A boundary layer scaling technique for estimating near-surface wind energy using numerical weather prediction and wind map data

Allen, David J.; Tomlin, Alison S.; Bale, Catherine S.E.; Skea, Alasdair; Vosper, Simon; Gallani, M.L.

doi:10.1016/j.apenergy.2017.09.029

Cited by 87 publications

(18 citation statements)

References 30 publications

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“…They are also less capable of short-range WS prediction at the local or station level. Several physical models have recently been developed, for instance, Allen et al, (2017) presented a boundary layer scaling model to predict long-term average near-surface WS [16], a physical-based model for WS prediction in complex terrain was developed by [17].…”

Section: Introductionmentioning

confidence: 99%

A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction

et al. 2020

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Accurate wind speed (WS) modelling is crucial for optimal utilization of wind energy. Numerical Weather Prediction (NWP) techniques, generally used for WS modelling are not only less cost-effective but also poor in predicting in shorter time horizon. Novel WS prediction models based on the multivariate empirical mode decomposition (MEMD), random forest (RF) and Kernel Ridge Regression (KRR) were constructed in this paper better accuracy in WS prediction. Particle swarm optimization algorithm (PSO) was employed to optimize the parameters of the hybridized MEMD model with RF (MEMD-PSO-RF) and KRR (MEMD-PSO-KRR) models. Obtained results were compared to those of the standalone RF and KRR models. The proposed methodology is applied for monthly WS prediction at meteorological stations of Iraq, Baghdad (Station 1 ) and Mosul (Station 2 ) for the period 1977-2013. Results showed higher accuracy of MEMD-PSO-RF model in predicting WS at both stations with a correlation coefficient (r) of 0.972 and r = 0.971 during testing phase at Station 1 and Station 2 , respectively. The MEMD-PSO-KRR was found as the second most accurate model followed by Standalone RF and KRR, but all showed a competitive performance to the MEMD-PSO-RF model. The outcomes of this work indicated that the MEMD-PSO-RF model has a remarkable performance in predicting WS and can be considered for practical applications.INDEX TERMS Wind speed prediction, multivariate empirical mode decomposition, random forest, Kernel Ridge Regression, Iraq region.

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Section: Introductionmentioning

confidence: 99%

A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction

et al. 2020

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“…The computational fluid dynamics (CFD) and numerical weather prediction (NWP) are the most critical technologies in the physical models. The research work presented in [10] proposes a boundary layer scaling (BLS) technique based on the NWP model for long-term wind speed forecasting. The statistical forecasting models mainly use the wind time series data and try to find the mathematical relationships between the spatialtemporal samples or historical data, which yields accurate estimation results in the short-term prediction tasks.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning-Based Model for Wind Speed Forecasting Based on DWPT and Bidirectional LSTM Network

2020

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Accurate wind speed forecasting is essential for the reliability and security of the power system, and optimal operation and management of wind integrated smart grids. However, it is still a challenging task due to the highly uncertain and volatile nature of wind speed. Accordingly, in this work, a novel deep learning-based model integrating the discrete wavelet packet transform (DWPT) and bidirectional long short-term memory (BLSTM) is developed to precisely capture deep temporal features and learn the time-varying relationship of wind speed time series. In the proposed method, by applying the DWPT, both approximations and details parts are decomposed by passing through the filters to choose the frequency band related to the features of the original signal more adaptively. The BLSTM networks are incorporated to deal with the uncertainties more effectively as they have bidirectional memory capability (feedforward and feedback loops) to investigate both previous and future hidden layers data. To simultaneously improve the forecasting performance and decrease the learning complexity, the reconstructed state space of historical wind data is employed to reflect the evolution laws of wind speed. Two case studies using real-world wind speed datasets gathered from Flatirons campus (M2) of National Renewable Energy Laboratory (NREL) located in Colorado, USA and weather station of Edmonton, Canada are implemented to demonstrate the effectiveness and superiority of the proposed hybrid method compared to the shallow architectures and state-of-the-art deep learning models in the recent literature.INDEX TERMS Bidirectional LSTM, deep learning, time series, Takens' embedding theorem, wavelet packet decomposition, wind speed prediction.

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“…These can be classified as either physical or statistical models [9]. Physical models employ physical and mathematical formulas to estimate wind speed information [10]; however, these models can be complex and time-consuming to employ. Statistical models, including the time series models and machine learning-based techniques, have the potential to estimate wind speed using historical data and other related parameters [2].…”

Section: Introductionmentioning

confidence: 99%

Estimating the short-term and long-term wind speeds: implementing hybrid models through coupling machine learning and linear time series models

Mehdizadeh

Sales²,

Safari

2020

SN Appl. Sci.

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Wind speed data are of particular importance in the design and management of wind power projects. In the current study, three types of linear time series models including autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) were employed to estimate short-term (i.e., daily) and long-term (i.e., monthly) wind speeds. The required data were gathered, respectively, from the Tabriz and Zahedan stations in the northwest and southeast of Iran. The MA models outperformed the AR and ARMA on the both daily and monthly scales. Daily and monthly wind speed values, as a function of lagged wind speed data, were then estimated using two machine learning models of random forests (RF) and multivariate adaptive regression splines (MARS). It was found that the RF and MARS provided similar results; however, RF performed slightly better than the MARS. Finally, the stand-alone time series and machine learning models were coupled to improve the accuracy of the wind speed estimation. Accordingly, the hybrid RF-AR, RF-MA, RF-ARMA, MARS-AR, MARS-MA, and MARS-ARMA models were implemented. It was concluded that, the hybrid models outperformed the stand-alone RF and MARS for both short-and long-term wind speed estimations where, the RF-AR and MARS-AR hybrid models provided the best performances. The hybrid models tested in the present study could be effective alternatives to the stand-alone machine learning-based RF and MARS models for the estimation of wind speed time series.

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A boundary layer scaling technique for estimating near-surface wind energy using numerical weather prediction and wind map data

Cited by 87 publications

References 30 publications

A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction

A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction

Hybrid Deep Learning-Based Model for Wind Speed Forecasting Based on DWPT and Bidirectional LSTM Network

Estimating the short-term and long-term wind speeds: implementing hybrid models through coupling machine learning and linear time series models

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