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

• Published in: Energies (Basel) Vol. 15; no. 14; p. 5084
• Main Authors: Metsä-Eerola, Iivo, Pulkkinen, Jukka, Niemitalo, Olli, Koskela, Olli
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2022
• Subjects: Air conditioning; Algorithms; Bayesian analysis; Building automation; Buildings; Carbon dioxide; Climate change; District heating; Emissions; Energy conservation; Energy consumption; Energy efficiency; Green buildings; Greenhouse gases; Heat; Heating; Humidity; HVAC; Indoor air quality; Industrial applications; Internet of Things; machine learning; Mathematical models; Modelling; Neural networks; Optimization; Performance prediction; Recurrent neural networks; Software; Ventilation; Water temperature; Finland
• DOI: 10.3390/en15145084
• ISSN: 1996-1073

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.