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Short-term prediction of electric demand in building sector via hybrid support vector regression

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  • Chen, Yibo
  • Tan, Hongwei

Abstract

Reliable and highly-generalized prediction models of short-term electric demand are urgently needed for the building sector, as the crucial basis of sophisticated building energy management. Advances in metering technologies and machine learning methods provide both opportunities and challenges for modified approaches. With multi-resolution wavelet decomposition (MWD) as a preprocessing from the point of view of signal analysis, the hybrid support vector regression (SVR) model was applied in two case study buildings to predict the hourly electric demand intensity. Taking ten-dimensional parameters of 29 workdays as the training sample, this model was carried out in a mall and a hotel, the consumed electric demand sequences of which represented the stationary and non-stationary series respectively.

Suggested Citation

  • Chen, Yibo & Tan, Hongwei, 2017. "Short-term prediction of electric demand in building sector via hybrid support vector regression," Applied Energy, Elsevier, vol. 204(C), pages 1363-1374.
    • Handle: RePEc:eee:appene:v:204:y:2017:i:c:p:1363-1374
    DOI: 10.1016/j.apenergy.2017.03.070
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    References listed on IDEAS

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