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Artículos de revistas sobre el tema "Wind power prediction"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 26 de julio de 2025

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1

Wu, Xinhua, Nan Chen, Qianyun Du, Shuangshuang Mao, and Xiaoming Ju. "Short-term wind power prediction model based on ARMA-GRU-QPSO and error correction." Journal of Physics: Conference Series 2427, no. 1 (2023): 012028. http://dx.doi.org/10.1088/1742-6596/2427/1/012028.

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Abstract Power system dispatch benefits from accurate wind power predictions. To increase the prediction precision for wind power, this paper proposes a combined model for predicting short-term wind power based on the autoregressive moving average-gated recurrent unit (ARMA-GRU). Firstly, we build the ARMA model and GRU model respectively to predict wind power. Then we optimize the combined model’s weights by quantum particle swarm algorithm (QPSO). Finally, we build an error correction model for the prediction errors to acquire the final results for the wind power predictions. Our experimenta

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2

Bao-Wei Zhang, Bao-Wei Zhang, Hong-Bo Cui Bao-Wei Zhang, and Jiu-Xiang Song Hong-Bo Cui. "Wind Power Prediction Based on Difference Method." 電腦學刊 33, no. 4 (2022): 195–204. http://dx.doi.org/10.53106/199115992022083304016.

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<p> The renewable wind power sources are difficult to be predicted in view of the fluctuating factors such as wind bearing, pressure, wind speed, and humidity of the surrounding atmosphere. An attempt is made in this paper to propose a difference method to build a neural network and a long short term memory (LSTM) model for wind power prediction. First, the correlation of each data is analyzed and then per-forming difference processing on the original data to solve the problem that the original data cannot be analyzed by probability distribution. The prediction is made by building the ne

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3

Hu, Hongda, Zhiyong Hu, Kaiwen Zhong, et al. "Long-term offshore wind power prediction using spatiotemporal kriging: A case study in China’s Guangdong Province." Energy Exploration & Exploitation 38, no. 3 (2019): 703–22. http://dx.doi.org/10.1177/0144598719889368.

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The predicted wind power in coastal waters is an important factor when planning and developing offshore wind farms. The stochastic wind field challenges the accuracy of these predictions. Using single-point wind measurements, most previous studies have focused on the prediction of short-term wind power, ranging from minutes to several days. Longer-term wind power predictions would better support decision-making related to offshore wind power balance management and reserve capacities. In addition, larger-scale wind power predictions, based on gridded wind field data, would provide a more compre

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4

Guo, Wei, Li Xu, Danyang Zhao, Dianqiang Zhou, Tian Wang, and Xujing Tang. "A Wind Power Combination Forecasting Method Based on GASF Image Representation and UniFormer." Journal of Marine Science and Engineering 12, no. 7 (2024): 1173. http://dx.doi.org/10.3390/jmse12071173.

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In the field of wind power prediction, traditional methods typically rely on one-dimensional time-series data for feature extraction and prediction. In this study, we propose an innovative short-term wind power forecasting approach using a “visual” 2D image prediction method that effectively utilizes spatial pattern information in time-series data by combining wind power series and related environmental features into a 2D GASF image. Firstly, the wind power data are decomposed using the ICEEMDAN algorithm optimized by the BWO (Beluga Whale Optimization) algorithm, extracting the submodal IMF (

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5

Liu, Yi, Jun He, Yu Wang, Zong Liu, Lixun He, and Yanyang Wang. "Short-Term Wind Power Prediction Based on CEEMDAN-SE and Bidirectional LSTM Neural Network with Markov Chain." Energies 16, no. 14 (2023): 5476. http://dx.doi.org/10.3390/en16145476.

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Accurate wind power data prediction is crucial to increase wind energy usage since wind power data are characterized by uncertainty and randomness, which present significant obstacles to the scheduling of power grids. This paper proposes a hybrid model for wind power prediction based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE), bidirectional long short-term memory network (BiLSTM), and Markov chain (MC). First, CEEMDAN is used to decompose the wind power series into a series of subsequences at various frequencies, and then SE is emp

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6

Liu, Yuanlong, Yuanbiao Zhang, and Ziyue Chen. "Wind Power Prediction Investigation." Research Journal of Applied Sciences, Engineering and Technology 5, no. 5 (2013): 1762–68. http://dx.doi.org/10.19026/rjaset.5.4935.

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7

Liu, Hai Ke, Jiang Xia Feng, Shen Quan Yang, and Tao Jia. "Wind Power Prediction Model Based on ARMA and Improved BP-ANN." Advanced Materials Research 1008-1009 (August 2014): 183–87. http://dx.doi.org/10.4028/www.scientific.net/amr.1008-1009.183.

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In order to improve the prediction accuracy of wind power, this research is based on time series and improved BP-ANN algorithm. The basic idea is described as follows: wind speed forecasting model is established by using time series method; wind speed-wind power model is built by utilising improved BP-ANN algorithm; wind speed data from time series forecasting is used as input of neural network model, and the prediction results for wind power are obtained. In order to analyse the availability of wind power prediction model, the mean absolute error and correlation coefficient are compared to an

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8

Li, Wei, Hong Tu Zhang, and Ting Ting An. "Study on Short-Term Wind Power Prediction Model Based on ARMA Theory." Applied Mechanics and Materials 448-453 (October 2013): 1875–78. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.1875.

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At present, the difficulty of wind power integration has resulted in a large number of wind curtailment phenomena and wasted a lot of renewable energy. Due to the significant instability, anti-peak-regulation and intermittency of wind power, wind power integration needs an accurate prediction technique to be a basis. ARMA model has the advantage of high prediction accuracy in predicting short-term wind power. This paper puts forward the method for short-term wind power prediction using ARMA model and carries out empirical analysis using the data from a wind farm of Jilin province, which shows

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9

Solovev, Bogdan, and Giorgi Gamisonia. "WIND POWER PREDICTION METHODS FOR SHELF WIND POWER PLANTS." Electrical and data processing facilities and systems 18, no. 3-4 (2022): 108–20. http://dx.doi.org/10.17122/1999-5458-2022-18-3-4-108-120.

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Relevance Wind energy forecasting is an opportunity to evaluate the production possibilities of a wind farm in the short term. Production often refers to the available capacity of the wind farm in question. For example, to date, the installed wind power in Russia has reached 20 GW. Direct transmission operators use existing tools to forecast wind production up to 48 hours. Forecasting tools help optimize power system management. This article discusses the abundance of relevant forecasting methods in the field of wind energy, evaluates their effectiveness and value for the most effective contro

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10

Ramadevi, Bhukya, Venkata Ramana Kasi, and Kishore Bingi. "Hybrid LSTM-Based Fractional-Order Neural Network for Jeju Island’s Wind Farm Power Forecasting." Fractal and Fractional 8, no. 3 (2024): 149. http://dx.doi.org/10.3390/fractalfract8030149.

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Efficient integration of wind energy requires accurate wind power forecasting. This prediction is critical in optimising grid operation, energy trading, and effectively harnessing renewable resources. However, the wind’s complex and variable nature poses considerable challenges to achieving accurate forecasts. In this context, the accuracy of wind parameter forecasts, including wind speed and direction, is essential to enhancing the precision of wind power predictions. The presence of missing data in these parameters further complicates the forecasting process. These missing values could resul

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11

Zhao, Shuling, and Sishuo Zhao. "Wind Power Interval Prediction via an Integrated Variational Empirical Decomposition Deep Learning Model." Sustainability 15, no. 7 (2023): 6114. http://dx.doi.org/10.3390/su15076114.

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As global demand for renewable energy increases, wind energy has become an important source of clean energy. However, due to the instability and unpredictability of wind energy, predicting wind power becomes one of the keys to resolving the instability of wind power. The current point prediction model of wind power output has limitations and randomness in processing information. In order to improve the prediction accuracy and efficiency of wind power, a multi-step interval prediction method (VMD-TCN) is proposed in this article, which uses variational modal decomposition and an improved tempor

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12

Liu, Renfeng, Yinbo Song, Chen Yuan, Desheng Wang, Peihua Xu, and Yaqin Li. "GAN-Based Abrupt Weather Data Augmentation for Wind Turbine Power Day-Ahead Predictions." Energies 16, no. 21 (2023): 7250. http://dx.doi.org/10.3390/en16217250.

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This study introduces a data augmentation technique based on generative adversarial networks (GANs) to improve the accuracy of day-ahead wind power predictions. To address the peculiarities of abrupt weather data, we propose a novel method for detecting mutation rates (MR) and local mutation rates (LMR). By analyzing historical data, we curated datasets that met specific mutation rate criteria. These transformed wind speed datasets were used as training instances, and using GAN-based methodologies, we generated a series of augmented training sets. The enriched dataset was then used to train th

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13

Drisya, G. V., D. C. Kiplangat, K. Asokan, and K. Satheesh Kumar. "Deterministic prediction of surface wind speed variations." Annales Geophysicae 32, no. 11 (2014): 1415–25. http://dx.doi.org/10.5194/angeo-32-1415-2014.

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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

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14

Lange, Matthias. "On the Uncertainty of Wind Power Predictions—Analysis of the Forecast Accuracy and Statistical Distribution of Errors." Journal of Solar Energy Engineering 127, no. 2 (2005): 177–84. http://dx.doi.org/10.1115/1.1862266.

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In this work the uncertainty of wind power predictions is investigated with a special focus on the important role of the nonlinear power curve. Based on numerical predictions and measured data from six onshore wind farms the overall prediction accuracy is assessed and it is shown that due to the power curve the relative forecast error increases by a factor of 1.8–2.6 compared to the wind speed forecast. This factor can be considered as an effective nonlinearity factor. A decomposition of the commonly known root mean square error is beneficially used to distinguish different error sources relat

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15

Yang, Jiacheng, Shiyuan Wen, and Jia Lin. "A new power prediction model for wind power generation." Journal of Physics: Conference Series 2785, no. 1 (2024): 012067. http://dx.doi.org/10.1088/1742-6596/2785/1/012067.

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Abstract A wind power generation forecast model based on WOA-SVM is presented to exploit effective information in different data sets completely. This model addresses the difficulties associated with parameter selection, low prediction accuracy, and susceptibility to local optima in short-term wind energy prediction. The model systematically investigates the relationship between various wind parameters (mean wind speed, maximum wind speed, minimum wind speed, mean wind direction, and mean hull position) and wind power. It then evaluates the model’s performance using mean absolute error and coe

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16

Teng, Yun, Zhi Yao An, Xin Yu, Zhen Hao Wang, and Yong Gang Zhang. "Study of Wind Farm Power Output Predicting Model Based on Nonlinear Time Series." Applied Mechanics and Materials 670-671 (October 2014): 1526–29. http://dx.doi.org/10.4028/www.scientific.net/amm.670-671.1526.

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To solve the problem of the variancy of the wind power when wind farm connect with the power grid, a wind power predicting model of wind farm based on double ANNs is proposed in the paper. Wind velocity and wind direction on wind farm are the key of wind power predicting, and other circumstance conditions such as temperature, humidity, atmospheric pressure, are also great influence on it. The observed values of these five circumstance conditions can be treated as a nonlinear time series and be analyzed by the nonlinear time series ANNs model. The wind power predicting model consists of double

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17

Tsai, Wen-Chang, Chih-Ming Hong, Chia-Sheng Tu, Whei-Min Lin, and Chiung-Hsing Chen. "A Review of Modern Wind Power Generation Forecasting Technologies." Sustainability 15, no. 14 (2023): 10757. http://dx.doi.org/10.3390/su151410757.

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The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction

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18

Zhang, Yujie, Lei Zhang, Duo Sun, Kai Jin, and Yu Gu. "Short-Term Wind Power Forecasting Based on VMD and a Hybrid SSA-TCN-BiGRU Network." Applied Sciences 13, no. 17 (2023): 9888. http://dx.doi.org/10.3390/app13179888.

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Wind power generation is a renewable energy source, and its power output is influenced by multiple factors such as wind speed, direction, meteorological conditions, and the characteristics of wind turbines. Therefore, accurately predicting wind power is crucial for the grid operation and maintenance management of wind power plants. This paper proposes a hybrid model to improve the accuracy of wind power prediction. Accurate wind power forecasting is critical for the safe operation of power systems. To improve the accuracy of wind power prediction, this paper proposes a hybrid model incorporati

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19

Li, De Xin, Xiang Yu Lv, and Zhi Hui Song. "Short-Term Prediction of Wind Power Output Based on Markov Chain." Applied Mechanics and Materials 448-453 (October 2013): 1789–95. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.1789.

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Wind power short-term predicting technology has a great significance in process of wind power decision-making. Recent years, the technology had been studied extensively in industry. Markov chain model has strong adaptability, forecast accuracy higher and other else advantages, which is suitable for wind power short-term prediction. This paper have set up one step Markov prediction model and based on which predicting short-term wind power output, and taken the historical power data of an actual wind farm in Jilin Province as an example to simulate and analyze. The paper also have proposed and u

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20

Yu, Feng Ming, Xi Cang Li, Jin Hua Song, Chun Xiang Gao, and Chun Long Jiang. "Research on Wind Power Prediction by Combining Mesoscale Numerical Model with Neural Network Model." Advanced Materials Research 512-515 (May 2012): 771–77. http://dx.doi.org/10.4028/www.scientific.net/amr.512-515.771.

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Effective wind power prediction on wind farm can not only guarantee safe operation of wind farm, but also increase wind power storage and utilization efficiency. This research combines mesoscale numerical weather prediction model with BP neural network model for the use of wind power prediction. WRF model is used to recalculate the meteorological elements of trial wind farm from Jun. 2008 to Jun. 2009, and the accuracy check result shows that the correlation coefficient between predicted value and corresponding measured value of wind speed reaches 0.72. Predictions accuracy of wind direction,

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21

Sun, Zhen’ao, and Zhe Chen. "Power Generation Prediction Method of Offshore Wind Turbines Based on Cascaded Deep Learning." International Transactions on Electrical Energy Systems 2022 (September 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/4404867.

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Aiming at the problems of low prediction accuracy, long time, and poor results in current wind turbine generation power prediction methods, an offshore wind turbine generation power prediction method based on cascaded deep learning is proposed. Using deep belief networks, stacked autoencoding networks, and long short-term memory networks, a cascaded deep learning method is proposed to predict the power generation of offshore wind turbines. Multiple feature extractors are used to extract and fuse high-level features to form a unified feature with richer information to predict the power generati

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22

Yang, Xi Yun, Peng Wei, Huan Liu, and Bao Jun Sun. "Short-Term Wind Power Forecasting Based on SVM with Backstepping Wind Speed of Power Curve." Applied Mechanics and Materials 224 (November 2012): 401–5. http://dx.doi.org/10.4028/www.scientific.net/amm.224.401.

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Accurate wind farm power prediction can relieve the disadvantageous impact of wind power plants on power systems and reduce the difficulty of the scheduling of power dispatching department. Improving accuracy of short-term wind speed prediction is the key of wind power prediction. The authors have studied the short-term wind power forecasting of power plants and proposed a model prediction method based on SVM with backstepping wind speed of power curve. In this method, the sequence of wind speed that is calculated according to the average power of the wind farm operating units and the scene of

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23

Yang, Yiheng. "Wind Power Prediction Based on LSTM and Self-Attention Mechanism." Applied and Computational Engineering 141, no. 1 (2025): 30–38. https://doi.org/10.54254/2755-2721/2025.21570.

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With the intensification of global climate change and energy crises, wind energy, as a clean and renewable energy source, has gradually become a crucial component in the energy sector. However, the intermittent and unstable nature of wind power generation poses significant challenges to accurately predicting the power output of wind turbines. This study proposes a wind power prediction model combining Long Short-Term Memory (LSTM) networks and Self-Attention mechanisms. LSTM net- works effectively capture long-term dependencies in time series through their gat- ing mechanisms, while the Self-A

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24

Yang, Yankun, Yuling Li, Lin Cheng, and Shiyou Yang. "Short-Term Wind Power Prediction Based on a Modified Stacking Ensemble Learning Algorithm." Sustainability 16, no. 14 (2024): 5960. http://dx.doi.org/10.3390/su16145960.

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A high proportion of new energy has become a prominent feature of modern power systems. Due to the intermittency, volatility, and strong randomness in wind power generation, an accurate and reliable method for the prediction of wind power is required. This paper proposes a modified stacking ensemble learning method for short-term wind power predictions to reduce error and improve the generalization performance of traditional single networks in tackling the randomness of wind power. Firstly, the base learners including tree-based models and neural networks are improved based on the Bagging and

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25

Cai, Zelin, Tao Feng, Jun Guo, Bo Hu, and Lei Wang. "Wind power short-term prediction over mountain area using a high-resolution WRF model." E3S Web of Conferences 260 (2021): 02012. http://dx.doi.org/10.1051/e3sconf/202126002012.

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Accurate wind power prediction are crucial for power-grid integration and load balancing, as well as the safe and stable operation of the power grid. In this study, the relationship between the wind speed and wind power over mountain area is firstly investigated using the observations in Hunan Baiguoshan Mountain, and the fitting equation is proposed to predict the wind power with wind speed. Using the simulation of the WRF model with a 3-kilometer horizontal resolution, its prediction performance for short-term wind power is further analyzed. The results show that a sixth power relationship e

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26

Kim, Gyeongmin, and Jin Hur. "A Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations." Sustainability 13, no. 22 (2021): 12723. http://dx.doi.org/10.3390/su132212723.

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Renewable-power-generating resources can provide unlimited clean energy and emit at most minute amounts of air pollutants and greenhouse gases, whereas fossil fuels are contributing to environmental pollution problems and climate change. The share of global power capacity comprising renewable-power-generating resources is increasing. However, due to the variability and uncertainty of wind resources, predicting the power output of these resources remains a key problem that must be resolved to establish stable power system operation and planning. In this study, we propose an ensemble prediction

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27

Xue, Yanan, Jinliang Yin, and Xinhao Hou. "Short-Term Wind Power Prediction Based on Multi-Feature Domain Learning." Energies 17, no. 13 (2024): 3313. http://dx.doi.org/10.3390/en17133313.

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Wind energy, as a key link in renewable energy, has seen its penetration in the power grid increase in recent years. In this context, accurate and reliable short-term wind power prediction is particularly important for the real-time scheduling and operation of power systems. However, many deep learning-based methods rely on the relationship between wind speed and wind power to build a prediction model. These methods tend to consider only the temporal features and ignore the spatial and frequency domain features of the wind power variables, resulting in poor prediction accuracy. In addition to

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Jency, W. G., and J. E. Judith. "Pearson Autocovariance Distinct Patterns and Attention-Based Deep Learning for Wind Power Prediction." Journal of Electrical and Computer Engineering 2022 (April 22, 2022): 1–12. http://dx.doi.org/10.1155/2022/8498021.

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Swift development in wind power and extension of wind generation necessitates significant research in numerous fields. Due to this, wind power is weather dependent; it is fluctuating and is sporadic over numerous time periods. Hence, timely wind power prediction is perceived as an extensive contribution to well-grounded wind power prediction with complex patterns. In addition, a number of wind power prediction methods have been developed. For proper planning and operation of power systems with complicated patterns, wind power prediction in an accurate and timely manner is essential. This paper

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Zhang, Pei, Yanling Wang, Likai Liang, Xing Li, and Qingtian Duan. "Short-Term Wind Power Prediction Using GA-BP Neural Network Based on DBSCAN Algorithm Outlier Identification." Processes 8, no. 2 (2020): 157. http://dx.doi.org/10.3390/pr8020157.

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Accurately predicting wind power plays a vital part in site selection, large-scale grid connection, and the safe and efficient operation of wind power generation equipment. In the stage of data pre-processing, density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify the outliers in the wind power data and the collected wind speed data of a wind power plant in Shandong Province, and the linear regression method is used to correct the outliers to improve the prediction accuracy. Considering the important impact of wind speed on power, the average value,

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Lee, Joseph C. Y., Peter Stuart, Andrew Clifton, et al. "The Power Curve Working Group's assessment of wind turbine power performance prediction methods." Wind Energy Science 5, no. 1 (2020): 199–223. http://dx.doi.org/10.5194/wes-5-199-2020.

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Abstract. Wind turbine power production deviates from the reference power curve in real-world atmospheric conditions. Correctly predicting turbine power performance requires models to be validated for a wide range of wind turbines using inflow in different locations. The Share-3 exercise is the most recent intelligence-sharing exercise of the Power Curve Working Group, which aims to advance the modeling of turbine performance. The goal of the exercise is to search for modeling methods that reduce error and uncertainty in power prediction when wind shear and turbulence digress from design condi

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Shi, Yuxuan, Yanyu Wang, and Haoran Zheng. "Wind Speed Prediction for Offshore Sites Using a Clockwork Recurrent Network." Energies 15, no. 3 (2022): 751. http://dx.doi.org/10.3390/en15030751.

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Offshore sites show greater potential for wind energy utilization than most onshore sites. When planning an offshore wind power farm, the speed of offshore wind is used to estimate various operation parameters, such as the power output, extreme wind load, and fatigue load. Accurate speed prediction is crucial to the running of wind power farms and the security of smart grids. Unlike onshore wind, offshore wind has the characteristics of random, intermittent, and chaotic, which will cause the time series of wind speeds to have strong nonlinearity. It will bring greater difficulties to offshore

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He, Jia, Fangchun Tang, Junxin Feng, et al. "Wind Power Prediction Method and Outlook in Microtopographic Microclimate." Energies 18, no. 7 (2025): 1686. https://doi.org/10.3390/en18071686.

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With the increase in installed capacity of wind turbines, the stable operation of the power system has been affected. Accurate prediction of wind power is an important condition to ensure the healthy development of the wind power industry and the safe operation of the power grid. This paper first introduces the current status of wind power prediction methods under normal weather, and introduces them in detail from three aspects: physical model method, statistical prediction method and combined prediction method. Then, from the perspectives of numerical simulation analysis and statistical predi

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Jency, W. G., and J. E. Judith. "Homogenized Point Mutual Information and Deep Quantum Reinforced Wind Power Prediction." International Transactions on Electrical Energy Systems 2022 (December 14, 2022): 1–15. http://dx.doi.org/10.1155/2022/3686786.

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Accurate wind power prediction is very predominant for genuine and effective power systems with high wind power perception. Wind power prediction, as well as wind power generation resources, receives the electrical energy by converting wind into rotational energy of the blades and converting rotational energy into electrical energy by the generator. Wind energy increases with the cube of wind speed. There are numerous common and deep learning methods that have evolved to attain wind power prediction. Deep learning-based methods are referred to as straightforward, and robust, and have been util

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Wu, Xiaomei, Songjun Jiang, Chun Sing Lai, Zhuoli Zhao, and Loi Lei Lai. "Short-Term Wind Power Prediction Based on Data Decomposition and Combined Deep Neural Network." Energies 15, no. 18 (2022): 6734. http://dx.doi.org/10.3390/en15186734.

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A hybrid short-term wind power prediction model based on data decomposition and combined deep neural network is proposed with the inclusion of the characteristics of fluctuation and randomness of nonlinear signals, such as wind speed and wind power. Firstly, the variational mode decomposition (VMD) is used to decompose the wind speed and wind power sequences in the input data to reduce the noise in the original signal. Secondly, the decomposed wind speed and wind power sub-sequences are reconstructed into new data sets with other related features as the input of the combined deep neural networ

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35

Zhou, Jianguo, Xiaolei Xu, Xuejing Huo, and Yushuo Li. "Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks." Sustainability 11, no. 3 (2019): 650. http://dx.doi.org/10.3390/su11030650.

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The randomness and volatility of wind power poses a serious threat to the stability, continuity, and adjustability of the power system when it is connected to the grid. Accurate short-term wind power prediction methods have important practical value for achieving high-precision prediction of wind farm power generation and safety and economic dispatch. Therefore, this paper proposes a novel combined model to improve the accuracy of short-term wind power prediction, which involves grey correlation degree analysis, ESMD (extreme-point symmetric mode decomposition), sample entropy (SampEn) theory,

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36

Li, Fan, Hongzhen Wang, Dan Wang, Dong Liu, and Ke Sun. "A Review of Wind Power Prediction Methods Based on Multi-Time Scales." Energies 18, no. 7 (2025): 1713. https://doi.org/10.3390/en18071713.

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In response to the ‘zero carbon’ goal, the development of renewable energy has become a global consensus. Among the array of renewable energy sources, wind energy is distinguished by its considerable installed capacity on a global scale. Accurate wind power prediction provides a fundamental basis for power grid dispatching, unit combination operation, and wind farm operation and maintenance. This study establishes a framework to bridge theoretical innovations with practical implementation challenges in wind power prediction. This work uses a narrative method to synthesize and discuss wind powe

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Tian, Yuqian, Dazhi Wang, Guolin Zhou, Jiaxing Wang, Shuming Zhao, and Yongliang Ni. "An Adaptive Hybrid Model for Wind Power Prediction Based on the IVMD-FE-Ad-Informer." Entropy 25, no. 4 (2023): 647. http://dx.doi.org/10.3390/e25040647.

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Accurate wind power prediction can increase the utilization rate of wind power generation and maintain the stability of the power system. At present, a large number of wind power prediction studies are based on the mean square error (MSE) loss function, which generates many errors when predicting original data with random fluctuation and non-stationarity. Therefore, a hybrid model for wind power prediction named IVMD-FE-Ad-Informer, which is based on Informer with an adaptive loss function and combines improved variational mode decomposition (IVMD) and fuzzy entropy (FE), is proposed. Firstly,

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38

Wang, Bo, Tiancheng Wang, Mao Yang, Chao Han, Dawei Huang, and Dake Gu. "Ultra-Short-Term Prediction Method of Wind Power for Massive Wind Power Clusters Based on Feature Mining of Spatiotemporal Correlation." Energies 16, no. 6 (2023): 2727. http://dx.doi.org/10.3390/en16062727.

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With the centralization of wind power development, power-prediction technology based on wind power clusters has become an important means to reduce the volatility of wind power, so a large-scale power-prediction method of wind power clusters is proposed considering the prediction stability. Firstly, the fluctuating features of wind farms are constructed by acquiring statistical features to further build a divided model of wind power clusters using fuzzy clustering algorithm. Then the spatiotemporal features of the data of wind power are obtained using a spatiotemporal attention network to trai

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39

Yang, Mao, Gang Du, and Li Sun. "Probabilistic Interval Prediction of Wind Power." Applied Mechanics and Materials 740 (March 2015): 429–32. http://dx.doi.org/10.4028/www.scientific.net/amm.740.429.

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As wind power generation rapid development in china, wind power prediction is the key to the system operate safely. Given significant uncertainties involved in wind generation, probabilistic interval forecasting provides a unique solution to estimate and quantify the potential impacts and risks facing system operation with wind penetration beforehand. this paper based on the point forecast, calculate wind power prediction error, formulate the distribution of prediction error, you can get the historical probabilistic distribution of prediction error, use the distribution of error to build the r

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40

Zhou, Yan, Fuzhen Wei, Kaiyang Kuang, and Rabea Jamil Mahfoud. "Research on a Deep Ensemble Learning Model for the Ultra-Short-Term Probabilistic Prediction of Wind Power." Electronics 13, no. 3 (2024): 475. http://dx.doi.org/10.3390/electronics13030475.

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An accurate method for predicting wind power is crucial in effectively mitigating wind energy fluctuations and ensuring a stable power supply. Nevertheless, the inadequacy of the stability of wind energy severely hampers the consistent functioning of the power grid and the reliable provision of electricity. To enhance the accuracy of wind power forecasting, this paper proposes an ensemble model named the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and convolutional bidirectional long short-term memory (CNN-BiLSTM), which incorporates a data preprocessing techni

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41

Kader, Mst Sharmin, Riyadzh Mahmudh, Han Xiaoqing, Ashfaq Niaz, and Muhammad Usman Shoukat. "Active power control strategy for wind farms based on power prediction errors distribution considering regional data." PLOS ONE 17, no. 8 (2022): e0273257. http://dx.doi.org/10.1371/journal.pone.0273257.

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One of the renewable energy resources, wind energy is widely used due to its wide distribution, large reserves, green and clean energy, and it is also an important part of large-scale grid integration. However, wind power has strong randomness, volatility, anti-peaking characteristics, and the problem of low wind power prediction accuracy, which brings serious challenges to the power system. Based on the difference of power prediction error and confidence interval between different new energy power stations, an optimal control strategy for active power of wind farms was proposed. Therefore, we

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42

Yang, Zhang, Yang, and Lv. "Deterministic and Probabilistic Wind Power Forecasting Based on Bi-Level Convolutional Neural Network and Particle Swarm Optimization." Applied Sciences 9, no. 9 (2019): 1794. http://dx.doi.org/10.3390/app9091794.

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The intermittency and uncertainty of wind power result in challenges for large-scale wind power integration. Accurate wind power prediction is becoming increasingly important for power system planning and operation. In this paper, a probabilistic interval prediction method for wind power based on deep learning and particle swarm optimization (PSO) is proposed. Variational mode decomposition (VMD) and phase space reconstruction are used to pre-process the original wind power data to obtain additional details and uncover hidden information in the data. Subsequently, a bi-level convolutional neur

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43

Zhang, Yi, Feng Zhang, Yutao Qiu, et al. "Research on refined prediction of coastal wind power based on dynamic downscale and deep learning prediction." Journal of Physics: Conference Series 2488, no. 1 (2023): 012051. http://dx.doi.org/10.1088/1742-6596/2488/1/012051.

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Abstract With the expansion of the installed capacity of offshore wind power, its impact on the safe operation of the power grid is increasingly obvious, and improving the prediction accuracy of offshore wind power power has become a key problem to be solved in the industry. The improvement of prediction accuracy, on the one hand, can facilitate the operation control of power grid, on the other hand, can also significantly improve the economic benefits of wind power plants. Based on power downscaling and deep learning methods, this paper carries out localized short-term and ultra-short-term po

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44

Waweru, Paul, Charles Kagiri, and Titus Mulembo. "Wind Power Prediction Model Using Machine Learning." Journal of Power, Energy, and Control 1, no. 1 (2024): 48–57. http://dx.doi.org/10.62777/pec.v1i1.6.

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Before installing a wind turbine, it's essential to conduct wind power forecasting to gauge the effectiveness of the wind power initiative. Conventionally, wind speed measurements have been conducted instantaneously between various points. These measurement points solely indicate the locations where wind turbines will be positioned. However, these locations might exhibit reduced wind speeds, potentially making them less suitable for the optimal placement of the wind turbine. To address location challenges, we suggest conducting wind power predictions in areas where wind measuring instruments a

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45

Wang, Hao, Chen Peng, Bolin Liao, Xinwei Cao, and Shuai Li. "Wind Power Forecasting Based on WaveNet and Multitask Learning." Sustainability 15, no. 14 (2023): 10816. http://dx.doi.org/10.3390/su151410816.

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Accurately predicting the power output of wind turbines is crucial for ensuring the reliable and efficient operation of large-scale power systems. To address the inherent limitations of physical models, statistical models, and machine learning algorithms, we propose a novel framework for wind turbine power prediction. This framework combines a special type of convolutional neural network, WaveNet, with a multigate mixture-of-experts (MMoE) architecture. The integration aims to overcome the inherent limitations by effectively capturing and utilizing complex patterns and trends in the time serie

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46

Bokde, Neeraj, Andrés Feijóo, Daniel Villanueva, and Kishore Kulat. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction." Energies 12, no. 2 (2019): 254. http://dx.doi.org/10.3390/en12020254.

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Reliable and accurate planning and scheduling of wind farms and power grids to ensure sustainable use of wind energy can be better achieved with the use of precise and accurate prediction models. However, due to the highly chaotic, intermittent and stochastic behavior of wind, which means a high level of difficulty when predicting wind speed and, consequently, wind power, the evolution of models capable of narrating data of such a complexity is an emerging area of research. A thorough review of literature, present research overviews, and information about possible expansions and extensions of

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47

Wei, Chih-Chiang, and Cheng-Shu Chiang. "Assessment of Offshore Wind Power Potential and Wind Energy Prediction Using Recurrent Neural Networks." Journal of Marine Science and Engineering 12, no. 2 (2024): 283. http://dx.doi.org/10.3390/jmse12020283.

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In recent years, Taiwan has actively pursued the development of renewable energy, with offshore wind power assessments indicating that 80% of the world’s best wind fields are located in the western seas of Taiwan. The aim of this study is to maximize offshore wind power generation and develop a method for predicting offshore wind power, thereby exploring the potential of offshore wind power in Taiwan. The research employs machine learning techniques to establish a wind speed prediction model and formulates a real-time wind power potential assessment method. The study utilizes long short-term m

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48

Shi, Chongqing, and Xiaoli Zhang. "Recurrent neural network wind power prediction based on variational modal decomposition improvement." AIP Advances 13, no. 2 (2023): 025027. http://dx.doi.org/10.1063/5.0135711.

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In order to avoid the problem that the traditional recurrent neural network (RNN) wind power prediction model cannot take into account both the law of wind power variation and the impact of sudden change factors, this paper proposes an improved cyclic neural network wind power prediction model based on variational modal decomposition (VMD). The VMD algorithm is used to decompose the output power of wind power into different frequency components and analyze the impact of different frequency components on the prediction model. Combined with the feature extraction ability of the neural network, i

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49

Huang, Qiyue, Yapeng Wang, Xu Yang, and Sio-Kei Im. "Research on Wind Power Prediction Based on A Gated Transformer." Applied Sciences 13, no. 14 (2023): 8350. http://dx.doi.org/10.3390/app13148350.

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Wind power, as a type of renewable energy, has received widespread attention from domestic and foreign experts. Although it has the advantages of cleanliness and low pollution, its strong randomness and volatility can bring disadvantages to the stable operation of the power grid. Accurate power prediction can avoid the adverse effects of wind power, and is of great significance for power grid frequency regulation, peak shaving, and energy improvement. However, traditional wind power prediction methods can only achieve accurate predictions in the short term and perform poorly in medium- to long

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50

Thamizharasi, Ms, Chunduri Aditya, Veeranki Durgabhiram, and Chukka Praveen. "Wind Power Analysis Using Machine Learning in Wind Turbines." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 1559–64. http://dx.doi.org/10.22214/ijraset.2023.52452.

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Abstract: In order to effectively estimate how energy production and consumption will develop and change, this research suggests a new neural network prediction method. The authors concentrate on well-known techniques that can manage a vast volume of data and utilize machine learning to combine results from numerical weather prediction models with local observations. The importance of accurate energy consumption prediction in promoting energy conservation is highlighted, along with the nonlinear correlation between lighting energy usage and its influencing factors. Support vector regression wi

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