Short-term wind power prediction using an improved grey wolf optimization algorithm with back-propagation neural network
نویسندگان
چکیده
Abstract A short-term wind power prediction method is proposed in this paper with experimental results obtained from a farm located Northeast China. In order to improve the accuracy of using traditional back-propagation (BP) neural network algorithm, improved grey wolf optimization (IGWO) algorithm has been adopted optimize its parameters. The performance evaluated by experiments. First, features are described show fundamental information single turbine rated 1500 kW and generation coefficient 2.74 was introduced technical details turbines. Original data whole were preprocessed quartile remove abnormal points. Then, retained predicted analysed IGWO–BP algorithm. Analysis proves practicability efficiency model. Results that average ~11% greater than BP method. way, can be ensure effective utilization energy. designed tested method,
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ژورنال
عنوان ژورنال: Clean energy
سال: 2022
ISSN: ['2515-396X', '2515-4230']
DOI: https://doi.org/10.1093/ce/zkac011