Nowcasting methods for optimising building performance

Du, Hu ORCID: https://orcid.org/0000-0002-1637-0626, Bandera, Carlos Fernández and Chen, Lei 2019. Nowcasting methods for optimising building performance. Presented at: Building Simulation 2019: 16th Conference of International Building Performance Simulation Association, Rome, Italy, 2-4 September 2019. -.

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Abstract

In meteorology term, nowcasting is weather forecasting for the next few minutes to six hours using all immediately available weather data. It is a relatively new subject, which often involves remote sensing, numerical weather prediction models, and advanced data communication infrastructure. High-quality weather nowcasting is crucial for optimising building performance in the near future. A range of nowcasting techniques has been used for such purposes. It includes statistical, machine learning, Numerical Weather Prediction (NWP), top-down and bottom-up approaches. This paper firstly reviews the advantages and disadvantages of common nowcasting methods with the focus on solar radiation nowcasting. Based on the review, popular methods have been classified into five categories. Authors then investigated further the nowcasting data provided by weather Application Programming Interfaces (APIs) that is backed by Numerical Weather Prediction. This is due to its large-scale application potential and the significances in the most recent update on solar radiation nowcast. Secondly, the paper explores the implications of applying weather nowcasting to dynamic building simulations, most importantly, examining its impact on the accuracy of indoor temperature prediction for free float buildings, heating load prediction and heating energy for heated buildings. The study used three buildings from BESTEST ANSI/ASHRAE Standard 140-2014 as the case studies. The results show that the most recent update of weather API includes meaningful solar radiation prediction. If the building does not have a large south facing glazing, the indoor temperature and heating load predictions from dynamic models are reasonably accurate.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Architecture Energy Systems Research Institute (ESURI)
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TH Building construction
Funders:	Welsh Government, European Commission
Date of First Compliant Deposit:	12 July 2019
Date of Acceptance:	26 June 2019
Last Modified:	06 May 2023 01:19
URI:	https://orca.cardiff.ac.uk/id/eprint/123759

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