CAJ | 학술논문

本文中利用时间、大气干球温度、环境温度、太阳辐射强度、t-1时刻的系统冷负荷和t-24时刻的系统冷负荷作为输入变量进行建模预测。本文充分利用遗传算法的全局搜索的优势以及BP神经网络精于局部精确搜索的特性,采用遗传算法（Genetic Algorithm，GA）优化神经网络算法各因子的初始权重,充分达到了两种智能算法有机结合，达到了优势互补的目的。结果表明，遗传算法和神经网络的有效结合显著提高了预测精度，证明了这种方法的有效性和可靠性，为指导动态冰蓄冷空调系统负荷预测和提高预测精度提供了新途径。
본문중이용시간、대기간구온도、배경온도、태양복사강도、t-1시각적계통랭부하화t-24시각적계통랭부하작위수입변량진행건모예측。본문충분이용유전산법적전국수색적우세이급BP신경망락정우국부정학수색적특성,채용유전산법（Genetic Algorithm，GA）우화신경망락산법각인자적초시권중,충분체도료량충지능산법유궤결합，체도료우세호보적목적。결과표명，유전산법화신경망락적유효결합현저제고료예측정도，증명료저충방법적유효성화가고성，위지도동태빙축랭공조계통부하예측화제고예측정도제공료신도경。
The prediction model of air conditioning load was established that selected environment factors, according to the magnitude of the effect of time,wet bulb temperature,relative atmospheric temperature, Solar radiation intensity,and system cooling load at t-1and t-24 as the input variable.The model combines genetic algorithm(GA)based on global optimization with back propagation(BP)based on gradient descent in the paper make the linking weights of networks self-adaptive evolution in constantly iterative process. The results show that GA-BP could significantly increased model computational speed and accuracy,proved the effectiveness and reliability of this method provides a new way for guiding the prediction of air conditioning load and improve model accuracy.