Multisource Wind Speed Fusion Method for Short-Term Wind Power Prediction

An, Jianqi; Yin, Feng; Wu, Min; She, Jinhua; Chen, Xin

doi:10.1109/tii.2020.3006928

Cited by 21 publications

(10 citation statements)

References 34 publications

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“…Various wind power probabilistic forecasting models are proposed in the reviewed works [24–65]. All these works focus on short‐term wind power forecasting.…”

Section: Short‐term Wppf Complexity and Evaluationmentioning

confidence: 99%

“…Historical data [24-31, 34, 36-38, 41-64] Meteorological data [27, 36, 38, 41-42, 59, 62, 64] NWP [31,35,40,41,43,44,58,61,63] TABLE 6 Details on NWP of reviewed works…”

Section: Data Type Reviewed Workmentioning

confidence: 99%

“…Improving the accuracy of ultra‐short‐term forecasting models could play a decisive role in the operation and planning of power systems, since it could reduce the spinning reserve cost and further improve the structure of the power grid [67]. Reviewed works [25,28–29, 37, 44, 47, 54–55, 57, 60] and [63–65] investigate ultra‐short‐term forecasting through different models and methodologies.…”

Section: Modelsmentioning

confidence: 99%

“…The forecasting horizon typically ranges from one hour to 48 h [68]. Reviewed works [24–25, 27–29, 31, 34, 36–38] and [40–65] investigate short‐term forecasting through different models and methodologies, using different input data and different error metrics.…”

Section: Modelsmentioning

confidence: 99%

See 3 more Smart Citations

A review of short‐term wind power probabilistic forecasting and a taxonomy focused on input data

Bazionis

Karafotis

Georgilakis

2021

IET Renewable Power Gen

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A review of state‐of‐the‐art short‐term wind power probabilistic forecasting models is the focus here. The improvement of the accuracy and efficiency of probabilistic forecasting models has been in the centre of attention of researchers in recent years, since the need to further comprehend and efficiently use the uncertainty of forecasts is increasing. Since the optimal operation and control of energy systems and electricity markets is one of the important aspects of performing wind power forecasts, this review focuses in short‐term probabilistic forecasting models, which could prove to be useful in the daily planning and operation of power systems. The short‐term concept of forecasts is analysed in detail, along with the case studies and examples proposed by the reviewed literature. The key advantages and disadvantages of the reviewed probabilistic forecasting methodologies are identified. Furthermore, different classifications of the reviewed works according to the data that are used to provide an accurate forecasting model are also provided. Future directions in the field of short‐term wind power probabilistic forecasting are also proposed.

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“…Various wind power probabilistic forecasting models are proposed in the reviewed works [24–65]. All these works focus on short‐term wind power forecasting.…”

Section: Short‐term Wppf Complexity and Evaluationmentioning

confidence: 99%

“…Historical data [24-31, 34, 36-38, 41-64] Meteorological data [27, 36, 38, 41-42, 59, 62, 64] NWP [31,35,40,41,43,44,58,61,63] TABLE 6 Details on NWP of reviewed works…”

Section: Data Type Reviewed Workmentioning

confidence: 99%

Section: Modelsmentioning

confidence: 99%

Section: Modelsmentioning

confidence: 99%

See 2 more Smart Citations

A review of short‐term wind power probabilistic forecasting and a taxonomy focused on input data

Bazionis

Karafotis

Georgilakis

2021

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…The combination prediction model can comprehensively use the statistical information of each single prediction model, establish the combination prediction model according to the technical characteristics and advantages of each model through the idea of complementary advantages, overcome the limitations of the single prediction model, and effectively reduce the probability of large errors. Typical combination prediction models include combination prediction model based on weight coefficient (An et al, 2021; Sun et al, 2019), combination prediction model combined with data preprocessing (Wang et al, 2019; Zhang et al, 2019a), combination prediction model based on model parameter optimization (Qin et al, 2021), etc. A large number of studies show that the prediction accuracy of the combination prediction model has been improved compared with the single prediction model.…”

Section: Introductionmentioning

confidence: 99%

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

Lian

2021

Wind Engineering

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The accuracy of wind power prediction directly affects the operation cost of power grid and is the result of power grid supply and demand balance. Therefore, how to improve the prediction accuracy of wind power is very important. In order to improve the prediction accuracy of wind power, a prediction model based on wavelet denoising and improved slime mold algorithm optimized support vector machine is proposed. The wavelet denoising algorithm is used to denoise the wind power data, and then the support vector machine is used as the prediction model. Because the prediction results of support vector machine are greatly affected by model parameters, an improved slime mold optimization algorithm with random inertia weight mechanism is used to determine the best penalty factor and kernel function parameters in support vector machine model. The effectiveness of the proposed prediction model is verified by using two groups actually collected wind power data. Seven prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, the performance indicators, the Pearson’s correlation coefficient, the DM test, box-plot distribution, the results show that the proposed prediction model has high prediction accuracy.

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An online transfer learning model for wind turbine power prediction based on spatial feature construction and system-wide update

Liu

Wang

et al. 2023

Applied Energy

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Multisource Wind Speed Fusion Method for Short-Term Wind Power Prediction

Cited by 21 publications

References 34 publications

A review of short‐term wind power probabilistic forecasting and a taxonomy focused on input data

A review of short‐term wind power probabilistic forecasting and a taxonomy focused on input data

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

An online transfer learning model for wind turbine power prediction based on spatial feature construction and system-wide update

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