IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i22p7512-d676103.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/22/7512/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/22/7512/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christos Manasis & Nicholas Assimakis & Vasilis Vikias & Aphrodite Ktena & Tassos Stamatelos, 2020. "Power Generation Prediction of an Open Cycle Gas Turbine Using Kalman Filter," Energies, MDPI, vol. 13(24), pages 1-15, December.
    2. Eva Lucas Segarra & Hu Du & Germán Ramos Ruiz & Carlos Fernández Bandera, 2019. "Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models," Energies, MDPI, vol. 12(7), pages 1-16, April.
    3. Lara Ramadan & Isam Shahrour & Hussein Mroueh & Fadi Hage Chehade, 2021. "Use of Machine Learning Methods for Indoor Temperature Forecasting," Future Internet, MDPI, vol. 13(10), pages 1-18, September.
    4. Massaoudi, Mohamed & Refaat, Shady S. & Chihi, Ines & Trabelsi, Mohamed & Oueslati, Fakhreddine S. & Abu-Rub, Haitham, 2021. "A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting," Energy, Elsevier, vol. 214(C).
    5. Naoki Futawatari & Yosuke Udagawa & Taro Mori & Hirofumi Hayama, 2020. "Improving Prediction Accuracy Concerning the Thermal Environment of a Data Center by Using Design of Experiments," Energies, MDPI, vol. 13(18), pages 1-21, September.
    6. Jaemin Kim & Yujin Nam, 2020. "Development of the Performance Prediction Equation for a Modular Ground Heat Exchanger," Energies, MDPI, vol. 13(22), pages 1-13, November.
    7. Jing Zhao & Yaoqi Duan & Xiaojuan Liu, 2018. "Uncertainty Analysis of Weather Forecast Data for Cooling Load Forecasting Based on the Monte Carlo Method," Energies, MDPI, vol. 11(7), pages 1-18, July.
    8. Frayssinet, Loïc & Merlier, Lucie & Kuznik, Frédéric & Hubert, Jean-Luc & Milliez, Maya & Roux, Jean-Jacques, 2018. "Modeling the heating and cooling energy demand of urban buildings at city scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2318-2327.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Joanna Piotrowska-Woroniak & Krzysztof Cieśliński & Grzegorz Woroniak & Jonas Bielskus, 2022. "The Impact of Thermo-Modernization and Forecast Regulation on the Reduction of Thermal Energy Consumption and Reduction of Pollutant Emissions into the Atmosphere on the Example of Prefabricated Build," Energies, MDPI, vol. 15(8), pages 1-32, April.
    2. Eva Lucas Segarra & Germán Ramos Ruiz & Vicente Gutiérrez González & Antonis Peppas & Carlos Fernández Bandera, 2020. "Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets," Sustainability, MDPI, vol. 12(17), pages 1-27, August.
    3. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    4. Andreas Lenk & Marcus Vogt & Christoph Herrmann, 2024. "An Approach to Predicting Energy Demand Within Automobile Production Using the Temporal Fusion Transformer Model," Energies, MDPI, vol. 18(1), pages 1-34, December.
    5. Deng, Song & Dong, Xia & Tao, Li & Wang, Junjie & He, Yi & Yue, Dong, 2024. "Multi-type load forecasting model based on random forest and density clustering with the influence of noise and load patterns," Energy, Elsevier, vol. 307(C).
    6. Cen, Xiao & Chen, Zengliang & Chen, Haifeng & Ding, Chen & Ding, Bo & Li, Fei & Lou, Fangwei & Zhu, Zhenyu & Zhang, Hongyu & Hong, Bingyuan, 2024. "User repurchase behavior prediction for integrated energy supply stations based on the user profiling method," Energy, Elsevier, vol. 286(C).
    7. Qinghe Zhao & Xinyi Liu & Junlong Fang, 2023. "Extreme Gradient Boosting Model for Day-Ahead STLF in National Level Power System: Estonia Case Study," Energies, MDPI, vol. 16(24), pages 1-29, December.
    8. Wuyue An & Lin Wang & Dongfeng Zhang, 2023. "Comprehensive commodity price forecasting framework using text mining methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1865-1888, November.
    9. Tharindu P. De Alwis & S. Yaser Samadi, 2024. "Stacking-based neural network for nonlinear time series analysis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(3), pages 901-924, July.
    10. Kim, Yu Jin & Entchev, Evgeuniy & Na, Sun Ik & Kang, Eun Chul & Baik, Young-Jin & Lee, Euy Joon, 2023. "Investigation of system optimization and control logic on a solar geothermal hybrid heat pump system based on integral effect test data," Energy, Elsevier, vol. 284(C).
    11. Martin Eriksson & Jan Akander & Bahram Moshfegh, 2022. "Investigating Energy Use in a City District in Nordic Climate Using Energy Signature," Energies, MDPI, vol. 15(5), pages 1-22, March.
    12. Hung Viet Nguyen & Haewon Byeon, 2023. "Prediction of Out-of-Hospital Cardiac Arrest Survival Outcomes Using a Hybrid Agnostic Explanation TabNet Model," Mathematics, MDPI, vol. 11(9), pages 1-17, April.
    13. Davoodi, Shayan & Fereydooni, Ali & Rastegar, Mohammad Ali, 2024. "Can portfolio construction considering ESG still gain high profits?," Research in International Business and Finance, Elsevier, vol. 67(PA).
    14. Nageler, P. & Schweiger, G. & Schranzhofer, H. & Mach, T. & Heimrath, R. & Hochenauer, C., 2018. "Novel method to simulate large-scale thermal city models," Energy, Elsevier, vol. 157(C), pages 633-646.
    15. Zhaosheng Zhang & Shuo Wang & Ni Lin & Zhenpo Wang & Peng Liu, 2023. "State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles Based on Regional Capacity and LGBM," Sustainability, MDPI, vol. 15(3), pages 1-20, January.
    16. Guelpa, E. & Capone, M. & Sciacovelli, A. & Vasset, N. & Baviere, R. & Verda, V., 2023. "Reduction of supply temperature in existing district heating: A review of strategies and implementations," Energy, Elsevier, vol. 262(PB).
    17. Zhang, Dongxue & Wang, Shuai & Liang, Yuqiu & Du, Zhiyuan, 2023. "A novel combined model for probabilistic load forecasting based on deep learning and improved optimizer," Energy, Elsevier, vol. 264(C).
    18. He, Yaoyao & Cao, Chaojin & Wang, Shuo & Fu, Hong, 2022. "Nonparametric probabilistic load forecasting based on quantile combination in electrical power systems," Applied Energy, Elsevier, vol. 322(C).
    19. Mohamed Trabelsi & Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Shady S. Refaat & Tingwen Huang & Fakhreddine S. Oueslati, 2022. "An Effective Hybrid Symbolic Regression–Deep Multilayer Perceptron Technique for PV Power Forecasting," Energies, MDPI, vol. 15(23), pages 1-14, November.
    20. Jiakang Wang & Hui Liu & Guangji Zheng & Ye Li & Shi Yin, 2023. "Short-Term Load Forecasting Based on Outlier Correction, Decomposition, and Ensemble Reinforcement Learning," Energies, MDPI, vol. 16(11), pages 1-16, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7512-:d:676103. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.