE). Statistical models use three inputs: (1) Local wind observations; (2) extended EOFs (empirical orthogonal functions) derived from past local observations and ERA-Interim variables in a previous 24-h period covering a domain around the area of study; and (3) wind forecasts provided by ERA-Interim. Results indicate that, for horizons less than 1-4 h, persistence is the best model. For longer predictions, RF provides the best forecasts. For horizontal components at 4-24 h horizons, RF slightly outperformed ERA-Interim wind forecasts. For gust, RF performs better than ERA-Interim for all the horizons. Persistence is the most influential factor for 2-5 h. Beyond this horizon, predictors from the ERA-Interim wind forecasts led the contribution. Hybrid numerical-statistical methods can be used to improve short-term wind forecasts.Atmosphere 2020, 11, 45 2 of 22Wind predictions are issued based on meteorological numerical weather forecast (NWF) systems, in which data assimilation (DA) plays an important role in order to properly correct errors in the forecasting model by the use of observations in the data assimilation stages. In these NWF systems, the data assimilation methods optimally estimate the state of the atmosphere [19,20] and a numerical weather forecast model is used to predict the future states of the atmosphere. Two examples of these advanced NWP systems are the ERA-Interim [21] global atmospheric reanalysis model, from 1979 until 2019 [21], and NCEP re-analyses [22]. They are not real-time operational NWF systems, since the DA and the forecasting models are frozen in time, but they are re-run for the whole period using all the available observations commonly used in numerical weather forecasts in order to avoid, as much as possible, errors in the record due to changes in the structure (architecture, resolution, kind of observations processed) of the archives derived from operational NWF systems. In the case of the ERA-Interim re-analyses, besides analyses, forecasts are also provided starting at the analysis times. These forecasts are the ones used in this paper as a surrogate of an operational model. However, re-analyses are quite coarse-resolution models when compared with operational models of the same generation. In the particular case of the ERA-Interim, the spatial resolution is 0.75 • , too coarse for local wind forecasts.To fill this resolution gap, numerical and statistical downscaling techniques are customarily used. Numerical downscaling increases the resolution by nesting a higher-resolution model, such ...

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Section: Discussionsupporting

confidence: 52%

“…Hourly observations were used as target variables with ANN and genetic expression programming (GEP) [48]. A method similar to analogues [49] was also applied to forecast wind speed in the next hour [50]. Even [51] monthly mean wind speed has been forecasted combining time-series with ANN.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Studies for a Hybrid Numerical–Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)

et al. 2019

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“…Xinrong et al [35] modeled a Relevance Vector Machine (RVM) and Empirical Mode Decomposition (EMD) based wind speed forecasting model. Hu et al [36] proposed a pattern based approach for short-term wind prediction to do better than the clustering based approach. Zhang et al [37] carried out work on a hybrid wind speed forecasting based on intelligent optimized algorithm.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems

Ranganayaki

Deepa

2016

The Scientific World Journal

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Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature.

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“…In recent years, due to the rise of large social networks such as Facebook, Flickr and Twitter, Internet data mining is a hot research field [1,2,3,4,5,6,7]. In particularly, event influence evaluation (Event Impacts Assessment, EIA) is one of the frontier subjects [8].…”

Section: Introductionmentioning

confidence: 99%

An Estimate Method of Event Influence Scope Based on Special Field

Xue

Wang

et al. 2015

2015 International Conference on Identification, Information, and Knowledge in the Internet of Things (IIKI)

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The increase of scale and complexity exceeds of Internet big data presents unprecedented opportunities on deeply mining and taking full advantage of the big value. Meanwhile, the discovery, propagation and influence evaluation of web event become popular. The incompleteness and incredibility of Internet big data are a challenge issues for eastimating to the scope of event influence. To solve the above problem,

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Pattern-Based Wind Speed Prediction Based on Generalized Principal Component Analysis

Cited by 76 publications

References 33 publications

Sensitivity Studies for a Hybrid Numerical–Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)

Sensitivity Studies for a Hybrid Numerical–Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)

An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems

An Estimate Method of Event Influence Scope Based on Special Field

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