On-Site temperature and irradiance forecast tuning for improved load prediction in buildings

IF 6.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Amine Jarraya , Tim Diller , Himanshu Nagpal , Anton Soppelsa , Federico Trentin , Gregor Henze , Roberto Fedrizzi
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引用次数: 0

Abstract

Building management systems (BMSs) with predictive control strategies rely on accurate weather forecasts to optimise heating and cooling operations. These strategies depend on precise climatic inputs to adjust system operations dynamically. Typically, weather forecast data is sourced from the internet and is generated by numerical weather prediction (NWP) models using advanced mathematical simulations. However, these models fail to account for localised nano-climatic variations, such as significant temperature and irradiance differences between the north and south sides of a building or the actual environmental conditions around the on-site sensors. These nano-climatic effects directly influence the calculation of the future thermal load of the building, which is crucial for predictive control approaches. To address this challenge, we propose a hybrid methodology that integrates NWP forecasts with local measurements from on-site sensors, improving NWP forecast accuracy. Our approach employs Inverse Distance Weighting (IDW) to interpolate NWP outputs to a specific geographical position and applies exponential smoothing for further finetuning by using historical error patterns. This methodology enhances the predictive accuracy of temperature and irradiance forecasts, achieving reductions of up to 60% to 80% in temperature errors and up to 20% to 30% in irradiance errors. Based on the finetuned weather forecast, the accuracy of building’s thermal load prediction is improved up to 86% compared to the predictions with IDW weather forecast.
现场温度和辐照度预测调谐,改善建筑物负荷预测
具有预测控制策略的楼宇管理系统(bms)依靠准确的天气预报来优化供暖和制冷操作。这些策略依赖于精确的气候输入来动态调整系统运行。通常,天气预报数据来自互联网,由数值天气预报(NWP)模型使用先进的数学模拟生成。然而,这些模型无法解释局部的纳米气候变化,例如建筑物南北两侧的显著温度和辐照度差异,或者现场传感器周围的实际环境条件。这些纳米气候效应直接影响建筑未来热负荷的计算,这对于预测控制方法至关重要。为了应对这一挑战,我们提出了一种混合方法,将NWP预测与现场传感器的本地测量相结合,提高NWP预测的准确性。我们的方法采用逆距离加权(IDW)将NWP输出内插到特定的地理位置,并通过使用历史误差模式应用指数平滑进行进一步微调。该方法提高了温度和辐照度预测的预测精度,实现了温度误差减少60%至80%,辐照度误差减少20%至30%。在此基础上,与IDW天气预报相比,建筑热负荷预测精度提高了86%。
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来源期刊
Energy and Buildings
Energy and Buildings 工程技术-工程:土木
CiteScore
12.70
自引率
11.90%
发文量
863
审稿时长
38 days
期刊介绍: An international journal devoted to investigations of energy use and efficiency in buildings Energy and Buildings is an international journal publishing articles with explicit links to energy use in buildings. The aim is to present new research results, and new proven practice aimed at reducing the energy needs of a building and improving indoor environment quality.
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