Understanding the reliability of localized near future weather data for building performance prediction in the UK

Hu Du, Phil Jones, Bobo Ng

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

Access to reliable site-specific near future weather data is crucial for forecasting temporally-dynamic building energy demand and consumption, and determining the state of on-site renewable energy generation. Often there is a missing link between weather forecast providers and building energy management systems. This short paper discusses the potential to conduct building performance modelling using localized high resolution weather forecast freely available from the United Kingdom Met Office DataPoint service. It creates a great opportunity for building performance simulation professionals and building energy managers to re-use site-specific high resolution weather forecast data to predict near future building performance at both individual building and city scale. In this paper, authors have developed a framework of forecasting near future building performance and a Matlab script to automatically gather observed weather data from 140 weather stations and weather forecasts for nearly 6,000 locations in the UK. To understand the reliability of weather forecast, three-hourly forecasts of temperature, relative humidity, wind speed and wind direction are compared with observations from weather stations. This provides evidences to use the next 24-hour forecast to predict dynamic building energy demand and consumption, and determine the on-site renewable energy generation output. Because of the high accuracy of forecast, the rolling forecast can be recorded on daily basis to construct weather files for locations that do not have weather stations. This will increase current 14 locations of the CIBSE weather data to nearly 6,000 locations covering population centers, sporting venues and tourist attractions.
Original languageEnglish
Title of host publication2016 IEEE International Smart Cities Conference (ISC2)
PublisherIEEE
Pages529-532
ISBN (Print)978-1-5090-1845-1
DOIs
Publication statusE-pub ahead of print - 3 Oct 2016

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