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Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

Author

Listed:
  • Joanna Kajewska-Szkudlarek

    (Institute of Environmental Engineering, Wrocław University of Environmental and Life Sciences, Grunwaldzki Square 24, 50-363 Wrocław, Poland)

  • Jan Bylicki

    (Warsaw University of Life Sciences SGGW, Nowoursynowska 166, 02-787 Warsaw, Poland)

  • Justyna Stańczyk

    (Institute of Environmental Engineering, Wrocław University of Environmental and Life Sciences, Grunwaldzki Square 24, 50-363 Wrocław, Poland)

  • Paweł Licznar

    (Faculty of Environmental Engineering, Wrocław University of Science and Technology, Grunwaldzki Square 9, 50-377 Wrocław, Poland)

Abstract

An accurate air-temperature prediction can provide the energy consumption and system load in advance, both of which are crucial in HVAC (heating, ventilation, air conditioning) system operation optimisation as a way of reducing energy losses, operating costs, as well as pollution and dust emissions while maintaining residents’ thermal comfort. This article presents the results of an outdoor air-temperature time-series prediction for a multifamily building with the use of artificial neural networks during the heating period (October–May). The aim of the research was to analyse in detail the created neural models with a view to select the best combination of predictors and the optimal number of neurons in a hidden layer. To meet that task, the Akaike information criterion was used. The most accurate results were obtained by MLP 3-3-1 (r = 0.986, AIC = 1300.098, SSE = 4467.109), with the ambient-air-temperature time series observed 1, 2, and 24 h before the prognostic temperature as predictors. The AIC proved to be a useful method for the optimum model selection in a machine-learning modelling. What is more, neural network models provide the most accurate prediction, when compared with LR and SVR. Additionally, the obtained temperature predictions were used in HVAC applications: entering-water temperature and indoor temperature modelling.

Suggested Citation

  • Joanna Kajewska-Szkudlarek & Jan Bylicki & Justyna Stańczyk & Paweł Licznar, 2021. "Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems," Energies, MDPI, vol. 14(22), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7512-:d:676103
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    References listed on IDEAS

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