Wind speed forecast model for wind farm based on a hybrid machine learning algorithm

Haque, Ashraf Ul; Mandal, Paras; Meng, Julian; Negnevitsky, Michael

doi:10.1080/14786451.2013.826224

Cited by 36 publications

(10 citation statements)

References 30 publications

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“…The results proved that the built models were satisfactory. From the upper reviewed literature, it can be seen that: (a) the hybrid forecasting methods always can have better performance than the single ones in the wind speed predictions; (b) in the proposed hybrid methods, the signal processing algorithms (e.g., wavelet decomposition [3,6], empirical mode decomposition [4], etc) are adopted to decrease the instability of the raw wind speed data to decrease the high-precision forecasting difficulty and the intelligent optimizing algorithms (e.g., genetic algorithm [6], particle swarm optimization [9], coral reefs optimization [14], etc) are utilized to promote the computational capacity of the built forecasting models for the accurate results; and (c) the neural networks have been generally applied in the wind speed predictions.…”

Section: Introductionmentioning

confidence: 98%

“…The performance of the proposed method was validated by using daily wind speed data from Iraq to Malaysia. Haque et al [3] presented a new hybrid wind speed forecasting method by based on the WT (Wavelet Transform) and the FART (Fuzzy Adaptive Resonance Theory). The WT was exploited to decompose the raw wind speed data, the FART was built to do the real forecasting computation.…”

Section: Introductionmentioning

confidence: 99%

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New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks

et al. 2015

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a b s t r a c tWind speed high-precision prediction is one of the most important technical aspects to protect the safety of wind power utilization. In this study, two new hybrid methods [FEEMD-MEA-MLP/FEEMD-GA-MLP] are proposed for the wind speed accurate multi-step predictions by combining FEEMD (Fast Ensemble Empirical Mode Decomposition), MEA (Mind Evolutionary Algorithm), GA (Genetic Algorithm) and MLP (Multi Layer Perceptron) neural networks. In these two hybrid methods, the FEEMD algorithm is adopted to decompose the original wind speed series into a number of sub-layers and the MLP neural networks optimized by the MEA algorithm and the GA algorithm are built to predict the decomposed wind speed sub-layers, respectively. The innovation of the study is to investigate the promoted percentages of the MLP neural networks by the FEEMD decomposition and the MEA/GA optimization, respectively. The involved forecasting models in the performance comparison in the study include the hybrid FEEMD-MEA-MLP, the hybrid FEEMD -GA-MLP, the hybrid FEEMD-MLP, the hybrid MEA-MLP, the hybrid GA-MLP and the single MLP. Two experimental results show that: (a) among all the involved methods, the hybrid FEEMD-MEA-MLP model has the best forecasting performance; (b) the FEEMD algorithm promotes the performance of the MLP neural networks significantly while the MEA/GA algorithms do not improve the performance of the MLP neural networks significantly; and (c) the hybrid FEEMD-MEA-MLP method and the hybrid FEEMD-GA-MLP method are both effective in the wind speed high-precision predictions.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks

et al. 2015

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“…Recent work [27,28] has shown the ability for statistical models to efficiently maximize wind farm power production in a control oriented approach with a physics-driven wake model. Further, neural networks have been used extensively to improve wind farm power forecasting (see, e.g., [29][30][31]). Due to the known deficiencies of low-order wake models in capturing the complex physics of utility-scale wind farms and given the recent successes of statistical approaches to model wind farm power production, we propose a statistically driven wake model.…”

Section: Introductionmentioning

confidence: 99%

Wind Farm Modeling with Interpretable Physics-Informed Machine Learning

Howland

Dabiri

2019

Energies

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Turbulent wakes trailing utility-scale wind turbines reduce the power production and efficiency of downstream turbines. Thorough understanding and modeling of these wakes is required to optimally design wind farms as well as control and predict their power production. While low-order, physics-based wake models are useful for qualitative physical understanding, they generally are unable to accurately predict the power production of utility-scale wind farms due to a large number of simplifying assumptions and neglected physics. In this study, we propose a suite of physics-informed statistical models to accurately predict the power production of arbitrary wind farm layouts. These models are trained and tested using five years of historical one-minute averaged operational data from the Summerview wind farm in Alberta, Canada. The trained models reduce the prediction error compared both to a physics-based wake model and a standard two-layer neural network. The trained parameters of the statistical models are visualized and interpreted in the context of the flow physics of turbulent wind turbine wakes.

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“…In general, these models outperform the time series models in short term predictions, but their performance edge is not maintained across all locations universally (Soman et al, 2010). Recently researchers have also begun to use hybrid models, which combine different ap-110 proaches for better forecasting results, such as mixing physical and statistical models or short-term and medium-term models (Soman et al, 2010;Liu et al, 2014;Haque et al, 2013). The central idea of physical approach is to incorporate the physical considerations of local topography into the 115 numerical weather prediction scheme by modelling the local wind profile possibly considering the atmospheric stability.…”

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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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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Wind speed forecast model for wind farm based on a hybrid machine learning algorithm

Cited by 36 publications

References 30 publications

New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks

New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks

Wind Farm Modeling with Interpretable Physics-Informed Machine Learning

Deterministic prediction of surface wind speed variations

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