Development of a Machine Learning-Based Indoor Temperature Prediction Model and Optimal Control Algorithm for AHU Discharge Air Temperature in CAV System

Sang-Hun Yeon; Dong Eun Jung; Kwang-Ho Lee

doi:10.21086/ksles.2024.10.31.5.333

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2024 Vol.31, Issue 5 Next Page

Research Article

Development of a Machine Learning-Based Indoor Temperature Prediction Model and Optimal Control Algorithm for AHU Discharge Air Temperature in CAV System EnergyPlus와 Python 연동을 통한 기계학습 기반 실내 온도 예측모델 및 정풍량 AHU 토출 공기 온도 최적 제어 알고리즘 개발

31 October 2024. pp. 333-342

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Abstract

The CAV (Constant Air Volume) system is widely used for cooling and heating buildings by maintaining a constant airflow while varying the discharge temperature. However, this system has limitations in responding promptly to various external environmental conditions and indoor thermal load changes. This study aims to control the discharge temperature of a real-time CAV system based on indoor temperature prediction using an Artificial Neural Network(ANN). A coupled simulation using EnergyPlus and Python was conducted, analyzing data from August 1st to 31st during the summer season. The developed ANN showed a prediction error with a cv(RMSE) of approximately 7.6% and an R² of 0.73. By controlling the AHU discharge temperature in real-time, indoor comfort was maintained with a PMV range of ‑0.2 to +0.7, and a PPD of less than 10% for most of the time. The energy consumption showed a variation of approximately 2 MWh compared to cases with fixed discharge temperatures.

Keywords

Energy simulation

Artificial neural network

AHU discharge air temperature control

Thermal comfort

Indoor temperature

References

Afram, A., et al. (2021). Theory and applications of HVAC control systems - A review of model predictive control (MPC). Building and Environment, 72, 343-355.

10.1016/j.buildenv.2013.11.016

Al Assaad, D., et al. (2023). A novel quantitative assessment framework of the IAQ resilience performance of buildings: The resilience score metric. Building and Environment, 243, 110669.

10.1016/j.buildenv.2023.110669

ASHRAE. (2014). ASHRAE Guideline 14-2014 Measurement of Energy and Demand Savings. ASHRAE, Atlanta, GA, USA.

ASHRAE. (2017). ASHRAE Standard 55 - Thermal Environmental Conditions for Human Occupancy. ASHRAE, Atlanta, GA, USA.

ASHRAE. (2019). ASHRAE Standard 90.1-2019: Energy Standard for Buildings Except Low-Rise Residential Buildings. ASHRAE, Atlanta, GA, USA.

Bonato, P., et al. (2020). Modelling and simulation-based analysis of a façade-integrated decentralized ventilation unit. Journal of Building Engineering, 29, 101183.

10.1016/j.jobe.2020.101183

Chen, C., et al. (2020). Energy analysis of three ventilation systems for a large machining plant. Energy and Buildings, 224, 110272.

10.1016/j.enbuild.2020.110272

Gao, J., Xu, X., Li, X., Zhang, J., Zhang, Y., & Wei, G. (2018). Model-based space temperature cascade control for constant air volume air-conditioning system. Building and Environment, 145, 308-318.

10.1016/j.buildenv.2018.09.034

Han, J., Kim, M., & Kim, J. (2015). Deep convolutional neural networks for image classification: a comprehensive review. Journal of Computer Science and Technology, 30(5), 910-919.

He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).

10.1109/ICCV.2015.123

Huang, G., et al. (2009). A robust model predictive control strategy for improving the control performance of air-conditioning systems. Energy Conversion and Management, 50(10), 2650-2658.

10.1016/j.enconman.2009.06.014

International Energy Agency (2020). Energy Efficiency 2020. IEA, Paris.

Kalogirou, S. A. (2000). Applications of artificial neural-networks for energy systems. Applied Energy, 67(1-2), 17-35.

10.1016/S0306-2619(00)00005-2

Kingma, D. P., & Ba, J. (2015). Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980.

Li, X., & Wen, J. (2014). Review of building energy modeling for control and operation. Renewable and Sustainable Energy Reviews, 37, 517-537.

10.1016/j.rser.2014.05.056

Nair, V., & Hinton, G. E. (2010). Rectified Linear Units Improve Restricted Boltzmann Machines. In Proceedings of the 27th International Conference on Machine Learning (ICML-10).

Nassif, N., Kajl, S., & Sabourin, R. (2005). Optimization of HVAC control system strategy using two-objective genetic algorithm. HVAC&R Research, 11(3), 459-486.

10.1080/10789669.2005.10391148

Srinath, K. R. (2017). Python-the fastest growing programming language. International Research Journal of Engineering and Technology (IRJET), 4(12).

U.S. Department of Energy. (2020). EnergyPlus Version 9.5 Documentation: EMS Application Guide.

Wang, S., & Jin, X. (2000). Model-based optimal control of VAV air-conditioning system using genetic algorithm. Building and Environment, 35(6), 471-487.

10.1016/S0360-1323(99)00032-3

Yan, Z., & Zhang, G. (2013). Multi-agent control system with intelligent optimization for smart and energy-efficient buildings. Journal of Cleaner Production, 59, 127-135.

Zhou, Q., & Wang, S. (2006). A model-based predictive control approach for optimal start of air-conditioning systems. Building and Environment, 41(12), 1782-1794.

Information

Publisher :The Korean Society of Living Environmental System
Publisher(Ko) :한국생활환경학회
Journal Title :Journal of The Korean Society of Living Environmental System
Journal Title(Ko) :한국생활환경학회
Volume : 31
No :5
Pages :333-342
Received Date : 2024-08-29
Revised Date : 2024-09-20
Accepted Date : 2024-10-31
DOI :https://doi.org/10.21086/ksles.2024.10.31.5.333

Journal of The Korean Society of Living Environmental System ISSN:1226-1289(Print) 한국생활환경학회

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