CAJ | 학술논문

为了实现对大规模并网型光伏电站调度，分析影响光伏出力的气象相关因素，以光照强度和温度作为输入量，分季节建立了一种基于LVQ-GA-BP神经网络预测系统。通过LVQ（Learning Vector Quantization）神经网络对样本进行分类，将分类后的样本训练，得出基于BP神经网络光伏电站出力预测系统，从而提高光伏预测精度。采用GA算法替代传统的学习算法优化BP神经网络的权值和阀值，提高预测网络的训练速度。将建立的LVQ-GA-BP预测系统与传统系统进行了比较和分析，结果表明：该方法的建立，不仅提高了光伏出力的预测精度，而且还提高了BP神经网络的训练速度，具有潜在的工程应用价值。
위료실현대대규모병망형광복전참조도，분석영향광복출력적기상상관인소，이광조강도화온도작위수입량，분계절건립료일충기우LVQ-GA-BP신경망락예측계통。통과LVQ（Learning Vector Quantization）신경망락대양본진행분류，장분류후적양본훈련，득출기우BP신경망락광복전참출력예측계통，종이제고광복예측정도。채용GA산법체대전통적학습산법우화BP신경망락적권치화벌치，제고예측망락적훈련속도。장건립적LVQ-GA-BP예측계통여전통계통진행료비교화분석，결과표명：해방법적건립，불부제고료광복출력적예측정도，이차환제고료BP신경망락적훈련속도，구유잠재적공정응용개치。
In order to schedule the large-scale grid-connected PV generation, the weather-related factors of PV power output are analyzed. PV short-term power output forecasting system is proposed in accordance with the four seasons based on LVQ-GA-BP neural network, whose input parameters are light intensity and temperature. The samples about PV output and weather-related factors are classified by learning vector quantization (LVQ) neural network. Then, the classified samples are trained to get the PV short-term output forecasting system based on GA-BP neural network in purpose of increasing forecasting accuracy. Secondly, we propose GA algorithm is an alternative to traditional learning algorithm to optimize BP neural network weights and thresholds, improving forecasting network training speed. At last, the LVQ-GA-BP forecasting system and the traditional forecasting system are compared and analyzed. The results show that the proposed forecasting system not only improve the PV output forecasting accuracy, but also raise the BP neural network training speed, which has potential value in engineering applications.