Wind Power Prediction in View of Ramping Events Based on Classified Spatiotemporal Network

Bing-bing, Xia; Huang, Qiyue; Wang, Hao; Ying, Liheng

doi:10.3389/fenrg.2021.754274

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Hence improved RNN and CNN models, such as long short-term memory (LSTM) (Zhang et al, 2019a;Zhang et al, 2019b;Wu et al, 2019), GRU (Ding et al, 2019;, and temporal convolutional network (TCN) (Gan et al, 2021;He et al, 2022) have been widely used in wind power prediction. In recent years, the generative adversarial network (GAN) has attracted a lot of attention (Yuan et al, 2021;Zhou et al, 2021;Xia et al, 2022). Its generative model maps noise variables to multi-layer perceptron networks to make the generated data as close as possible to the distribution of training samples.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, environmental problems have become more serious, so the exploration of clean energy will become an inevitable trend in the future development . The regional integrated energy system (RIES) breaks the barrier between energy planning and operation, and its internal multi-energy coupling equipment can realize energy gradient utilization Wang B. et al, 2022), which plays a huge part in realizing the goal of promoting economic and environmental benefits.…”

Section: Introductionmentioning

confidence: 99%

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Key technologies for hybrid energy system planning and operation

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Key technologies for hybrid energy system planning and operation

2024

Frontiers Research Topics

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“…Hence improved RNN and CNN models, such as long short-term memory (LSTM) (Zhang et al, 2019a;Zhang et al, 2019b;Wu et al, 2019), GRU (Ding et al, 2019;Chen et al, 2022), and temporal convolutional network (TCN) (Gan et al, 2021;He et al, 2022) have been widely used in wind power prediction. In recent years, the generative adversarial network (GAN) has attracted a lot of attention (Yuan et al, 2021;Zhou et al, 2021;Xia et al, 2022). Its generative model maps noise variables to multi-layer perceptron networks to make the generated data as close as possible to the distribution of training samples.…”

Section: Introductionmentioning

confidence: 99%

An ensemble model for short-term wind power prediction based on EEMD-GRU-MC

Wang,

Su,

et al. 2024

Front. Energy Res.

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As a kind of clean and renewable energy, wind power is of great significance for alleviating energy crisis and environmental pollution. However, the strong randomness and large volatility of wind power bring great challenges to the dispatching and safe operation of the power grid. Hence, accurate and reliable short-term prediction of wind power is crucial for the power grid dispatching department arranging reasonable day-ahead generation schedules. Targeting the problem of low model prediction accuracy caused by the strong intermittency and large volatility of wind power, this paper develops a novel ensemble model for short-term wind power prediction which integrates the ensemble empirical mode decomposition (EEMD) algorithm, the gated recurrent unit (GRU) model and the Markov chain (MC) technique. Firstly, the EEMD algorithm is used to decompose the historical wind power sequence into a group of relatively stationary subsequences to reduce the influence of random fluctuation components and noise. Then, the GRU model is employed to predict each subsequence, and the predicted values of each subsequence are aggregated to get the preliminary prediction results. Finally, to further enhance the prediction accuracy, the MC is used to modified the prediction results. A large number of numerical examples indicates that the proposed EEMD-GRU-MC model outperforms the six benchmark models (i.e., LSTM, GRU, EMD-LSTM, EMD-GRU, EEMD-LSTM and EEMD-GRU) in terms of multiple evaluation indicators. Taking the spring dataset of the ZMS wind farm, for example, the MAE, RMSE and MAPE of the EEMD-GRU-MC model is 1.37 MW, 1.97 MW, and from 1.76%, respectively. Moreover, the mean prediction error of the developed model in all scenarios is less than or close to 2%. After 30 iterations, the proposed model uses an average of about 35 min to accurately predict the wind power of the next day, proving its high computation efficiency. It can be concluded that the ensemble model based on EEMD-GRU-MC is a promising prospect for short-term wind power prediction.

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Long-term Clustered Unit Commitment Considering Lack-of-Inertia and Lack-of-Flexiramp Requirements

Yuan,

Chen,

et al. 2024

2024 IEEE/IAS 60th Industrial and Commercial Power Systems Technical Conference (I&CPS)

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Wind Power Prediction in View of Ramping Events Based on Classified Spatiotemporal Network

Cited by 5 publications

References 26 publications

Key technologies for hybrid energy system planning and operation

Key technologies for hybrid energy system planning and operation

An ensemble model for short-term wind power prediction based on EEMD-GRU-MC

Long-term Clustered Unit Commitment Considering Lack-of-Inertia and Lack-of-Flexiramp Requirements

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