reviewEnergy and BuildingsApr 30, 2020Closed access

A review of the-state-of-the-art in data-driven approaches for building energy prediction

YSYing SunFHFariborz HaghighatBCBenjamin C. M. Fung

Concordia University · McGill University

Indexed incrossref

Abstract

No abstract available for this paper.

Citation impact

486
total citations
FWCI
23.20
Percentile
100%
References
160
Citations per year

Authors

3

Topics & keywords

Topics
Keywords
  • Predictive modelling
  • Process (computing)
  • Computer science
  • Data-driven
  • Energy (signal processing)
  • Feature (linguistics)
  • Property (philosophy)
  • Data mining
UN Sustainable Development Goals
  • Affordable and clean energy
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