基于FOA-Elman神經(jīng)網(wǎng)絡(luò)的光伏發(fā)電功率預(yù)測模型
李蕓，李萍，麻利新
（寧夏大學(xué) 物理與電子電氣工程學(xué)院，寧夏 銀川 750021）
摘 要：光伏發(fā)電功率對光伏發(fā)電的可靠性起著決定性作用。針對Elman神經(jīng)網(wǎng)絡(luò)收斂速度慢、訓(xùn)練時間較長的問題，利用果蠅算法（FOA）來優(yōu)化Elman神經(jīng)網(wǎng)絡(luò)的權(quán)值和閾值，從而提高運(yùn)行效率。建立了基于FOA-Elman神經(jīng)網(wǎng)絡(luò)的光伏發(fā)電功率預(yù)測模型，并給出了算法設(shè)計(jì)及編碼方案。仿真實(shí)驗(yàn)結(jié)果表明，F(xiàn)OA-Elman模型預(yù)測精度比傳統(tǒng)Elman神經(jīng)網(wǎng)絡(luò)模型預(yù)測精度高，更適合于光伏發(fā)電功率預(yù)測。
關(guān)鍵詞：光伏發(fā)電；功率預(yù)測；果蠅算法；Elman 神經(jīng)網(wǎng)絡(luò)；預(yù)測精度
中圖分類號：TM615 文獻(xiàn)標(biāo)識碼：A 文章編號：1007-3175(2019)12-0001-04
Prediction Model of Photovoltaic Power Generation Based on FOA-Elman Neural Network
LI Yun, LI Ping, MA Li-xin
（School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, China）
Abstract: The photovoltaic power generation plays an important role in the reliability of photovoltaic power system.Aiming at the slow convergence speed and long training time of Elman neural network, this paper used the fruit fly optimization algorithm (FOA) to optimize the weights and thresholds of Elman neural network to improve the operation efficiency. A photovoltaic power prediction model based on FOAElman neural network was established, and the algorithm, design and coding scheme were given. The simulation results show that the prediction accuracy of FOA-Elman model is higher than that of traditional Elman neural network model, more suitable for the photovoltaic power prediction.
Key words: photovoltaic power generation; power prediction; fruit fly optimization algorithm; Elman neural network; prediction accuracy
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