On Hourly Forecasting Heating Energy Consumption of HVAC with Recurrent Neural Networks

Iivo Metsä-Eerola, Jukka Pulkkinen, Olli Niemitalo and Olli Koskela
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Iivo Metsä-Eerola: HAMK Smart Research Unit, Häme University of Applied Sciences, P.O. Box 230, 13101 Hämeenlinna, Finland
Jukka Pulkkinen: HAMK Smart Research Unit, Häme University of Applied Sciences, P.O. Box 230, 13101 Hämeenlinna, Finland
Olli Niemitalo: HAMK Smart Research Unit, Häme University of Applied Sciences, P.O. Box 230, 13101 Hämeenlinna, Finland
Olli Koskela: HAMK Smart Research Unit, Häme University of Applied Sciences, P.O. Box 230, 13101 Hämeenlinna, Finland

Energies, 2022, vol. 15, issue 14, 1-20

Abstract: Optimizing the heating, ventilation, and air conditioning (HVAC) system to minimize district heating usage in large groups of managed buildings is of the utmost important, and it requires a machine learning (ML) model to predict the energy consumption. An industrial use case to reach large building groups is restricted to using normal operational data in the modeling, and this is one reason for the low utilization of ML in HVAC optimization. We present a methodology to select the best-fitting ML model on the basis of both Bayesian optimization of black-box models for defining hyperparameters and a fivefold cross-validation for the assessment of each model’s predictive performance. The methodology was tested in one case study using normal operational data, and the model was applied to analyze the energy savings in two different practical scenarios. The software for the modeling is published on GitHub. The results were promising in terms of predicting the energy consumption, and one of the scenarios also showed energy saving potential. According to our research, the GitHub software for the modeling is a good candidate for predicting the energy consumption in large building groups, but further research is needed to explore its scalability for several buildings.

Keywords: HVAC; district heating; machine learning; recurrent neural networks; energy efficiency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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