A review of wind power and wind speed forecasting methods with different time horizons

Soman, Saurabh S.; Zareipour, Hamidreza; Malik, O.P.; Mandal, Paras

doi:10.1109/naps.2010.5619586

Cited by 631 publications

(422 citation statements)

References 45 publications

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“…Its usage is very limited, but is still used in the industry for making very short-term predictions (Soman et al, 2010). The physical models, which utilise data of various atmospheric parameters to build up complex mathematical models, furnish another classical way to forecast wind speed.…”

Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

“…The simplest among the prediction schemes for wind speed is the method of persistence which is based on the assumption that over very small time intervals wind speed does not change appreciably. Its usage is very limited, but is still 80 used in the industry for making very short term predictions (Soman et al, 2010). The physical models, which utilise data of various atmospheric parameters to build up complex mathematical models, furnish another classical way to forecast wind speed.…”

Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

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Deterministic prediction of surface wind speed variations

Drisya

Kiplangat

Asokan³

et al. 2014

Ann. Geophys.

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Abstract. Accurate prediction of wind speed is an important aspect of various tasks related to wind energy management such as wind turbine predictive control and wind power scheduling. The most typical characteristic of wind speed data is its persistent temporal variations. Most of the techniques reported in the literature for prediction of wind speed and power are based on statistical methods or probabilistic distribution of wind speed data. In this paper we demonstrate that deterministic forecasting methods can make accurate short-term predictions of wind speed using past data, at locations where the wind dynamics exhibit chaotic behaviour. The predictions are remarkably accurate up to 1 h with a normalised RMSE (root mean square error) of less than 0.02 and reasonably accurate up to 3 h with an error of less than 0.06. Repeated application of these methods at 234 different geographical locations for predicting wind speeds at 30-day intervals for 3 years reveals that the accuracy of prediction is more or less the same across all locations and time periods.Comparison of the results with f-ARIMA model predictions shows that the deterministic models with suitable parameters are capable of returning improved prediction accuracy and capturing the dynamical variations of the actual time series more faithfully. These methods are simple and computationally efficient and require only records of past data for making short-term wind speed forecasts within practically tolerable margin of errors.

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Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

Deterministic prediction of surface wind speed variations

Drisya

Kiplangat

Asokan³

et al. 2014

Ann. Geophys.

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“…Considering the random fluctuation of solar energy resources, the access to grid-connected PV power generation relies on accurate and reliable power forecasting for the safety, stability, and economical operation of electric power system [3]. PV power forecasting can be performed using physical methods or statistical methods [4]. However, the modeling process of physical methods is complex, and the results may not be applicable for some extreme cases.…”

Section: Introductionmentioning

confidence: 99%

An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window

Zhu

Lian

et al. 2017

Energies

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Due to the large scale of grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economic operation of the electric power system. In the paper, by analyzing the influence of external ambient factors and the changing characteristics of PV modules with time, it is found that PV power generation is a nonlinear and time-varying process. This suggests that a certain single forecasting model is inadequate for representing actual generation characteristics, and it is difficult to obtain an accurate forecasting result. An adaptive back propagation (BP) neural network model adopting scrolling time window is proposed to solve the problem. Via an update of the training data of BP neural network with the scrolling time window, the forecasting model adapts to time and a changing external environment with the required modeling precision. Meanwhile, through evaluation of the forecasting performance in different time windows, an optimized time window can be determined to guarantee accuracy. Finally, using the actual operation data of a PV plant in Beijing, the approach is validated as being applicable for PV power forecasting and is able to effectively respond to the dynamic change of the PV power generation process. This improves the forecasting accuracy and also reduces computation complexity as compared with the conventional BP neural network algorithm.

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“…Wind power prediction methods are developed to deal with this problem and aim to predict generated power based on historical weather/wind data by utilising data mining methods (Wang et al 2011(Wang et al , 2016Colak et al 2012;Soman et al 2010;Zhao et al 2016;Jiang et al 2017). In general, historical wind data obtained from weather stations are used by data mining algorithms to make the predictions.…”

Section: Introductionmentioning

confidence: 99%

Autoencoder for wind power prediction

Tasnim

Rahman

et al. 2017

Renewables

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Successful integration of renewable energy sources like wind power into smart grids largely depends on accurate prediction of power from these intermittent sources. Production of wind power cannot be controlled as the wind speed can vary based on weather conditions. Accurate prediction of wind power can assist smart grid that intelligently decides on the usage of alternative power sources based on demand forecast. Time series wind speed data are normally used for wind power prediction. In this paper, we have investigated the usage of a set of secondary features obtained using deep learning for wind power prediction. Deep learning is a special form on neural network that is capable of capturing the structural properties of time series data in terms of a set of numeric features. More precisely, we have designed a two-stage autoencoder (a particular type of deep learning) and incorporated the structural features into a prediction framework. Using the structural features, we have achieved as high as 12.63% better prediction accuracy than traditionally used statistical features.© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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A review of wind power and wind speed forecasting methods with different time horizons

Cited by 631 publications

References 45 publications

Deterministic prediction of surface wind speed variations

Deterministic prediction of surface wind speed variations

An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window

Autoencoder for wind power prediction

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