Short-term wind speed prediction using Extended Kalman filter and machine learning

Hur, Sung-ho

doi:10.1016/j.egyr.2020.12.020

Cited by 64 publications

(21 citation statements)

References 10 publications

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“…The ability to handle enormous datasets and nonlinear correlation with more flexibility and resilience has resulted in the remarkable development of renewable energy applications. Meanwhile, existing methods also can be divided into nonhybrid models and hybrid models [24][25][26][27][28] based on current research. Bootstrap is another method used in wind speed forecasting (WSF), in which the dataset is small and is not divided into the training set and the testing set.…”

Section: Forecasting Of Wind and Wave Energymentioning

confidence: 99%

Review on Deep Learning Research and Applications in Wind and Wave Energy

2022

Energies

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Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification.

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Section: Forecasting Of Wind and Wave Energymentioning

confidence: 99%

Review on Deep Learning Research and Applications in Wind and Wave Energy

2022

Energies

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“…As an illustration, Lee and Johnson [27] showed that Kalman filtering improves the accuracy of machine learning models such as GP regression and Ullah et al [28] proposed a hybrid method combining an ANN and a Kalman filter technique to improve the performance of a prediction algorithm under dynamic conditions. In addition, Hur [29] recently presented a wind speed prediction scheme that comprises two stages: estimation by an extended Kalman filter (EKF) and prediction by a neural network. While the aforementioned literature survey has demonstrated the capability for wind forecasting, the proposed methods may not be applicable for the MERRA-2 wind dataset, which is one of the most commonly used wind datasets in the aviation industry, as the methods were not trained and developed using the MERRA-2 dataset.…”

Section: Related Workmentioning

confidence: 99%

Unscented Kalman Filter-Aided Long Short-Term Memory Approach for Wind Nowcasting

Kim

Lee

2021

Aerospace

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Obtaining reliable wind information is critical for efficiently managing air traffic and airport operations. Wind forecasting has been considered one of the most challenging tasks in the aviation industry. Recently, with the advent of artificial intelligence, many machine learning techniques have been widely used to address a variety of complex phenomena in wind predictions. In this paper, we propose a hybrid framework that combines a machine learning model with Kalman filtering for a wind nowcasting problem in the aviation industry. More specifically, this study has three objectives as follows: (1) compare the performance of the machine learning models (i.e., Gaussian process, multi-layer perceptron, and long short-term memory (LSTM) network) to identify the most appropriate model for wind predictions, (2) combine the machine learning model selected in step (1) with an unscented Kalman filter (UKF) to improve the fidelity of the model, and (3) perform Monte Carlo simulations to quantify uncertainties arising from the modeling process. Results show that short-term time-series wind datasets are best predicted by the LSTM network compared to the other machine learning models and the UKF-aided LSTM (UKF-LSTM) approach outperforms the LSTM network only, especially when long-term wind forecasting needs to be considered.

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“…Hur et al 13 used an unscented Kalman filter (UKF) for integration with SVR, to establish the SVR‐UKF prediction model, which improved the prediction accuracy of the SVR model. To predict the mean monthly wind speed in the Hexi Corridor, Tian et al 14 proposed a hybrid local mean decomposition (LMD) and combined kernel function least squares support vector machine (LSSVM) model. Said this model exhibited a more powerful forecasting capacity for short‐term wind speed prediction at wind farms.…”

Section: Introductionmentioning

confidence: 99%

An ultra‐short‐term wind speed prediction model using LSTM based on modified tuna swarm optimization and successive variational mode decomposition

Tuerxun

Guo

et al. 2022

Energy Science & Engineering

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Accurate ultra-short-term wind speed prediction is extremely important for the power control of wind farms, the safe dispatch of power systems, and the stable operation of power grids. At present, most wind farms mainly rely on supervisory control and data acquisition systems to obtain operation and maintenance data which includes operating characteristics of wind turbines. In the ultra-short-term wind speed prediction, a long short-term memory network is one of the commonly used deep learning methods. To address the problem that improper selection of long short-term memory network's hyperparameters may affect the prediction results, In the present study, a hybrid prediction model based on the long short-term memory and the modified tuna swarm optimization algorithm was established, and was used to predict after the wind speed sample data had been decomposed by successive variational mode decomposition method. The experimental results reveal that the proposed model effectively improved the accuracy of wind speed prediction for wind farms compared with the support vector regression, deep belief networks, and long short-term memory models optimized by particle swarm optimization algorithm.

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Short-term wind speed prediction using Extended Kalman filter and machine learning

Cited by 64 publications

References 10 publications

Review on Deep Learning Research and Applications in Wind and Wave Energy

Review on Deep Learning Research and Applications in Wind and Wave Energy

Unscented Kalman Filter-Aided Long Short-Term Memory Approach for Wind Nowcasting

An ultra‐short‐term wind speed prediction model using LSTM based on modified tuna swarm optimization and successive variational mode decomposition

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