Wind Power Prediction for Wind Farm Clusters Based on the Multi-feature Similarity Matching Method

Peng, Xiaosheng; Chen, Yuzhu; Cheng, Kai; Zhao, Yunzheng; Wang, Bo; Che, Jianfeng; Wen, Jinyu; Lu, Chen; Lee, Wei-Jen

doi:10.1109/ias.2019.8912320

Cited by 8 publications

(9 citation statements)

References 19 publications

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“…It can be seen that, compared with the method of Peng et al [6] and the method of Sun et al [7], the proposed method has a better effect on predicting the power generation of offshore wind turbines.…”

Section: Comparison Of Power Generation Prediction Effects Ofmentioning

confidence: 94%

“…It can be seen from Figure 6 that under different data sample collection intervals, the offshore wind turbine generation power prediction result of the method of Peng et al [6] is relatively large, and there is a certain deviation from the actual value. e prediction result of the offshore wind turbine power generation by the method of Sun et al [7] is relatively small, and the deviation from the actual value is the largest.…”

Section: Comparison Of Power Generation Prediction Effects Ofmentioning

confidence: 99%

“…Offshore Wind Turbines. In order to verify the prediction effect of the proposed method, the methods of Peng et al [6] and Sun et al [7] are used to compare with the proposed method, and the comparison results of the power generation power prediction results of different methods of offshore wind turbines are shown in Figure 6.…”

Section: Comparison Of Power Generation Prediction Effects Ofmentioning

confidence: 99%

“…Turbines. To further verify the prediction time of the proposed method, the method of Peng et al [6], the method of Sun et al [7], and the proposed method were used to compare, and the comparison results of the power generation power prediction time of offshore wind turbines were obtained as shown in Figure 7.…”

Section: Comparison Of Power Generation Forecast Time Of Offshore Windmentioning

confidence: 99%

“…At present, scholars in related fields have studied wind power prediction and achieved some theoretical results. Peng et al [6] proposed a wind power prediction method for wind farms based on multifeature similarity matching. e multifeature similarity matching method is used to optimize and analyze the key parameters.…”

Section: Introductionmentioning

confidence: 99%

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Power Generation Prediction Method of Offshore Wind Turbines Based on Cascaded Deep Learning

Sun

Chen

2022

International Transactions on Electrical Energy Systems

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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 generation sequence of offshore wind turbines. According to the modeling strategy and port design strategy, using the stacked autoencoding networks as the basic unit, a cascaded deep learning model for generating power prediction of offshore wind turbines is established. Through the selection of input variables, the variables that have a great correlation with wind power are obtained. The layer-by-layer greedy algorithm is used for training from bottom to top, and supervised learning is used to fine-tune the network parameters from top to bottom to realize the generation power prediction of the offshore wind turbine. The experimental results show that the proposed method is effective in predicting the power generation of offshore wind turbines, which can effectively improve the prediction accuracy and shorten the prediction time.

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Section: Comparison Of Power Generation Prediction Effects Ofmentioning

confidence: 94%

Section: Comparison Of Power Generation Prediction Effects Ofmentioning

confidence: 99%

Section: Comparison Of Power Generation Prediction Effects Ofmentioning

confidence: 99%

Section: Comparison Of Power Generation Forecast Time Of Offshore Windmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Power Generation Prediction Method of Offshore Wind Turbines Based on Cascaded Deep Learning

Sun

Chen

2022

International Transactions on Electrical Energy Systems

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Wind farm cluster power prediction based on graph deviation attention network with learnable graph structure and dynamic error correction during load peak and valley periods

Yang,

Guo,

Huang

et al. 2024

Energy

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RSMD-RF-BGSkip Based PV Generation Prediction Method

Xie,

Lin

2024

IEEE Access

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With the increasing use of photovoltaic (PV) power generation, power forecasting has become equally important to maintain stable and economic operation of the power system. However, the high frequency component of the PV power time series reduces the accuracy of the model predictions. Therefore, this paper proposes a short-term prediction model for PV power based on Row Secondary Modal Decomposition (RSMD), Random Forest (RF), and BGSkip neural network. Firstly, modal features with different complexity are obtained by RSMD. Secondly, RF is used to select different modal features. Then, to improve the prediction performance of the model, the BGSkip model employs a hybrid neural network to accurately predict the nonlinear part, while the linear part is handled by an autoregressive model. The prediction results of these two parts are integrated through the BGSkip model to output more accurate prediction values. Finally, the historical data of PV power plants in a region of Liaoning is utilized for experimental validation. The experimental results show that 2 R , MAPE E

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Wind Power Prediction for Wind Farm Clusters Based on the Multi-feature Similarity Matching Method

Cited by 8 publications

References 19 publications

Power Generation Prediction Method of Offshore Wind Turbines Based on Cascaded Deep Learning

Power Generation Prediction Method of Offshore Wind Turbines Based on Cascaded Deep Learning

Wind farm cluster power prediction based on graph deviation attention network with learnable graph structure and dynamic error correction during load peak and valley periods

RSMD-RF-BGSkip Based PV Generation Prediction Method

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