Privacy-preserving distributed parameter estimation for probability distribution of wind power forecast error

Jia, Mengshuo; Huang, Shaowei; Wang, Zhiwen; Shen, Chen

doi:10.1016/j.renene.2020.06.102

Cited by 10 publications

(4 citation statements)

References 46 publications

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“…The results of the algorithm show that it has high reliability and corresponding predictive degree. 11 Theuer et al proposed two different models to reduce the large prediction errors associated with height extrapolation. The experimental results generally found that lidar-based predictions were less sensitive to atmospheric conditions than persistence, and the additional use of wind turbines operational data can significantly improve minute-scale LiDAR-based predictions.…”

Section: Related Workmentioning

confidence: 99%

“…In formula (11), it 𝛽 0 represents the self-attraction of fireflies, and k the value is generally 2, which means distance coefficient. All firefly positions in the whole space are affected by the joint effect of fluorescein intensity and attraction, and the calculation formula is shown in formula (12).…”

Section: Lstm Wind Power Prediction Model Under Fa Fusionmentioning

confidence: 99%

“…Mengshuo et al proposed a distributed expectation maximization algorithm based on the Gaussian mixture model to verify the wind integral data set. The results of the algorithm show that it has high reliability and corresponding predictive degree 11 . Theuer et al proposed two different models to reduce the large prediction errors associated with height extrapolation.…”

Section: Related Workmentioning

confidence: 99%

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Short‐term wind power prediction based on the combination of firefly optimization and LSTM

Zhang,

Zheng

2023

Adv Control Appl

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With the development of social resources, people's consumption of energy is huge, so renewable energy, such as wind energy, has been widely concerned and developed. Although there has been sufficient development of wind power generation, its output has some problems such as uncertainty, which leads to insufficient utilization of wind energy resources and uneven power output quality level, which brings great challenges to the grid connection. To solve this problem, a short‐term wind power prediction model combining firefly algorithm and long term memory network is proposed. The main motivation of the research is to improve the accuracy of wind power prediction and thus improve the utilization of wind energy resources. Compared with the existing methods, the innovation of FA‐LSTM model lies in the integration of the two algorithms, making full use of the advantages of FA in global search optimization and LSTM in time series data processing, and improving the accuracy and stability of prediction. During the experiment, we used different wind farm data to train and test the model. The results show that the FA‐LSTM model can improve the optimal fitness by more than 50% compared with other algorithms, and the iterative prediction error is smaller. Standard root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the model. The accuracy of RMSE and MAE reached over 97% and 98% respectively. When the test data is highly volatile, the data accuracy of FA‐LSTM model reaches 92% and 94%, and the FA‐LSTM model drops to the stable value faster. FA‐LSTM model has the best fitting degree with the true value curve, and the fitting degree reaches more than 90%. Comparing the actual power and predicted power of different units, the actual power of Unit 1 is 34.875, and the predicted power obtained by FA‐LSTM model is 34.935, with an error of only 0.06. The key finding of this study is that the prediction model combining FA and LSTM has high accuracy and stability in wind power prediction, and can effectively deal with the uncertainty and volatility of wind energy resource utilization. FA‐LSTM model provides an effective solution for wind power prediction, which is helpful to improve the utilization rate of wind energy resources.

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Section: Related Workmentioning

confidence: 99%

Section: Lstm Wind Power Prediction Model Under Fa Fusionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Short‐term wind power prediction based on the combination of firefly optimization and LSTM

Zhang,

Zheng

2023

Adv Control Appl

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“…As a result, it is not easy to describe the conversion relationship of PV resources to power completely with the unified forecasting model all year round [12][13][14] . Therefore, a short-term power forecasting model for photovoltaic power generation based on seasonal classification is proposed in this paper and analyzes the characteristics of resource-power conversion under different climate characteristics in detail [15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

The Method of Photovoltaic Power Forecast based on Seasonal Classification and Limit Learning Machine

Li¹,

Ma²,

Hu³

et al. 2023

J. Phys.: Conf. Ser.

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The seasonal distribution characteristics of photovoltaic power plant output fluctuation are analyzed, and a short-term power forecasting method based on seasonal classification is proposed. Firstly, the seasonal distribution characteristics of photovoltaic output and its fluctuation are analyzed. Secondly, the forecasting model of photovoltaic output in different seasons is established by the Limit Learning Machine neural network. Finally, an empirical analysis is carried out by using photovoltaic output data. The results show that the seasonal classification method of short-term PV power forecast is better than the unclassified model.

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Ultra-short-term wind power prediction based on PVMD-ESMA-DELM

Chen

Tan³

et al. 2022

Energy Reports

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Privacy-preserving distributed parameter estimation for probability distribution of wind power forecast error

Cited by 10 publications

References 46 publications

Short‐term wind power prediction based on the combination of firefly optimization and LSTM

Short‐term wind power prediction based on the combination of firefly optimization and LSTM

The Method of Photovoltaic Power Forecast based on Seasonal Classification and Limit Learning Machine

Ultra-short-term wind power prediction based on PVMD-ESMA-DELM

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