A grey-box model of next-day building thermal load prediction for energy-efficient control

Qiang Zhou, Shengwei Wang, Xinhua Xu, Fu Xiao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

81 Citations (Scopus)

Abstract

Accurate building thermal load prediction is essential to many building energy control strategies. To get reliable prediction of the hourly building load of the next day, air temperature/relative humidity and solar radiation prediction modules are integrated with a grey-box model. The regressive solar radiation module predicts the solar radiation using the forecasted cloud amount, sky condition and extreme temperatures from on-line weather stations, while the forecasted sky condition is used to correct the cloud amount forecast. The temperature/relative humidity prediction module uses a dynamic grey model (GM), which is specialized in the grey system with incomplete information. Both weather prediction modules are integrated into a building thermal load model for the on-line prediction of the building thermal load in the next day. The validation of both weather prediction modules and the on-line building thermal load prediction model are presented.
Original languageEnglish
Pages (from-to)1418-1431
Number of pages14
JournalInternational Journal of Energy Research
Volume32
Issue number15
DOIs
Publication statusPublished - 1 Dec 2008

Keywords

  • Building load
  • Grey-box model
  • Load prediction
  • Weather prediction

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Nuclear Energy and Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology

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