摘要
地铁站人流量变化对通风空调系统节能运行有重要影响,为了提高地铁站人员冷负荷预测的准确度,采用遗传算法优化BP神经网络(GA-BP)的方法建立地铁站人流量预测模型,并对地铁站人员冷负荷进行动态计算。通过引入遗传算法,优化BP神经网络的初始权值和阈值,提高了BP神经网络的非线性学习能力。利用地铁站的实际人员冷负荷对模型进行验证,并与传统BP神经网络方法的预测结果进行比较。结果表明,本方法有效地提高了BP神经网络的非线性学习能力和地铁站逐时人员冷负荷预测的准确性和稳定性。与传统BP神经网络方法相比,GA-BP模型的日人员冷负荷预测平均误差降低10%左右,日逐时人员冷负荷预测拟合相关系数值提高了0.1。
The passenger flows of the metro station have significant impact on the energy-saving operation of the air-conditioning system.In order to improve the prediction accuracy of the personnel cooling load in the subway stations,a prediction model was proposed in this paper using the BP neural network(GA-BP)method optimized by the genetic algorithm,and the personnel cooling load of the subway stations was calculated dynamically.The initial weights and thresholds of BP neural network were optimized by using the genetic algorithm,and the nonlinear learning ability of BP neural network was improved.The model was validated by the actual operation data,and the simulated results were compared with the prediction results of the traditional BP neural network method.The results show that the proposed method can effectively improve the nonlinear learning ability of BP neural network and the accuracy and stability of the hourly personnel cooling load prediction.By comparing with the traditional BP neural network method,the average daily personnel cooling load prediction error of the GA-BP model is reduced by at least 10%,and the fitting correlation coefficient value of daily hourly personnel cooling load prediction is increased by at least 0.1.
作者
杨福
王衍金
江战红
Yang Fu;Wang Yanjin;Jiang Zhanhong(School of Civil Engineering and Architecture,East China Jiaotong University,Nanchang 330013,China;Nanchang Railway Transit Group Co.,Ltd.,Nanchang 330038,China)
出处
《华东交通大学学报》
2021年第2期44-50,共7页
Journal of East China Jiaotong University
基金
国家自然科学基金项目(52068021)
江西省自然科学基金项目(20202BABL204060)
江西省教育厅科技项目(GJJ190301)。
