☆ 4.7 Article

Application of artificial neural networks for optimized AHU discharge air temperature set-point and minimized cooling energy in VAV system

APPLIED THERMAL ENGINEERING (2019)

Journal

APPLIED THERMAL ENGINEERING

Volume 153, Issue -, Pages 726-738

Publisher

PERGAMON-ELSEVIER SCIENCE LTD

DOI: 10.1016/j.applthermaleng.2019.03.061

Keywords

ANN (Artificial Neural Network); AHU (Air Handling Unit); AHU Discharge Air Temperature (DAT); EnergyPlus; Matlab; BCVTB (Building Controls Virtual Test Bed)

Funding

Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) - Korea Government Ministry of Trade, Industry Energy [20184030201900]
Korea Evaluation Institute of Industrial Technology (KEIT) [20184030201900] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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Abstract

Chillers and boilers based air handling unit (AHU) system is one of the most widely used heating and cooling systems in office buildings in Korea. However, in most conventional forced-air systems, the guidelines for the AHU discharge air temperature (DAT) are not fully established and thus AHU DAT are constantly fixed to a particular set-point, regardless of dynamic changes of operating variables. In this circumstance, this study aimed at developing a control algorithm that can operate a conventional VAV system with optimal set-points for the AHU DAT. Three-story office building was modeled using co-simulation technique between EnergyPlus and Matlab via BCVTB (Building Controls Virtual Test Bed). In addition, artificial neural network (ANN) model, which was designed to predict the cooling energy consumption for the upcoming next time-step, was embedded into the control algorithm using neural network toolbox within Matlab. By comparing the predicted energy for the different set-points of the AHU DAT, the control algorithm can determine the most energy-effective AHU DAT set-point to minimize the cooling energy. The results showed that the prediction accuracy between simulated and predicted outcomes turned out to have a low coefficient of variation root mean square error (CvRMSE) value of approximately 24%. In addition, the predictive control algorithm was able to significantly reduce cooling energy consumption by approximately 10%, compared to a conventional control strategy of fixing AHU DAT to 14 degrees C. These findings suggest that the ANN model and the control algorithm showed energy saving potential for various types of forced air systems by taking dynamic operating conditions into account in each time-step.

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Machine learning based simultaneous control of air handling unit discharge air and condenser water temperatures set-point for minimized cooling energy in an office building

Hyeon Jin Jee, Sang Hun Yeon, Jiwon Park, Yeobeom Yoon, Kwang Ho Lee

Summary: An AI-based real-time prediction and control model was developed to optimize condenser water temperature and discharge air temperature set-points in a water-cooled AHU system. The ANN model, utilizing embedded MATLAB neural network objects, could determine optimal temperature set-points based on outdoor air wet-bulb temperature. Compared to the conventional fixed temperature algorithm, the ANN-based optimized control reduced total cooling energy consumption by 22%.

ENERGY AND BUILDINGS (2023)