Journal
APPLIED SOFT COMPUTING
Volume 94, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2020.106463
Keywords
Novel combined forecasting system; Modified multi-objective optimization; Wind farm; Sub-models selection
Categories
Funding
- Ministry of Education - China Mobile Research Foundation [MCM20170206]
- Fundamental Research Funds for the Central Universities [lzujbky-2019-kb51, lzujbky-2018-k12]
- National Natural Science Foundation of China [61402210]
- Major National Project of High Resolution Earth Observation System [30-Y20A34-9010-15/17]
- State Grid Corporation of China Science and Technology Project [SGGSKY00WYJS2000062]
- Program for New Century Excellent Talents in University [NCET-12-0250]
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDA03030100]
- Google Research Awards
- Google Faculty Award
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Forecasting models have been widely used in wind-speed time series forecasting that are often nonlinear, irregular, and non-stationary. Current forecasting models based on artificial neural network can adapt to various wind-speed time series. However, they cannot simultaneously and effectively forecast the entire wind-speed time series of a wind farm. In this paper, a novel combined forecasting system is developed for a wind farm that includes that SSAWD secondary de-noising algorithm is used to pre-process original wind speed data, and then the sub-model selection strategy is used to select five optimal sub models for the combined model. Meanwhile, a modified multi-objective optimization algorithm optimizes weight of the combined model, and the experimental results show that this forecasting system outperforms other traditional systems and can be effectively used to forecast wind-speed time series of a large wind farm. (C) 2020 Elsevier B.V. All rights reserved.
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