Short-term spatio-temporal prediction of wind speed and direction

Dowell, Jethro; Weiss, Stephan; Hill, David; Infield, David

doi:10.1002/we.1682

Cited by 44 publications

(24 citation statements)

References 38 publications

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“…In addition, the data-driven detection of dependence structures means that the benefits of a spatial treatment can be realised without knowledge of precise locations, or in situations where many generators are located in a small area, as is commonplace in the smart grid paradigm. This technique is equally applicable to other forecasting problems where VARs have been used, such as wind speed [15] and solar power forecasting [27], including short-term forecasting at other temporal resolutions, e.g. hourly.…”

Section: Discussionmentioning

confidence: 99%

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Very-Short-Term Probabilistic Wind Power Forecasts by Sparse Vector Autoregression

Dowell

Pinson

2015

IEEE Trans. Smart Grid

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183

115

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Link back to DTU OrbitCitation (APA): Dowell, J., & Pinson, P. (2016). Very-short-term wind power probabilistic forecasts by sparse vector autoregression. IEEE Transactions on Smart Grid, 7(2), 763 -770. DOI: 10.1109/TSG.2015 Abstract-A spatio-temporal method for producing very-shortterm parametric probabilistic wind power forecasts at a large number of locations is presented. Smart grids containing tens, or hundreds, of wind generators require skilled very-short-term forecasts to operate effectively, and spatial information is highly desirable. In addition, probabilistic forecasts are widely regarded as necessary for optimal power system management as they quantify the uncertainty associated with point forecasts. Here we work within a parametric framework based on the logit-normal distribution and forecast its parameters. The location parameter for multiple wind farms is modelled as a vector-valued spatiotemporal process, and the scale parameter is tracked by modified exponential smoothing. A state-of-the-art technique for fitting sparse vector autoregressive models is employed to model the location parameter and demonstrates numerical advantages over conventional vector autoregressive models. The proposed method is tested on a dataset of 5 minute mean wind power generation at 22 wind farms in Australia. 5-minute-ahead forecasts are produced and evaluated in terms of point and probabilistic forecast skill scores and calibration. Conventional autoregressive and vector autoregressive models serve as benchmarks.Index Terms-Probabilistic forecasting, wind power, power system operations, renewable energy.

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Section: Discussionmentioning

confidence: 99%

“…Spatial correlation between wind speed and direction has been exploited in [14] by regressing on different spatial information depending on the wind direction, and in [15] by fitting vector autoregressivetype models. Using multiple wind farms as 'spatial sensors' was shown to improve wind power forecast skill at a target site in [16].…”

Section: Introductionmentioning

confidence: 99%

Very-Short-Term Probabilistic Wind Power Forecasts by Sparse Vector Autoregression

Dowell

Pinson

2015

IEEE Trans. Smart Grid

Self Cite

183

115

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“…These methods use measurements from neighbourhood of target location (wind farm) for more accurate wind speed forecasting with a modest processing overhead [15][16][17][18][19][20][21]. Since wind is a horizontal movement in atmosphere, its spatial correlation carries important information for such spatial models.…”

Section: Introductionmentioning

confidence: 99%

“…In [5], a methodology is proposed for optimum probabilistic forecasting of geographically dispersed information. In [19], multichannel adaptive filtering technique is applied for short-term prediction which promise lower complexity, improved robustness and ability to track seasonal variations. Most of the above methods are based on the statistical analysis and interpretation of the location specific multi-channel data collected in years.…”

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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“…A number of methods have been developed to model the spatio-temporal relationship between multiple measurement locations to produce accurate predictions [3][4][5]. In addition, wind direction is frequently modelled alongside wind speed in a complex-valued time series as in [6], among others.…”

Section: Introductionmentioning

confidence: 99%