SHORT-TERM WIND POWER PRIDICTION BASED ON NICHE GENETIC ALGORITHM AND RADIAL BASIS SURROGATE MODEL

Liu Peihan; Yin Cui; Jia Na; Fan Xiaochao; Yang Qingbin

doi:10.19912/j.0254-0096.tynxb.2023-0615

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (8) : 554-564. DOI: 10.19912/j.0254-0096.tynxb.2023-0615

SHORT-TERM WIND POWER PRIDICTION BASED ON NICHE GENETIC ALGORITHM AND RADIAL BASIS SURROGATE MODEL

Liu Peihan¹, Yin Cui¹, Jia Na², Fan Xiaochao¹, Yang Qingbin³

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Abstract

In order to improve the forecast accuracy of short-term power and promote more wind power being consumed by power grid,this paper establishes a rolling short-term （0-72 h） wind power prediction model based on dominant feature influencing factors and niche genetic algorithm improved neural network. Firstly, the niche technology based on penalty function and crowding mechanism is used to improve the traditional basic genetic algorithm. With the radial basis surrogate model （RBF） as the modeling basis, the improved genetic algorithm is used to optimize the connection weights of the RBF model with the minimum back-propagation error as the objective function. With its optimization ability, the optimal weights are obtained to achieve the improvement and secondary training of the RBF network; Then, based on dominant meteorological factors and an improved RBF model, an N-SGA-RBF wind power output prediction model is established to predict the output power of the wind farm for 0-72 hours for three consecutive days; Finally, the N-SGA-RBF model, RBF model, and BP model are compared for trend changes in prediction results, absolute and relative error distribution at each sampling point, accuracy and qualification rate of power generation prediction. An example verification analysis is conducted using measured data from a wind farm in eastern Xinjiang. The simulation results show that the established prediction model has high accuracy.

Key words

wind power / forecasting / radial basis surrogate model / niche genetic algorithm / intelligent optimization

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Liu Peihan, Yin Cui, Jia Na, Fan Xiaochao, Yang Qingbin. SHORT-TERM WIND POWER PRIDICTION BASED ON NICHE GENETIC ALGORITHM AND RADIAL BASIS SURROGATE MODEL[J]. Acta Energiae Solaris Sinica. 2024, 45(8): 554-564 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0615

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