A relevant data selection method for energy consumption prediction of low energy building based on support vector machine

S. Paudel, M. Elmitri, S. Couturier, H.P. Nguyen, I.G. Kamphuis, B. Lacarrière, O. Le Corre (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

44 Citations (Scopus)
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Abstract

Low energy buildings (LEBs) are being considered as a promising solution for the built environment to satisfy high-energy efficiency standards. The technology is based on lowering the overall heat transmission coefficient value (U-value) of the buildings envelope and increasing a heat capacity thus creating a higher thermal inertia. However, LEB introduces a large time constant compared to conventional building due to which it slows the rate of heat transfer between interior of building and outdoor environment and alters the indoor climate regardless of sudden changes in climatic conditions. Therefore, it is challenging to estimate and predict thermal energy demand for such LEBs.

This work focuses on artificial intelligence (AI) model to predict energy consumption of LEB. Two kinds of AI modeling approaches: “all data” and “relevant data” are considered. The “all data” uses all available training data and “relevant data” uses a small representative day dataset and addresses the complexity of building non-linear dynamics by introducing past day climatic impacts behavior. This extraction is based on dynamic time warping pattern recognition methods. The case study consists of a French residential LEB. The numerical results showed that “relevant data” modeling approach that relies on small representative data selection has higher accuracy (R2 = 0.98; RMSE = 3.4) than “all data” modeling approach (R2 = 0.93; RMSE = 7.1) to predict heating energy load.
Original languageEnglish
Pages (from-to)240-256
Number of pages17
JournalEnergy and Buildings
Volume138
DOIs
Publication statusPublished - 1 Mar 2017

Keywords

  • Building energy consumption
  • Low energy building
  • Online and offline learning
  • Prediction
  • Support vector machine

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