کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7161025 | 1462848 | 2016 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی (عمومی)
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China](/preview/png/7161025.png)
چکیده انگلیسی
Reducing the dependence on fossil-fuel-based resources is becoming significant due to the detrimental effects on environment and global energy-dependent. Thus, increased attention has been paid to wind power, a type of clean and renewable energy. However, owing to the stochastic nature of wind speed, it is essential to build a wind speed forecasting model with high-precision for wind power utilization. Therefore, this paper proposes a hybrid model which combines fast ensemble empirical model decomposition (FEEMD) with regularized extreme learning machine (RELM). The original wind speed series are first decomposed into a limited number of intrinsic mode functions (IMFs) and one residual series. Then RELM is built to forecast the sub-series. Partial auto correlation function (PACF) is applied to analyze the intrinsic relationships between the historical speeds so as to select the inputs of RELM. To verify the developed models, short-term wind speed data in July 2010 and monthly data from January 2000 to May 2010 in Hong songwa wind farm, Chengde city are used for model construction and testing. Two additional forecasting cases in Hebei province are also applied to prove the model's validity. The simulation test results show that the built model is effective, efficient and practicable.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy Conversion and Management - Volume 114, 15 April 2016, Pages 197-208
Journal: Energy Conversion and Management - Volume 114, 15 April 2016, Pages 197-208
نویسندگان
Wei Sun, Mohan Liu,