[1]王利珍,谭洪卫.应用蒙特卡罗模拟方法预测区域建筑负荷[J].西安建筑科技大学学报(自然科学版),2017,49(05):763-0770.[doi:10.15986/j.1006-7930.2017.05.023]
　WANG Lizhen,TAN Hongwei.Prediction of the regional building load using Monte Carlo simulation method[J].J. Xi’an Univ. of Arch. & Tech.(Natural Science Edition),2017,49(05):763-0770.[doi:10.15986/j.1006-7930.2017.05.023]
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应用蒙特卡罗模拟方法预测区域建筑负荷
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西安建筑科技大学学报（自然科学版）[ISSN:1006-7930/CN:61-1295/TU]

卷:
49
期数:
2017年05期
页码:
763-0770
栏目:
出版日期:
2017-10-28

文章信息/Info

Title:
Prediction of the regional building load using Monte Carlo simulation method
文章编号:
1006-7930(2017)05-0763-08
作者:
王利珍1; 谭洪卫2
1 上海市建筑科学研究院，上海 201108；2 同济大学 绿色建筑及新能源研究中心，上海 200092）
Author(s):
WANG Lizhen1;  TAN Hongwei2
(1. Shanghai Research Institute of Building Sciences, Shanghai 201108,China；
2. Research Center of Green Building & New Energy, Tongji University, Shanghai 200092,China)
关键词:
能源规划; 负荷预测; 蒙特卡罗模拟; 随机模型
Keywords:
regional building;  load prediction;  Monte Carlo method;  stochastic model;  uncertainty
分类号:
TU831.2
DOI:
10.15986/j.1006-7930.2017.05.023
文献标志码:
A
摘要:
目前城市区域建筑能源规划阶段单体建筑信息不完备、无法同时使用常规负荷计算软件对各建筑进行冷热电力负荷预测，本文提出采用蒙特卡罗模拟方法结合负荷计算原理预测区域建筑冷热电负荷的方法该方法首先构建适用于新区多功能建筑的冷热电负荷预测随机模型，并依据调研结果确定预测模型风险变量的特征分布，再利用蒙特卡罗随机模拟技术应用MATLAB语言编制程序可求解研究区域峰值冷热负荷概率分布、电力峰值分布和全年逐时负荷论文以某新区为例，模拟了区域建筑的负荷特性，仿真结果表明：区域建筑负荷预测随机模型可以有效地模拟新区冷热电峰值负荷的频数分布和累积概率，在典型应用场景下，研究区域峰值电力负荷95%置信度下为37 MW，峰值冷负荷为50 MW，比传统方法下降6%
Abstract:
In light of the fact that detailed building parameters during the period of regional energy planning are hard to obtain and the conventional energy consumption simulation software for load calculation cannot accurately put in the parameter values and simulate every building′s load relying only on the past experience, it is important to predict different kinds of load for the regional energy planning This paper presents a new load forecasting method based on Monte Carlo simulation methodology, which is proposed for forecasting the regional building cooling, heating and electricity load Regional building cooling, heating and electricity load prediction stochastic model (RBCHELPS model) is established followed by the risk characteristics of variable distribution in this model being determined Besides, the distribution of peak cooling load probability for regional buildings as well as the annual average hourly cooling load can also be obtained by using Monte Carlo method Furthermore, the simulation is performed according to the case study by considering the cooling, heating and electricity load characteristics of regional building With RBCHELPS model the peak load of the frequency distribution and cumulative probability of the study area be effectively calculated The simulation result shows that electrical load peak power under the confidence of 95% is about 37 MW, and the peak cooling load is 50 MW, lower than the traditional method decreased by 6%

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-12-14
修改稿日期:2017-07-15
基金项目:上海市建筑节能与绿色建筑技术创新服务平台（17DZ2292900）
第一作者:王利珍（1980-），女，工程师，博士，主要研究能源规划和绿色建筑技术.E-mail:lizhen_w@163.com
更新日期/Last Update: 2017-11-10