scholarly journals Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method

Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 843 ◽  
Author(s):  
Keke Wang ◽  
Dongxiao Niu ◽  
Lijie Sun ◽  
Hao Zhen ◽  
Jian Liu ◽  
...  
Keyword(s):  
Wind Power ◽  
Extreme Learning Machine ◽  
Harmony Search ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Wind Power Forecasting ◽  
Correlation Degree ◽  
Learning Machine ◽  
Power Forecasting

Accurately predicting wind power is crucial for the large-scale grid-connected of wind power and the increase of wind power absorption proportion. To improve the forecasting accuracy of wind power, a hybrid forecasting model using data preprocessing strategy and improved extreme learning machine with kernel (KELM) is proposed, which mainly includes the following stages. Firstly, the Pearson correlation coefficient is calculated to determine the correlation degree between multiple factors of wind power to reduce data redundancy. Then, the complementary ensemble empirical mode decomposition (CEEMD) method is adopted to decompose the wind power time series to decrease the non-stationarity, the sample entropy (SE) theory is used to classify and reconstruct the subsequences to reduce the complexity of computation. Finally, the KELM optimized by harmony search (HS) algorithm is utilized to forecast each subsequence, and after integration processing, the forecasting results are obtained. The CEEMD-SE-HS-KELM forecasting model constructed in this paper is used in the short-term wind power forecasting of a Chinese wind farm, and the RMSE and MAE are as 2.16 and 0.39 respectively, which is better than EMD-SE-HS-KELM, HS-KELM, KELM and extreme learning machine (ELM) model. According to the experimental results, the hybrid method has higher forecasting accuracy for short-term wind power forecasting.

scholarly journals Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine

2021 ◽  
Vol 13 (18) ◽  
pp. 10453
Author(s):  
Guoqing An ◽  
Ziyao Jiang ◽  
Libo Chen ◽  
Xin Cao ◽  
Zheng Li ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Coefficient Of Determination ◽  
Optimal Time ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Learning Machine ◽  
Power Forecasting

Improving the accuracy of wind power forecasting is an important measure to deal with the uncertainty and volatility of wind power. Wind speed and wind direction are the most important factors affecting the power generation of wind turbines. In this paper, we propose a wind power forecasting method that combines the sparrow search algorithm (SSA) with the deep extreme learning machine (DELM). Based on the DELM model, the length of the time series’ influence on the performance of the neural network is validated through the comparison of the forecast error indexes, and the optimal time series length of the wind power is determined. The sparrow search algorithm is used to optimize its parameters to solve the problem of random changes in model input weights and thresholds. The proposed SSA-DELM model is validated using the measured data of a certain wind turbine, and various forecasting indexes are compared with several current wind power forecasting methods. The experimental results show that the proposed model has better performance in ultra-short-term wind power forecasting, and its coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) are 0.927, 69.803, and 115.446, respectively.

scholarly journals Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6319
Author(s):  
Chia-Sheng Tu ◽  
Chih-Ming Hong ◽  
Hsi-Shan Huang ◽  
Chiung-Hsing Chen
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Regression Model ◽  
Wind Power ◽  
Support Vector ◽  
Forecasting Model ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Data Regression ◽  
Power Forecasting

This paper presents a short-term wind power forecasting model for the next day based on historical marine weather and corresponding wind power output data. Due the large amount of historical marine weather and wind power data, we divided the data into clusters using the data regression (DR) algorithm to get meaningful training data, so as to reduce the number of modeling data and improve the efficiency of computing. The regression model was constructed based on the principle of the least squares support vector machine (LSSVM). We carried out wind speed forecasting for one hour and one day and used the correlation between marine wind speed and the corresponding wind power regression model to realize an indirect wind power forecasting model. Proper parameter settings for LSSVM are important to ensure its efficiency and accuracy. In this paper, we used an enhanced bee swarm optimization (EBSO) to perform the parameter optimization for LSSVM, which not only improved the forecast model availability, but also improved the forecasting accuracy.

NARX Based Short Term Wind Power Forecasting Model

2015 ◽  
Vol 15 (1) ◽  
Author(s):  
M. Nandana Jyothi ◽  
V. Dinakar ◽  
N. S S Ravi Teja ◽  
K. Nanda Kishore
Keyword(s):  
Wind Power ◽  
Forecasting Model ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Power Forecasting

scholarly journals Short-Term Wind Power Forecasting Model based on ICA-BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
Author(s):  
Zi-Cheng Lan ◽  
Yuan-Biao Zhang ◽  
Jing Zhang ◽  
Xin-Guang Lv
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Bp Neural Network ◽  
Forecasting Model ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Model Based ◽  
Power Forecasting

Wind Power Forecasting Model Fusion Evaluation Based on Comprehensive Weights

2016 ◽  
Author(s):  
Jianyan Tian ◽  
Tingting Liu ◽  
Amit Banerjee ◽  
Aixue Wei ◽  
Shengqiang Yang ◽  
...  
Keyword(s):  
Wind Power ◽  
Evaluation Method ◽  
Forecasting Model ◽  
Model Optimization ◽  
Forecasting Accuracy ◽  
Wind Power Forecasting ◽  
Single Index ◽  
Forecasting Models ◽  
The Individual ◽  
Power Forecasting

Studies show that fusion modeling can improve the forecasting accuracy of wind power. Fusion modeling is the process of selective use of information from individual forecasting models. The reasonable evaluation of the individual models is the premise and basis of model optimization so that the individual models with high forecasting accuracy can be selected to establish the fusion model. Because the results of a single index model evaluation may not be comprehensive, the multi-index fusion evaluation method based on maximizing deviations and subjective correction is proposed. The method is applied to the selection of short-term wind power forecasting models. Firstly, this method establishes the individual model base of wind power forecasting model. Secondly, it establishes the more comprehensive evaluation index system. Thirdly, it combines maximizing deviations with the subjective correction coefficient to determine the comprehensive weight of each model, which is used to calculate the fusion evaluation value and get the evaluation order to achieve the model optimization. Finally, based on five years of data from a wind power plant in Shanxi Province, the validated experiments by multiple sets of forecasting data have been done using MATLAB in this paper. The simulation results demonstrate that the evaluation based on the proposed fusion evaluation method is more comprehensive and stable compared to evaluation using a single index. More importantly, it can effectively guide the model optimization with simple operating steps.

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