Short-Term Wind Power Forecasting Model based on ICA-BP Neural Network
- Zi-Cheng Lan
- Yuan-Biao Zhang
- Jing Zhang
- Xin-Guang Lv
Abstract
It’s of great significance for wind power integration into grid to forecast wind power. Based on forecasting wind power by BP neural network, the article introduces global optimization algorithm, Imperialist Competitive Algorithm (ICA) to provide optimized initial weights of BP neural network. Thus, it can overcome the entrapment in local optical optimum of BP neural network. Compared with BP neural network, it is found that the performances of ICA-BP neural network, which are training, testing and forecasting of wind power, are much better.
- Full Text:
PDF
- DOI:10.5539/cis.v8n1p1
Journal Metrics
WJCI (2020): 0.439
Impact Factor 2020 (by WJCI): 0.247
Google Scholar Citations (March 2022): 6907
Google-based Impact Factor (2021): 0.68
h-index (December 2021): 37
i10-index (December 2021): 172
(Click Here to Learn More)
Index
- Academic Journals Database
- BASE (Bielefeld Academic Search Engine)
- CiteFactor
- CNKI Scholar
- COPAC
- CrossRef
- DBLP (2008-2019)
- EBSCOhost
- EuroPub Database
- Excellence in Research for Australia (ERA)
- Genamics JournalSeek
- Google Scholar
- Harvard Library
- Infotrieve
- LOCKSS
- Mendeley
- PKP Open Archives Harvester
- Publons
- ResearchGate
- Scilit
- SHERPA/RoMEO
- Standard Periodical Directory
- The Index of Information Systems Journals
- The Keepers Registry
- UCR Library
- Universe Digital Library
- WJCI Report
- WorldCat
Contact
- Chris LeeEditorial Assistant
- cis@ccsenet.org