Impact of weather forecasting uncertainty on building thermal load predictions

IF 6.7 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Cong Thanh Do, Simeon N. Ingabo, Non Phichetkunbodee, Kuan-Chun Shih, Ying-Chieh Chan
{"title":"Impact of weather forecasting uncertainty on building thermal load predictions","authors":"Cong Thanh Do, Simeon N. Ingabo, Non Phichetkunbodee, Kuan-Chun Shih, Ying-Chieh Chan","doi":"10.1016/j.jobe.2025.113366","DOIUrl":null,"url":null,"abstract":"Accurate thermal load prediction is crucial for the efficient operation and control of Heating, Ventilation, and Air Conditioning (HVAC) systems. Weather forecast inaccuracies are a major source of deviation between predicted and actual building thermal loads, potentially compromising HVAC control strategies. However, the influence of these forecasting uncertainties has not been thoroughly quantified. This study conducts a statistical analysis of measured and forecasted weather data to characterize uncertainties in key parameters such as air temperature, relative humidity, solar irradiation, cloud cover, wind speed, and wind direction. The Monte Carlo method is used to evaluate the impact of each parameter's uncertainty on thermal load predictions across various scenarios, including different window orientations, thermal mass levels, shading control strategies, and weather conditions. Results show that forecast errors in solar irradiation and temperature have the greatest influence on thermal load prediction. On a hot sunny day, temperature forecast errors can lead to a 3.69 % overestimation in daily cooling demand for multi-zone spaces and 8.28 % for single-zone spaces. Solar irradiation forecast errors can result in overestimations as high as 30.93 % for multi-zone and 14.48 % for single-zone spaces. However, the influence of solar irradiation errors diminishes when shading devices are implemented.","PeriodicalId":15064,"journal":{"name":"Journal of building engineering","volume":"2 1","pages":""},"PeriodicalIF":6.7000,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of building engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.jobe.2025.113366","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Accurate thermal load prediction is crucial for the efficient operation and control of Heating, Ventilation, and Air Conditioning (HVAC) systems. Weather forecast inaccuracies are a major source of deviation between predicted and actual building thermal loads, potentially compromising HVAC control strategies. However, the influence of these forecasting uncertainties has not been thoroughly quantified. This study conducts a statistical analysis of measured and forecasted weather data to characterize uncertainties in key parameters such as air temperature, relative humidity, solar irradiation, cloud cover, wind speed, and wind direction. The Monte Carlo method is used to evaluate the impact of each parameter's uncertainty on thermal load predictions across various scenarios, including different window orientations, thermal mass levels, shading control strategies, and weather conditions. Results show that forecast errors in solar irradiation and temperature have the greatest influence on thermal load prediction. On a hot sunny day, temperature forecast errors can lead to a 3.69 % overestimation in daily cooling demand for multi-zone spaces and 8.28 % for single-zone spaces. Solar irradiation forecast errors can result in overestimations as high as 30.93 % for multi-zone and 14.48 % for single-zone spaces. However, the influence of solar irradiation errors diminishes when shading devices are implemented.
天气预报不确定性对建筑热负荷预测的影响
准确的热负荷预测对于暖通空调(HVAC)系统的有效运行和控制至关重要。天气预报的不准确是预测和实际建筑热负荷之间偏差的主要来源,可能会影响HVAC控制策略。然而,这些预测不确定性的影响还没有被彻底量化。本研究对实测和预报的天气数据进行统计分析,以表征气温、相对湿度、太阳辐照度、云量、风速和风向等关键参数的不确定性。蒙特卡罗方法用于评估各种情况下每个参数的不确定性对热负荷预测的影响,包括不同的窗户方向、热质量水平、遮阳控制策略和天气条件。结果表明,太阳辐照和温度的预测误差对热负荷预测影响最大。在炎热的晴天,温度预报误差会导致多区域空间的日制冷需求高估3.69%,单区域空间的日制冷需求高估8.28%。太阳辐射预测误差可导致多区和单区空间的高估高达30.93%和14.48%。然而,当遮阳装置实施时,太阳辐照误差的影响减弱。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of building engineering
Journal of building engineering Engineering-Civil and Structural Engineering
CiteScore
10.00
自引率
12.50%
发文量
1901
审稿时长
35 days
期刊介绍: The Journal of Building Engineering is an interdisciplinary journal that covers all aspects of science and technology concerned with the whole life cycle of the built environment; from the design phase through to construction, operation, performance, maintenance and its deterioration.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信