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Creating localised near future weather data for predicting the performance of buildings in the UK

Du, Hu ORCID:, Jones, Phillip John ORCID: and Lannon, Simon Charles ORCID: 2016. Creating localised near future weather data for predicting the performance of buildings in the UK. Presented at: The 12th REHVA World Congress CLIMA 2016, Aalborg, Denmark, 22-25 May 2016. Published in: Heiselberg, P. K. ed. CLIMA 2016 - 12th REHVA World Congress, 22-25 May 2016, Aalborg, Denmark. , vol.9 Aalborg: Aalborg University, Department of Civil Engineering, p. 537.

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Predicting near future building performance is an important aspect of demand response control strategies, which was highlighted in the Horizon 2020 societal challenges work programme by the European Commission. Past research shows that the optimisation of energy management with weather forecasting can generate 15-30% savings in most cases. Therefore, it is crucial to develop a method of gathering reliable weather forecast data and applying the forecast data into building performance simulation or building energy management system. Since 2011, the Meteorological Office (Met Office) in the United Kingdom released 3-hourly site-specific forecast data feeds for nearly 6,000 locations in the UK through the Met Office DataPoint in a format that is suitable for web application developers. This provids a great opportunity for building performance simulation professionals to re-use Met Office data for predicting near future building performance. With the freely available high frequency weather forecast data, the aim of this paper is to create localised near future weather data for predicting the performance of buildings in the UK. The project is built on authors’ previous research experience in future weather data and building performance modelling. The authors developed a method of automatically gathering forecast weather data for nearly 6,000 locations in the UK. Through the detailed comparison between forecast and observation, authors are confident that the 24-hour forecasts are very close to observations. Therefore, the high resolution forecasts for the significant large number of locations can be used to create ‘real’ weather data for locations that do not have weather stations.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Architecture
Subjects: T Technology > TH Building construction
Additional Information: Paper 537
Publisher: Aalborg University, Department of Civil Engineering
ISBN: 8791606349
Funders: Welsh Government
Date of First Compliant Deposit: 5 April 2016
Last Modified: 06 May 2023 01:21

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