Current status and future advances for wind speed and power forecasting

Jung, Jaesung; Broadwater, Robert

doi:10.1016/j.rser.2013.12.054

Cited by 589 publications

(280 citation statements)

References 111 publications

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“…Wind speed prediction problem is widely investigated in literature and various methods are presented [5,[7][8][9][10][11][12][13]. The available methods are generally separated as physical and statistical methods.…”

Section: Introductionmentioning

confidence: 99%

Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting

Filik

2016

Energies

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Abstract:In this paper, the spatio-temporal (multi-channel) linear models, which use temporal and the neighbouring wind speed measurements around the target location, for the best short-term wind speed forecasting are investigated. Multi-channel autoregressive moving average (MARMA) models are formulated in matrix form and efficient linear prediction coefficient estimation techniques are first used and revised. It is shown in detail how to apply these MARMA models to the spatially distributed wind speed measurements. The proposed MARMA models are tested using real wind speed measurements which are collected from the five stations around Canakkale region of Turkey. According to the test results, considerable improvements are observed over the well known persistence, autoregressive (AR) and multi-channel/vector autoregressive (VAR) models. It is also shown that the model can predict wind speed very fast (in milliseconds) which is suitable for the immediate short-term forecasting.

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Section: Introductionmentioning

confidence: 99%

Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting

Filik

2016

Energies

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“…As a kind of clean energy, wind power is receiving increasing attention and application in the world, under the recent concern about energy crisis and global warming issues [1,2]. However, a wind field's output power strongly depends on local real-time wind speed and is thus uncontrollable.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, a statistical correction to NWP is an effective means to reduce prediction errors without the potentially expensive cost to improve the model scheme and initial fields [7][8][9][10]. There has been a lot of work testing and improving various statistical correction methods in order to improve the forecast skill of NWPs [1]. Typical approaches include comparison and combination of different statistical models [11][12][13][14] and NWP datasets [15], and incorporating more input parameters.…”

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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“…A review of the young history of methods for short term prediction of such a kind of time series was given in [1], while more recent references can be found in [2] and [3]. Unfortunately, literature results seem pointing out that even by using sophisticate modeling techniques, wind speed time series can be predicted with some accuracy only at short time horizon, i.e.…”

Section: Introductionmentioning

confidence: 99%

One day ahead prediction of wind speed class

Fortuna

Nunnari

Guariso

2015

2015 International Conference on Renewable Energy Research and Applications (ICRERA)

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Abstract-This paper deals with the problem of clustering daily wind speed time series based on two features referred to as Wr and H, representing a measure of the relative daily average wind speed and the Hurst exponent, respectively. Daily values of the pairs (Wr, H) are first classified by means of the fuzzy c-means unsupervised clustering algorithm and then results are used to train a supervised MLP neural network classifier. It is shown that associating to a true wind speed time series a time series of classes, allows performing some useful statistics. Further, the problem of predicting 1-step ahead the class of daily wind speed is addressed by introducing NAR sigmoidal neural models into the classification process. The performance of the prediction model is finally assessed.

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Current status and future advances for wind speed and power forecasting

Cited by 589 publications

References 111 publications

Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting

Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting

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

One day ahead prediction of wind speed class

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