Journal
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
Volume 9, Issue 3, Pages 1177-1187Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2017.2774195
Keywords
Lower upper bound estimation (LUBE); optimization; recurrent neural network (RNN); wind power prediction
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Funding
- Natural Science and Engineering Research Council of Canada
- China Scholarship Council [201603170278]
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Interval forecast is an efficient method to quantify the uncertainties in renewable energy production. In this paper, the idea of prediction intervals (PIs) is employed to capture the uncertainty of wind power generation in power systems. The recurrent neural network (RNN) model is proposed to construct PIs with the lower upper bound estimation method, which is a powerful non-parametric forecast approach. In addition, a novel comprehensive cost function with a new PI evaluation index is designed with the purpose of enhancing the model training. To tune the parameters of RNN prediction model, the dragonfly algorithm with a linearly random weight update method is introduced as the optimization tool. The performance of the proposed prediction model is validated by a case study using a real world wind power dataset, and the comparative results show the superiority of the model.
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