第38卷第2期 2016年4月
土木建筑与环境工程
Journal of Civil,Architectural &. Environmental Engineering
doi:10. 11835/j.issn. 1674-4764. 2016. 02. 014
大型商场建筑夏季冷负荷动态预测模型
李慧“，段培永，刘凤英
（山东建筑大学a.可再生能源建筑利用技术教育部重点实验室， b.山东省可再生能源建筑应用技术重点实验室，济南250101）
Vol. 38 No. 2 Apr.2016
摘要：夏季建筑冷负荷的正确预测是实现大型复杂中央空调优化运行、节能降耗的关键。笔者探讨了商场建筑冷负荷的主要影响因素，确定了建筑动态冷负荷预测模型的输入，提出了夏季基于新风机组供电频率的商场顾客率间接测量方法，解决了商场内顾客量难以检测的难题。还提出了 AFC-HCMAC神经网络预测模型算法+实现了大型商场建筑冷负荷的动态预测。仿真结果表明：顾客率在商场冷负荷预测中占有重要地位，在冷负荷预测模型中增加商场顾客率可显著提高预测精度；AFC-HCMAC神经网络预测算法与传统的HCMAC神经网络算法比较，可有效降低神经网络节点数，提高预测精度。
关键词：冷负荷；动态预测；模糊聚类；数据
中图分类号：TU111.3
文献标志码：A
文章编号：1674-4764(2016)02-0104-07
Prediction model of dynamic cooling load for shopping mall
building in summer
Li Hui",Duan Peiyong',Liu Fengying
(a. Key Laboratory of Renewable Energy Technologies for Buildings, Ministry of Education, b. Shandong Key
Laboratory of Renewable Energy Technologies for Buildings, Shandong Jianzhu University, Jinan 2501ol, P. R. China) Abstract: The accurate energy consumption perdition for building is critical to improve the energy efficient of the operation of the operation of large-scale central air conditioning system in summer. Firstly+ the influencing factors of cooling load were identified to determine the inputs of cooling load predition model. Then the indirect measurement method was proposed to obtain the shopper rate based on the supply frequencies of new wind-8units to identify the custom number in summer. Last, an AFC-HCMAC neural network algorithm is proposed to for dynamic cooling load prediction. The results show that compared with the traditional HCMAC algorithm, the proposed AFC-HCMAC algorithm can effectively reduce the neural network nodes and improve the prediction accuracy. The shoppers rate plays an important role in the cooling load prediction for shopping mall. Increasing shopper rate in the inputs of prediction model can
significantly improve the prediction accuracy of dynamical cooling load forecasting for shopping mall. Keywords:cooling load; dynamical prediction; fuzzy clustering; data
收稿日期：2015-09-23
基金项目：国家自然科学基金（61374187）
作者简介：李慧（1970-），女，副教投，博士，主要从事建筑环境自动控制研究，（E-mail)lhh@sdjzu.edu.cn Received: 2015-0923
Foundation item: National Natural Science Foundation of China (No. 61374187)
Author brief:Li Hui(197o-), associate professor, PhD, main research interest: automation of building environment, (E
mail) Ihh@ sdjzu. edu. cn.