Wind power prediction based on PSO-Kalman

“…The particle swarm algorithm (PSO) [43] belongs to a type of swarm intelligence algorithm, which is designed by simulating the feeding action of a flock of birds. Assuming that there is just one piece of food in the region, the flock's task is to find this food source.…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Hybrid Neural Network Wind Speed Prediction Based on Secondary Decomposition and Weighted Averaging

Bi,

Bai,

Hou

et al. 2024

Preprint

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The randomicity and fluctuation of the wind speed will influence the precision of the forecast. To improve the precision of wind speed forecast, this paper presents a new method of combined wind speed forecast based on the second decomposition and weighted average. First, the ICEEMDAN decomposition method is used to get different sub-sequences, and then the fuzzy entropy is used to judge the degree of confusion of the sub-sequences. In this paper, the ARIMA model is used to predict the minimum fuzzy entropy. And the other subsequences are decomposed by BPNN, VMD and predicted by NAR and BP neural network with suitable weighting ratio for weighted average and PSO-LSTM neural network respectively, and ultimately all the predicted values are superimposed to get the final prediction. Experiments were conducted using three datasets and eight comparison models to verify the validity of this model. The prediction analysis was carried out using the actual measured data of a wind farm in Inner Mongolia, and the results indicated that (1) using fuzzy entropy can effectively improve the prediction precision; (2) the prediction accuracy of the combined prediction method of neural network based on secondary decomposition was greatly improved and the prediction results were more reliable; (3) Decompose one of the subsequences with VMD, predict it with NAR and BP neural network, and choose appropriate weight ratio for weighted average prediction will achieve better prediction results; (4) the root mean square error (RMSE) of the hybrid model on the three wind speed 1 datasets were 0.28777, 0.22786 and 0.17128, which are lower than the comparison values of other models. So, it is workable to use this hybrid model in wind speed prediction.

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“…Existing wind power prediction methods are categorized into ultra-short-term prediction [5,6], short-term prediction [7,8] and mid-to-long-term prediction [9,10], with multiple prediction time dimensions. Commonly used wind power prediction models include physical [11][12][13][14] and statistical [15][16][17][18] methods. Physical methods utilize weather forecast data and related geographic information, and the models are generally more complex.…”

Section: Introductionmentioning

confidence: 99%

ForecastNet Wind Power Prediction Based on Spatio-Temporal Distribution

Peng,

Guo,

Huang

et al. 2024

Applied Sciences

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The integration of large-scale wind power into the power grid threatens the stable operation of the power system. Traditional wind power prediction is based on time series without considering the variability between wind turbines in different locations. This paper proposes a wind power probability density prediction method based on a time-variant deep feed-forward neural network (ForecastNet) considering a spatio-temporal distribution. First, the outliers in the wind turbine data are detected based on the isolated forest algorithm and repaired through Lagrange interpolation. Then, based on the graph attention mechanism, the features of the proximity node information of the individual wind turbines in the wind farm are extracted and the input feature matrix is constructed. Finally, the wind power probability density prediction results are obtained using the ForecastNet model based on three different hidden layer variants. The experimental results show that the ForecastNet model with a hidden layer as a dense network based on the attention mechanism (ADFN) predicts better. The average width of the prediction intervals at achieved confidence levels for all interval coverage is reduced by 34.19%, 35.41%, and 35.17%, respectively, when compared to the model with the hidden layer as a multilayer perceptron. For different categories of wind turbines, ADFN also achieves relatively narrow interval average widths of 368.37 kW, 315.87 kW, and 299.13 kW, respectively.

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Wind power prediction based on PSO-Kalman

Cited by 17 publications

References 8 publications

An overview of deterministic and probabilistic forecasting methods of wind energy

An overview of deterministic and probabilistic forecasting methods of wind energy

Hybrid Neural Network Wind Speed Prediction Based on Secondary Decomposition and Weighted Averaging

ForecastNet Wind Power Prediction Based on Spatio-Temporal Distribution

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