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Articles
DOI: 10.14254/1795-6889.2025.21-2.10
Submitted: 2025-10-12
Published: 2025-10-12

Operational HVAC energy load prediction: Edge-oriented forecasting models

Lodz University of Technology, Poland
Warsaw School of Economics, Poland
Lodz University of Technology, Poland
Lodz University of Technology, Poland
Lodz University of Technology, Poland
Lodz University of Technology, Poland
Lodz University of Technology, Poland
operational prediction HVAC control energy load forecast edge computing anomaly detection cybersecurity

Abstract

Contemporary office building standards require the installation of advanced HVAC systems, which, alongside industrial processes, are significantly responsible for energy consumption. While the literature describes numerous multi-parameter, deep artificial neural networks that are highly complex but poorly explainable (black box), commercial control systems must be based on simple-to-implement, easily adaptable intelligent predictive models whose reliability stems from their monitorability and explainability. The study verified several edge-oriented approaches using machine learning techniques for short- and medium-term predictions (1 day) with relatively high granularity (15 minutes). High granularity of time intervals, combined with high prediction reliability, not only provide a practical opportunity to optimize energy consumption, but also to increase the efficiency of renewable energy sources exploitation. Operational, high granularity predictions can also be used to detect anomalies and attacks, as required by modern cybersecurity rules.

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References

  1. Afzaal, H., Farooque, A. A., Esau, T. J., Schumann, A. W., Zaman, Q. U., Abbas, F., & Bos, M. (2023). Artificial neural modeling for precision agricultural water management practices. In Precision Agriculture (pp. 169-186). Academic Press. DOI: https://doi.org/10.1016/B978-0-443-18953-1.00005-2
  2. Amasyali, K., & El-Gohary, N. M. (2018). A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews, 81, 1192-1205. DOI: https://doi.org/10.1016/j.rser.2017.04.095
  3. ASHRAE (2023). ANSI/ASHRAE Standard 55: Thermal Environmental Conditions for Human Occupancy (2023). American Society of Heating, Refrigerating and Air-Conditioning Engineers
  4. Aslam, W., Andrianesis, P., & Caramanis, M. (2023). Optimal hvac energy and regulation reserve scheduling in power markets. IEEE Transactions on Sustainable Energy, 15(1), 201-214. DOI: https://doi.org/10.1109/TSTE.2023.3279060
  5. Balali, Y., Chong, A., Busch, A., & O’Keefe, S. (2023). Energy modelling and control of building heating and cooling systems with data-driven and hybrid models—A review. Renewable and Sustainable Energy Reviews, 183, 113496. DOI: https://doi.org/10.1016/j.rser.2023.113496
  6. Bertolini, M., Duttilo, P., & Lisi, F. (2025). Accounting carbon emissions from electricity generation: a review and comparison of emission factor-based methods. Applied Energy, 392, 125992. DOI: https://doi.org/10.1016/j.apenergy.2025.125992
  7. Chen, Y., Guo, M., Chen, Z., Chen, Z., & Ji, Y. (2022). Physical energy and data-driven models in building energy prediction: A review. Energy Reports, 8, 2656-2671 DOI: https://doi.org/10.1016/j.egyr.2022.01.162
  8. Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj computer science, 7, e623. DOI: https://doi.org/10.7717/peerj-cs.623
  9. Das, U. K., Tey, K. S., Seyedmahmoudian, M., Mekhilef, S., Idris, M. Y. I., Van Deventer, W., ... & Stojcevski, A. (2018). Forecasting of photovoltaic power generation and model optimization: A review. Renewable and Sustainable Energy Reviews, 81, 912-928. DOI: https://doi.org/10.1016/j.rser.2017.08.017
  10. Di Persio, L., & Fraccarolo, N. (2023). Energy consumption forecasts by gradient boosting regression trees. Mathematics, 11(5), 1068. DOI: https://doi.org/10.3390/math11051068
  11. European Commission. (2017). Commission Regulation (EU) 2017/2195 of 23 November 2017 establishing a guideline on electricity balancing. Official Journal of the European Union, L 312/6.
  12. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32017R2195
  13. Fan, C., Wang, J., Gang, W., & Li, S. (2019). Assessment of deep recurrent neural network-based strategies for short-term building energy predictions. Applied energy, 236, 700-710. DOI: https://doi.org/10.1016/j.apenergy.2018.12.004
  14. Fraisse, G., Viardot, C., Lafabrie, O., & Achard, G. (2002). Development of a simplified and accurate building model based on electrical analogy. Energy and buildings, 34(10), 1017-1031. DOI: https://doi.org/10.1016/S0378-7788(02)00019-1
  15. Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232. DOI: https://doi.org/10.1214/aos/1013203451
  16. Hlongwane, N. W. (2025). Determinants of electricity consumption in South Africa: Insights from linear and nonlinear modeling approaches. Economics, Management and Sustainability, 10(1), 6–26. https://doi.org/10.14254/jems.2025.10-1.1 DOI: https://doi.org/10.14254/jems.2025.10-1.1
  17. Ho, T. K. (1995, August). Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition (Vol. 1, pp. 278-282). IEEE. DOI: https://doi.org/10.1109/ICDAR.1995.598994
  18. International Energy Agency. (2024, March 21). Poland energy mix. https://www.iea.org/countries/poland/energy-mix
  19. Jung, J., & Broadwater, R. P. (2014). Current status and future advances for wind speed and power forecasting. Renewable and Sustainable Energy Reviews, 31, 762-777. DOI: https://doi.org/10.1016/j.rser.2013.12.054
  20. Khan, A. A., Chaudhari, O., & Chandra, R. (2024). A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation. Expert Systems with Applications, 244, 122778. DOI: https://doi.org/10.1016/j.eswa.2023.122778
  21. Kim, H., Kim, M., Kim, H., & Park, S. (2020). Decomposition analysis of CO2 emission from electricity generation: Comparison of OECD countries before and after the financial crisis. Energies, 13(14), 3522. DOI: https://doi.org/10.3390/en13143522
  22. Klyuev, R. V., Morgoev, I. D., Morgoeva, A. D., Gavrina, O. A., Martyushev, N. V., Efremenkov, E. A., & Mengxu, Q. (2022). Methods of forecasting electric energy consumption: A literature review. Energies, 15(23), 8919. DOI: https://doi.org/10.3390/en15238919
  23. Koebrich, S., Cofield, J., McCormick, G., Saraswat, I., Steinsultz, N., & Christian, P. (2025). Towards objective evaluation of the accuracy of marginal emissions factors. Renewable and Sustainable Energy Reviews, 215, 115508. DOI: https://doi.org/10.1016/j.rser.2025.115508
  24. Krutsyak, M. O. (2019). Forecasting demand on the Ukrainian electricity market using socio-economic variables. Economics, Management and Sustainability, 4(1), 46–57. https://doi.org/10.14254/jems.2019.4-1.5 DOI: https://doi.org/10.14254/jems.2019.4-1.5
  25. Kumari, S., & Muthulakshmi, P. (2024). SARIMA model: an efficient machine learning technique for weather forecasting. Procedia Computer Science, 235, 656-670. DOI: https://doi.org/10.1016/j.procs.2024.04.064
  26. Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., ... & Battaglia, P. (2023). Learning skillful medium-range global weather forecasting. Science, 382(6677), 1416-1421. DOI: https://doi.org/10.1126/science.adi2336
  27. Liu, T., Xu, C., Guo, Y., & Chen, H. (2019). A novel deep reinforcement learning based methodology for short-term HVAC system energy consumption prediction. International Journal of Refrigeration, 107, 39-51. DOI: https://doi.org/10.1016/j.ijrefrig.2019.07.018
  28. Ministerstwo Klimatu i Środowiska. (2024a). Rozporządzenie Ministra Klimatu i Środowiska z dnia 24 kwietnia 2024 w sprawie ogłoszenia jednolitego tekstu rozporządzenia Ministra Klimatu i Środowiska w sprawie sposobu kształtowania i kalkulacji taryf oraz sposobu rozliczeń w obrocie energią elektryczną [Regulation of the Minister of Climate and Environment on the announcement of the consolidated text of the regulation on the method of shaping and calculating tariffs and settlement methods in electricity trading]. Retrieved from https://www.ure.gov.pl/download/9/13359/Rozporzadzenietaryfoweelektroenergetyczne.pdf
  29. Ministerstwo Klimatu i Środowiska. (2024b). Rozporządzenie Ministra Klimatu i Środowiska z dnia 24 września 2024 w sprawie w sprawie wskaźnika emisji gazów cieplarnianych dla energii elektrycznej w 2025. Retrieved from https://isap.sejm.gov.pl/isap.nsf/download.xsp/WDU20240001439/O/D20241439.pdf
  30. Magalhães, B., Bento, P., Pombo, J., Calado, M. D. R., & Mariano, S. (2024). Short-term load forecasting based on optimized random forest and optimal feature selection. Energies, 17(8), 1926. DOI: https://doi.org/10.3390/en17081926
  31. Nicholson, S., & Heath, G. (2021). Life cycle emissions factors for electricity generation technologies (No. FY17 AOP 4.6. 8.3). National Renewable Energy Laboratory-Data (NREL-DATA), Golden, CO (United States); National Renewable Energy Lab.(NREL), Golden, CO (United States).
  32. Olu-Ajayi, R., Alaka, H., Owolabi, H., Akanbi, L., & Ganiyu, S. (2023). Data-Driven Tools for Building Energy Consumption Prediction: A Review. Energies, 16(6), 2574. DOI: https://doi.org/10.3390/en16062574
  33. Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera‐Arroita, G., ... & Dormann, C. F. (2017). Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40(8), 913-929. DOI: https://doi.org/10.1111/ecog.02881
  34. Selmic, R. R., & Lewis, F. L. (2002). Neural-network approximation of piecewise continuous functions: application to friction compensation. IEEE transactions on neural networks, 13(3), 745-751. DOI: https://doi.org/10.1109/TNN.2002.1000141
  35. Sendra-Arranz, R., & Gutiérrez, A. (2020). A long short-term memory artificial neural network to predict daily HVAC consumption in buildings. Energy and Buildings, 216, 109952. DOI: https://doi.org/10.1016/j.enbuild.2020.109952
  36. Song, X., Deng, L., Wang, H., Zhang, Y., He, Y., & Cao, W. (2024). Deep learning-based time series forecasting. Artificial Intelligence Review, 58(1), 23. DOI: https://doi.org/10.1007/s10462-024-10989-8
  37. Szostek, K., Mazur, D., Drałus, G., & Kusznier, J. (2024). Analysis of the Effectiveness of ARIMA, SARIMA, and SVR Models in Time Series Forecasting: A Case Study of Wind Farm Energy Production. Energies (19961073), 17(19). DOI: https://doi.org/10.3390/en17194803
  38. Urząd Regulacji Energetyki. (2024, March 4). 2024. https://www.ure.gov.pl/pl/energia-elektryczna/charakterystyka-rynku/12744,2024.html
  39. Urząd Regulacji Energetyki. (2022, February 21). Jak optymalizować koszty energii elektrycznej w sektorze publicznym? https://www.ure.gov.pl/pl/urzad/informacje-ogolne/aktualnosci/9851,Jak-optymalizowac-koszty-energii-elektrycznej-w-sektorze-publicznym.html
  40. Verbeke, S., & Audenaert, A. (2018). Thermal inertia in buildings: A review of impacts across climate and building use. Renewable and sustainable energy reviews, 82, 2300-2318. DOI: https://doi.org/10.1016/j.rser.2017.08.083
  41. Walczak, J. (2025). Evidence-based Methodological Framework for Machine Learning Studies. Authorea Preprints. DOI: https://doi.org/10.36227/techrxiv.174803567.76355063/v1
  42. Wang, Z., & Srinivasan, R. S. (2017). A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renewable and sustainable energy reviews, 75, 796-808. DOI: https://doi.org/10.1016/j.rser.2016.10.079
  43. Xu, X., & Wang, S. (2008). A simplified dynamic model for existing buildings using CTF and thermal network models. International Journal of Thermal Sciences, 47(9), 1249-1262. DOI: https://doi.org/10.1016/j.ijthermalsci.2007.10.011
  44. Zhang, X., Pipattanasomporn, M., Chen, T., & Rahman, S. (2019). An IoT-based thermal model learning framework for smart buildings. IEEE Internet of Things Journal, 7(1), 518-527. DOI: https://doi.org/10.1109/JIOT.2019.2951106

How to Cite

Tomczyk, A., Wojciechowski, W., Walczak, J., Lipiński, P., Wosiak, A., Morawski, M., & Napieralski, P. (2025). Operational HVAC energy load prediction: Edge-oriented forecasting models. Human Technology, 21(2), 431–447. https://doi.org/10.14254/1795-6889.2025.21-2.10