Wind power forecasting by an empirical model using NWP outputs

Pelikán, Emil; Eben, Kryštof; Resler, Jaroslav; Juruš, Pavel; Krč, Pavel; Brabec, Marek; Brabec, T.; Musilek, Petr

doi:10.1109/eeeic.2010.5490019

Cited by 18 publications

(6 citation statements)

References 5 publications

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“…Some of them use numerical weather prediction (NWP) models capable of forecasting meteorological variables accurately [16]. Other techniques available in the literature, such as a persistence model, an autoregressive moving average model, artificial neural networks, support vector machines and wavelet transforms are based on obtaining detailed information about the intrinsic nature of the wind power time series [17].…”

Section: B Resultsmentioning

confidence: 99%

A probabilistic approach to solve economic dispatch problem in systems with intermittent power sources

Lujano-Rojas

Osório

Catalão

2014

2014 IEEE PES T&D Conference and Exposition

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As a crucial factor in global energy consumption, environmental problems related to the greenhouse gas emissions and high oil prices have motivated the growth and incorporation of alternative sources of energy into power systems. However, one of the most important facets of such problems is their intermittent nature, which means that the operation and management of a power system is a difficult task. In this paper, a probabilistic approach to solving the economic dispatch (ED) problem under conditions of uncertainty is presented. The proposed methodology allows for obtaining the probability distribution function (PDF) of all generation units, the energy not supplied (ENS) and the total generation cost. A case study based on an insular power system is analyzed, with reference to the important relationship that exists between the PDF of net load demand and the PDF of ENS, power production and total generation cost.

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Section: B Resultsmentioning

confidence: 99%

A probabilistic approach to solve economic dispatch problem in systems with intermittent power sources

Lujano-Rojas

Osório

Catalão

2014

2014 IEEE PES T&D Conference and Exposition

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“…Now, Wind speed forecasting methods can be divided into two kinds of the prediction methods which are based on physical model and the historical data. The prediction method based on the physical model commonly used numerical weather prediction (NWP) data to forecast wind speed [6,7]. NWP wind speed prediction is not targeted a wind turbine, but on a regional to forecast wind speed.…”

Section: Characteristic and Prediction Methods Of Wind Speedmentioning

confidence: 99%

Short-term wind speed prediction based on support vector machine of fuzzy information granulation

Cheng¹,

Guo²

2013

2013 25th Chinese Control and Decision Conference (CCDC)

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Wind power prediction is an effective way to decrease the effects for the large-scale grid-connected wind power generation. Improving accuracy of short-term wind prediction is the key to wind power prediction. There is often a lot of redundant information in the observed values of wind speed to result in large computation and affect the predictive validity. In this paper, a support vector machine (SVM) method was proposed based on information granulating. Firstly, original data was dealt with fuzzy information granulation to form information granulation.Secondly, adopting SVM is widely used in regression prediction to predict the changes of short-term average wind speed in trends and in space. At last, the actual data was compared with the predicted data to verify results. The verification experiments show that granulated data can not only reflect the characteristics of wind but also reduce redundant information. The predicted result was changing in the space of average wind speed. So, this method can predict the short-term wind speed space.

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“…The prevailing wind power prediction approaches can be briefly divided into four categories, namely physical modeling, statistical modeling, intelligent modeling, and hybrid structure modeling. As to physical models, numerical weather prediction (NWP), which could generate forecasting results of long-term and large-scale and based on ensemble of sigmoidal power function, was developed by Pelikan et al [20] to calculate week-ahead or day-ahead forecasting [21]. Nevertheless, technologies with physical-based requires typically fruitful characteristic value, such as atmospheric density, wind farm area topography and surface roughness of parts [9], whose applications are limited universally in shortterm wind power forecasting [22], [23].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Short-Term Wind Power Prediction Model Combining Data Processing, Multiple Parameters Optimization and Multi-Intelligent Models Apportion Strategy

Yan

2020

IEEE Access

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As the wind power dates have strong volatility and intermittency, it is difficult to meet the safety and stability of power system operation. To make full use of the characteristics of wind power data and further improve wind power forecasting performance, an innovative hybrid framework is proposed to response to the above challenges, of which the non-stationary weakening technique, sample entropy (SE)-based prediction model allocation, optimized reduced kernel extreme learning machine (RKELM) and a novel deep learning network are integrated befittingly. Initially, the original wind power sequence is decomposed into multiple sub-sequences by empirical wavelet transform (EWT) adaptively, after which SE is employed to analyse the high and low components of sub-sequences. Then, a simplified gate recurrent unit network (SGRU) to achieve the prediction of the component with low SE-value. Besides, for the components with high SE-values, the multiple parameters optimization is implemented for RKELM based on slime mould algorithm (SMA), which possesses superior convergence speed and precision. Ultimately, the final prediction results can be obtained by accumulating the predicted values of the multiple models. In the experiment phase, two datasets are adopted to verify the predictive ability of the proposed hybrid EWT-SMA-RKELM-SGRU model, where the sufficient results indicate that the proposed model has a superior performance.INDEX TERMS short-term wind power forecasting, prediction model allocation, slime mould algorithm, multiple parameters optimization, simplified gate recurrent unit network

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Wind power forecasting by an empirical model using NWP outputs

Cited by 18 publications

References 5 publications

A probabilistic approach to solve economic dispatch problem in systems with intermittent power sources

A probabilistic approach to solve economic dispatch problem in systems with intermittent power sources

Short-term wind speed prediction based on support vector machine of fuzzy information granulation

A Hybrid Short-Term Wind Power Prediction Model Combining Data Processing, Multiple Parameters Optimization and Multi-Intelligent Models Apportion Strategy

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