WSBP/NWBP small-world neural network and its performance for wind power forecasting
- Beijing Jiaotong Univ. (China)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
A small-world neural network has stronger generalization ability with high transfer efficiency than that of the regular neural networks. This paper presents two novel small-world neural networks, the Watts-Strogatz small-world based on a BP neural network (WSBP) and a Newman-Watts small-world neural network based on a BP neural network (NWBP), related to previous research of complex networks. The algorithms are developed separately by adopting WS and NW small-world networks as their topological structures, and their derivation and convergence criterion are progressively discussed. After that, the proposed models are subsequently tested by two typical nonlinear functions which confirm their significant improvement over the regular BP networks and other algorithms. Finally, a wind power prediction system is advanced to verify their generalization abilities, and show that the models are practically feasible and effective with improved accuracy and acceptable forecasting errors caused by wind fluctuation and randomness with a time scale up to 24 h.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE; National Natural Science Foundation of China (NNSFC)
- Grant/Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1649556
- Journal Information:
- CSEE Journal of Power and Energy Systems, Journal Name: CSEE Journal of Power and Energy Systems Journal Issue: 2 Vol. 6; ISSN 2096-0042
- Publisher:
- Chinese Society for Electrical Engineering (CSEE)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
Wind power forecasting using advanced neural networks models
Scaling and percolation in the small-world network model