Wind Power Forecasting Based on BP Neural Network
DOI:
https://doi.org/10.62051/ijcsit.v3n3.05Keywords:
Short-term load forecasting, Wind power, BP neural networkAbstract
With the increasing social demand of electric power, electric power companies need to make accurate and reasonable dispatching plans for electric power resources in order to meet the needs of various users. Considering only historical load data and five meteorological factors and date types, the BP neural network model is constructed to simulate and forecast the future seven-day power load. And compare it to the real thing. By comparison and analysis, it is concluded that the forecast accuracy can be improved by considering meteorological factors and date types, which provides an effective reference for decision-makers of electric power companies to make short-term electric power production plan and electric power dispatching arrangement.
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[1] Hu Daobo, Chen Fangfang, Zhang Qian, etc. Short-term wind power forecast based on feature optimization and Sparrow search optimization gated cycle unit [J]. Applied Sciences, 2023, 50(6): 63-68.
[2] Choi Ming-yung, Tung Man-tao, lo chi-kong. Short-term wind power prediction based on density cluster modal decomposition for convolutional neural network and short-term memory networks [J]. Power Science and technology Newspaper, 2024, 33(4): 22-28.
[3] Liu Peihan, Yin Cui, Janne, etc. Short-term wind power prediction based on niche genetic algorithm and radial basis function model [J]. Grid and clean energy, 2020,36(9): 68-75.
[4] Zhao ruizhi, Ding Yunfei. Short-term wind power prediction based on LSGAN and GA-ELM [J]. Journal of Shanghai Dianji University, 25(6): 311-317.
[5] Wang Rui, Hsu hsin-chiu, Lu Jing. Short-term wind power prediction based on variational modal decomposition and hybrid kernel limit learning machine optimized by Sparrow search algorithm [J]. Information and control, 2023, 52(4): 444-454.
[6] Chang Yufang, Yang Zixiao, Pan Feng, etc. Ultra-short term wind power forecasting method based on CEEMDAN-PE-WPD and multi-objective optimization [J]. Grid technology, 2023, 47(12): 5015-5026.
[7] Shi Hong Tao, Li Xibin, Ding Mao Sheng, etc. Egru-am short-term wind power forecasting method based on adaptive error correction. [J]. Electrical measurement and instrumentation, 2021, 58(1): 142-146.
[8] Zhang Ziqi, Ding Yunfei, Yang Jin. Ultra-short term wind power forecasting based on SSA-MKSVR-CV [J]. Journal of the Shanghai Dianji University, 2023, 26(3): 125-129.
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