Numerical Weather Prediction Correction Strategy for Short-Term Wind Power Forecasting Based on Bidirectional Gated Recurrent Unit and XGBoost

Liu, Yu; Tang, Fei; Gao, Xin; Zhang, Tongyan; Qi, J.; Xie, Jiarui; Li, Xinang; Guo, Yuhan

doi:10.3389/fenrg.2021.836144

Cited by 12 publications

(11 citation statements)

References 47 publications

(35 reference statements)

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“…. , N ), we design objective function (12) based on envelope entropy (13) as the fitness function. The reader can refer to [18] for the details of the parameter range setting.…”

Section: Vmd Optimized By Genetic Algorithmmentioning

confidence: 99%

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A Hybrid Short‐Term Wind Power Forecasting Model Considering Significant Data Loss

Goh,

Ding,

Dai

et al. 2023

IEEJ Transactions Elec Engng

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Accurate wind power forecasting (WPF) is pivotal for the power system dominated by high penetration of renewable energy. Most forecasting techniques require sufficient data samples as a premise for achieving accurate prediction. Due to equipment faults during data collection, complete data is not always available, resulting that the forecasting accuracy is greatly diminished. To address this issue, this paper proposes a novel two‐stage hybrid forecasting approach including data restoration stage and forecasting stage. For the data restoration stage, the bidirectional long short‐term memory (Bi‐LSTM) is integrated into generative adversarial network (GAN) to recover the missing data with consideration of the complex time dynamics and correlations among heterogeneous data. To improve the prediction accuracy, the complete generated wind power sequence is decomposed into multiple time sequences with low volatility based on the enhanced variational mode decomposition (VMD). For the forecasting stage, a hybrid forecasting algorithm that combines convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) with an improved attention mechanism is proposed, strengthening the forecasting performance by assigning optimal weights to key features. The proposed hybrid forecasting method outperforms traditional methods based on real wind farm data with different shares of data loss from Guangxi province in China. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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“…. , N ), we design objective function (12) based on envelope entropy (13) as the fitness function. The reader can refer to [18] for the details of the parameter range setting.…”

Section: Vmd Optimized By Genetic Algorithmmentioning

confidence: 99%

“…However, these models rely on a single predictive algorithm, failing to account for complex relationships that influence wind power more. Compared with a single prediction model, the hybrid model adopts feature extraction and signal decomposition techniques to capture internal connections and hidden features of wind farm information [13].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Short‐Term Wind Power Forecasting Model Considering Significant Data Loss

Goh,

Ding,

Dai

et al. 2023

IEEJ Transactions Elec Engng

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“…Where, Δt i is forecasting based on the hourly period, now the forecasting of GHI for the clear sky is shown as Eq. 16.…”

Section: Data Collection and Training Approachmentioning

confidence: 99%

Intelligent based hybrid renewable energy resources forecasting and real time power demand management system for resilient energy systems

2022

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The rapid growth of hybrid renewable Distributed Energy Resources (DERs) generation possess various challenges with inaccurate forecast models in stochastic power systems. The prime objective of this research is to maximum utilization of scheduled power from hybrid renewable based DERs to maintain the load-demand profile with reduce distributed grid burden. The proposed mixed input-based cascaded artificial neural network [Formula: see text] is realized for the prediction of a short-term based hourly solar irradiance and wind speed. The testing approach is performed through a historical hourly dataset of the proposed site. Further, the normalized data sets are divided into hourly-based samples for validating the load demand power with respect to the variation in metrological data. In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) model is simulated for short-term power demand prediction. This adaptive methodology is an effective approach for load-demand management which is based on cross-entropy. It also confirmed that during testing, the forecasting mean error and cross-entropy are less than 5% under a specific time slap of an individual day. The regression analysis is performed through the time series fitting simulation tool at different time horizons. The performance evaluation of the designed model is compared with the multi-layer perceptron model. Simulation results display the proposed mixed input-based cascaded system has enhanced accuracy and optimal performance than the multi-output correlated perceptron model.

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“…Because of the hierarchical and distributed feature representations, deep learning (DL) network possesses strong capability to predict high-frequency sequence and robustness to parameters (Hu and Chen, 2018). Deep learning methods including long short-term memory (LSTM) (Shahid et al, 2021), convolutional neural network (CNN) (Yu et al, 2020), deep belief network (DBN) (Wang et al, 2016a), and gated recurrent unit (GRU) (Li et al, 2022) have drawn much attention recently. Liu et al (2018b) proposed a deep learning framework based on LSTM neural network for one-step forecasting of wind speed.…”

Section: Introductionmentioning

confidence: 99%

Wind speed prediction model using ensemble empirical mode decomposition, least squares support vector machine and long short-term memory

Li²,

Xu³

2023

Front. Energy Res.

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Due to the randomness and intermittency of wind, accurate and reliable wind speed prediction is of great importance to the safe and stable operation of power grid. In this paper, a novel hybrid wind speed forecasting model based on EEMD (Ensemble Empirical Mode Decomposition), LSSVM (Least Squares Support Vector Machine), and LSTM (Long Short-Term Memory) is proposed, aiming at enhancing the forecasting accuracy of wind speed. The original data series is firstly processed by EEMD and SE into a series of components with different frequencies. Subsequently, a combined mechanism composed of LSSVM and LSTM is presented to train and predict the high-frequency and low-frequency sequences, respectively. Finally, the predicted values of all the data sequences are superimposed to obtain the ultimate wind speed forecasting results. In order to respectively illustrate the superiority of data feature processing and combined prediction mechanism in the proposed model, two experiments are performed on the two wind speed datasets. In accordance with the four performance metrics of the forecasting results, the EEMD-LSTM-LSSVM model obtains a higher accuracy in wind speed prediction task.

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Numerical Weather Prediction Correction Strategy for Short-Term Wind Power Forecasting Based on Bidirectional Gated Recurrent Unit and XGBoost

Cited by 12 publications

References 47 publications

A Hybrid Short‐Term Wind Power Forecasting Model Considering Significant Data Loss

A Hybrid Short‐Term Wind Power Forecasting Model Considering Significant Data Loss

Intelligent based hybrid renewable energy resources forecasting and real time power demand management system for resilient energy systems

Wind speed prediction model using ensemble empirical mode decomposition, least squares support vector machine and long short-term memory

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