Study of Short-Term Wind Power Forecasting Based on Adaptive Grey Prediction Method

2015 ◽  
Vol 734 ◽  
pp. 697-700 ◽  
Author(s):  
Wen Zhen Cai ◽  
Dong Tao Wang ◽  
Yuan Song Wang ◽  
Yong Yang ◽  
Zhi Long Gao
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Prediction Method ◽  
Test System ◽  
Grey Prediction ◽  
Grey System ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Short Term Forecasting ◽  
Power Forecasting

With the wind power developing fast in the world, the large scale of wind power integration in power system leads to great challenges, and the wind power forecasting will play a key role in dealing with these challenges. A wind power short-term forecasting method based on grey system is introduced in this paper. Firstly, a basic model of grey prediction method is given. Then, in order to smoothen the basic data for the grey modeling, a self adaptive grey prediction method is developed. Finally, the result of prediction for a test system of wind power are presented and the effectiveness of the method given by the paper has been proved.

scholarly journals A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA

Energies ◽  
2018 ◽  
Vol 11 (4) ◽  
pp. 697 ◽  
Author(s):  
Peng Lu ◽  
Lin Ye ◽  
Bohao Sun ◽  
Cihang Zhang ◽  
Yongning Zhao ◽  
...  
Keyword(s):  
Wind Power ◽  
Prediction Method ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Hybrid Prediction ◽  
Power Forecasting

scholarly journals Research on Short-Term Wind Power Forecasting by Data Mining on Historical Wind Resource

2020 ◽  
Vol 10 (4) ◽  
pp. 1295 ◽  
Author(s):  
Bin Tang ◽  
Yan Chen ◽  
Qin Chen ◽  
Mengxing Su
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Wind Power ◽  
Meteorological Data ◽  
Prediction Method ◽  
Short Term ◽  
Elman Neural Network ◽  
Wind Power Forecasting ◽  
Interval Prediction ◽  
Power Forecasting

In order to enhance the accuracy of short-term wind power forecasting (WPF), a short-term wind power forecasting method based on historical wind resources by data mining has been designed. Firstly, the spoiled data resulting from wind turbine and meteorological monitoring equipment is eliminated, and the missing data is added by the Lomnaofski optimization model, which is based on the temporal-spatial correlation of meteorological data. Secondly, the wind characteristics are analyzed by the continuous time similarity clustering (CTSC) method, which is used to select similar samples. To improve the accuracy of deterministic prediction and prediction error, the radial basis function neural network (RBF) deterministic forecasting model was built, which can approximate nonlinear solutions. In addition, the wind power interval prediction method, combining fuzzy information granulation and an Elman neural network (FIG-Elman), is proposed to acquire forecasting intervals. The deterministic prediction of the RBF-CTSC model has high accuracy, which can accurately describe the randomness, fluctuation and nonlinear characteristics of wind speed. Additionally, the mean absolute error (MAE) and root mean square error (RMSE) are reduced by the new model. The interval prediction of FIG-Elman results show that the interval width decreased by 18.85%, and the coverage probability of interval increased by 10.94%.

Forecast of short-term wind power based on a novel hybrid method

2019 ◽  
Vol 234 (8) ◽  
pp. 937-947
Author(s):  
Dinghui Wu ◽  
Haibo Huang ◽  
Ren Xiao ◽  
Cong Gao
Keyword(s):  
Wind Power ◽  
High Frequency ◽  
Low Frequency ◽  
Prediction Method ◽  
Gray Model ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Four Seasons ◽  
Frequency Curves ◽  
Power Forecasting

Short-term wind power forecasting plays an important role in power generation, because it prevents the power system operation from its uncertain and intermittent nature. This article proposes a novel method for short-term wind power forecasting, which combines the wavelet transform, particle swarm optimization dynamic gray model and Lyapunov exponent prediction method. First, the approach decomposes the wind power curve into the high-frequency and low-frequency curves by wavelet transform, which represent the detail and tendency signals, respectively. Then, we use the proposed particle swarm optimization dynamic gray model to forecast the low-frequency curve with its smooth and periodic outline. Moreover, Lyapunov exponent prediction method is used to predict high-frequency curves, which possess the chaos characteristics. Finally, we obtain the wind power forecasting result from the combination of the predicted low and high frequencies. The experiment of four seasons in an US wind farm validates that the proposed method is effective in solving the short-term wind power forecasting problem. The obtained results, discussed comprehensively, show that the hybrid method has better prediction accuracy than the other methods, such as artificial neural network, persistence, and autoregressive integrated moving average model, with the lowest average mean absolute percentage error is 8.07% and the average root mean square error is 0.8164 over four seasons.

scholarly journals Developing a Local Neurofuzzy Model for Short-Term Wind Power Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
E. Faghihnia ◽  
S. Salahshour ◽  
A. Ahmadian ◽  
N. Senu
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Learning Algorithm ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Large Scale Integration ◽  
Scale Integration ◽  
Power Forecasting

Large scale integration of wind generation capacity into power systems introduces operational challenges due to wind power uncertainty and variability. Therefore, accurate wind power forecast is important for reliable and economic operation of the power systems. Complexities and nonlinearities exhibited by wind power time series necessitate use of elaborative and sophisticated approaches for wind power forecasting. In this paper, a local neurofuzzy (LNF) approach, trained by the polynomial model tree (POLYMOT) learning algorithm, is proposed for short-term wind power forecasting. The LNF approach is constructed based on the contribution of local polynomial models which can efficiently model wind power generation. Data from Sotavento wind farm in Spain was used to validate the proposed LNF approach. Comparison between performance of the proposed approach and several recently published approaches illustrates capability of the LNF model for accurate wind power forecasting.

scholarly journals A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3586 ◽  
Author(s):  
Sizhou Sun ◽  
Jingqi Fu ◽  
Ang Li
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Search Algorithm ◽  
Interpolation Method ◽  
Least Square ◽  
Support Vector ◽  
Two Stage ◽  
Wind Power Forecasting ◽  
Power Forecasting

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

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