Correlation based neuro-fuzzy Wiener type wind power forecasting model by using special separate signals

2022 ◽  
Vol 253 ◽  
pp. 115173
Author(s):  
Yue Xu ◽  
Li Jia ◽  
Wei Yang
Keyword(s):  
Wind Power ◽  
Forecasting Model ◽  
Wind Power Forecasting ◽  
Neuro Fuzzy ◽  
Wiener Type ◽  
Power Forecasting

An improved wind power forecasting model based on eliminating outliers

2016 ◽  
Vol 40 (1) ◽  
pp. 50-58 ◽  
Author(s):  
Jingxin Guo ◽  
Xiao-Yu Zhang ◽  
Wenling Jang ◽  
Hongqing Wang
Keyword(s):  
Wind Power ◽  
Forecasting Model ◽  
Wind Power Forecasting ◽  
Model Based ◽  
Power Forecasting

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.

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.

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
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