Research Article
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Development of a Building Simulation Model for Indoor Temperature Prediction and HVAC System Anomaly Detection

Year 2023, Volume: 7 Issue: 4, 339 - 349, 31.12.2023
https://doi.org/10.30521/jes.1251339

Abstract

In order to reduce global energy consumption, energy-efficient, green and smart buildings have to be built. In addition to the application of other energy efficiency measures, an effective management of HVAC systems is required. High quality management and control of these systems ensures optimal occupant comfort levels, proper operation, rational energy consumption, and a positive impact on the environment. This is especially important for large buildings with complex systems such as hotels. As a contribution to the creation of appropriate tools for the management and control of HVAC systems in smart buildings, this paper presents the results of the current development of a detailed dynamic simulation model based on data collected from a smart room system in a hotel in Zagreb, Croatia. The smart room system, which is integrated into the hotel's building management system, provides historical data on set and current room temperatures, room occupancy schedule, window opening, fan coil operation status, fan rotation speed, valve opening, and operating mode with a time step of 5 minutes. The simulation model based on the TRNSYS software uses a part of the available data and calculates the current internal room temperatures. A comparison of the predicted and measured temperatures at each time step showed that the deviations are within the acceptable limits. The final objectives of the model development are the identification of anomalies in the operation of the HVAC system and the optimization of its operation with the aim of reducing energy consumption.

Supporting Institution

This work was supported in part by European Regional Development Fund (ERDF) under grant agreement number KK.01.2.1.02.0303, project Adria Smart Room. TRNSYS software was provided by Croatian Science Foundation under the project HEXENER (IP-2016-06-4095).

References

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  • [21] ASHRAE Guideline 14-2014: Measurement of Energy, Demand, and Water Savings. Atlanta, Georgia, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers, 2014
Year 2023, Volume: 7 Issue: 4, 339 - 349, 31.12.2023
https://doi.org/10.30521/jes.1251339

Abstract

References

  • [1] Mariano-Hernández, D, Hernández-Callejo, L, Zorita-Lamadrid, A, Duque-Pérez, O, Santos García, F. A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis. Journal of Building Engineering 2021; 33: 101692, DOI: 10.1016/j.jobe.2020.101692
  • [2] Drgoňa, J, Arroyo, J, Cupeiro Figueroa, I, Blum, D, Arendt, K, Kim, D, Perarnau Ollé, E, Oravec, J, Wette, M, Vrabie, D, Helsen, L. All you need to know about model predictive control for buildings. Annual Reviews in Control 2020; 50: 190-232, DOI: 10.1016/j.arcontrol.2020.09.001
  • [3] Kampelis, N, Papayiannis, GI, Kolokotsa, D, Galanis, GN, Isidori, D, Cristalli, C, Yannacopoulos, AN. An integrated energy simulation model for buildings. Energies 2020, 13(5):1170, DOI: 10.3390/en13051170
  • [4] Huang, H, Chen, L, Hu, E. A neural network-based multi-zone modelling approach for predictive control system design in commercial buildings. Energy and Buildings 2015; 97: 86-97, DOI: 10.1016/j.enbuild.2015.03.045
  • [5] Rao, DMKKV, Ukil, A. Modeling of room temperature dynamics for efficient building energy management. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2020; 50(2): 717-725, DOI: 10.1109/TSMC.2017.2758766
  • [6] Afroz, Z, Shafiullah, GM, Urmee, T, Higgins, G. Modeling techniques used in building HVAC control systems: A review. Renewable and Sustainable Energy Reviews 2018; 83: 64-84, DOI: 10.1016/j.rser.2017.10.044
  • [7] Reis, AS, Vaquero, P, Dias, MF, Tavares, A, Costa, A, Fonseca, J. Residential building rehabilitation in Porto historic center: Case study analysis by using a simulation model. Energy Reports 2022; 8(3): 437-441, DOI: https://doi.org/10.1016/j.egyr.2022.01.048
  • [8] Qiu, S, Li, Z, Pang, Z, Zhang, W, Li, Z. A quick auto-calibration approach based on normative energy models. Energy and Buildings 2018; 172: 35-46, DOI: 10.1016/j.enbuild.2018.04.053
  • [9] Monetti, V, Davin, E, Fabrizio, E, André, P, Filippi, M. Calibration of building energy simulation models based on optimization: A case study. Energy Procedia 2015; 78: 2971-2976, DOI: 10.1016/j.egypro.2015.11.693
  • [10] Murphy, MD, O’Sullivan, PD, Graça, GC, O’Donovan, A. Development, calibration and validation of an internal air temperature model for a naturally ventilated nearly zero energy building: comparison of model types and calibration methods. Energies 2021; 14(4):871, DOI: 10.3390/en14040871
  • [11] Rui, GR, Bandera, CF. Validation of calibrated energy models: Common errors. Energies 2017; 10(10):1587, DOI: 10.3390/en10101587
  • [12] Ji, L, Shu, C, Hou, D, Laouadi, A, Wang, L, Lacasse, M. Predicting indoor air temperatures by calibrating building thermal model with coupled airflow networks. In: CLIMA 2022 The 14th REHVA HVAC World Congress; 22-25 May 2022: TU Delft OPEN Publishing, pp. 843-850
  • [13] Royapoor, M, Roskilly, T. Building model calibration using energy and environmental data. Energy and Buildings 2015; 94: 109-120, DOI: 10.1016/j.enbuild.2015.02.050
  • [14] O’ Donovan, A, O’ Sullivan, PD, Murphy, MD. Predicting air temperatures in a naturally ventilated nearly zero energy building: Calibration, validation, analysis and approaches. Applied Energy 2019; 250: 991-1010, DOI: 10.1016/j.apenergy.2019.04.082
  • [15] Aparicio-Fernández, C., Vivancos, J.-L., Cosar-Jorda, P., Buswell, R.A. Energy modelling and calibration of building simulations: A case study of a domestic building with natural ventilation. Energies 2019; 12, DOI: https://doi.org/10.3390/en12173360
  • [16] Figueiredo, A., Kämpf, J., Vicente, R., Oliveira, R., Silva, T. Comparison between monitored and simulated data using evolutionary algorithms: Reducing the performance gap in dynamic building simulation. Journal of Building Engineering 2018; 17: 96–106, DOI: https://doi.org/10.1016/j.jobe.2018.02.003
  • [17] Martínez-Mariño, S., Eguía-Oller, P., Granada-Álvarez, E., Erkoreka-González, A. Simulation and validation of indoor temperatures and relative humidity in multi-zone buildings under occupancy conditions using multi-objective calibration. Building and Environment 2021; 200: 107973, DOI: https://doi.org/10.1016/j.buildenv.2021.107973
  • [18] Ranade, A, Provan, G, El-Din Mady, A, O’Sullivan, D. A computationally efficient method for fault diagnosis of fan-coil unit terminals in building heating, ventilation and air conditioning systems. Journal of Building Engineering 2020; 27: 100955, DOI: 10.1016/j.jobe.2019.100955
  • [19] Parzinger, M, Hanfstaengl, L, Sigg, F, Spindler, U, Wellisch, U, Wirnsberger, M. Residual analysis of predictive modelling data for automated fault detection in building’s heating, ventilation and air conditioning systems. Sustainability 2020; 12(17):6758, DOI: 10.3390/su12176758
  • [20] Dey, M, Rana, SP, Dudley, S. A case study based approach for remote fault detection using multi-level machine learning in a smart building. Smart Cities 2020; 3(2): 401-419, DOI: 10.3390/smartcities3020021
  • [21] ASHRAE Guideline 14-2014: Measurement of Energy, Demand, and Water Savings. Atlanta, Georgia, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers, 2014
There are 21 citations in total.

Details

Primary Language English
Subjects Engineering, Mechanical Engineering
Journal Section Research Articles
Authors

Darko Palaić This is me 0000-0001-6437-8529

Ivan štajduhar This is me 0000-0003-4758-7972

Sandi Ljubic 0000-0003-3456-8369

Iva Matetić This is me 0000-0002-8722-3965

Igor Wolf 0000-0002-8720-9549

Early Pub Date December 15, 2023
Publication Date December 31, 2023
Acceptance Date September 25, 2023
Published in Issue Year 2023 Volume: 7 Issue: 4

Cite

Vancouver Palaić D, štajduhar I, Ljubic S, Matetić I, Wolf I. Development of a Building Simulation Model for Indoor Temperature Prediction and HVAC System Anomaly Detection. Journal of Energy Systems. 2023;7(4):339-4.

Journal of Energy Systems is the official journal of 

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Electrical and Computer Engineering Research Group (ECERG)  8753


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