ANN Based Optimized AHU Discharge Air Temperature Control of Conventional VAV System for Minimized Cooling Energy in an Office Building

Jong Man Lee,Kwang Ho Lee,Won Hee Kang,S.I Tanabe,P Wargocki,G Cao,L Mazzarela,J Kurnitski,H Zhang,M.C Gameiro Da Silva,I Nastase,C Inard

doi:10.1051/e3sconf/201911105014

Abstract

In most conventional forced-air systems, the guidelines for the air handling unit(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°C.

Highlights

According to the World Meteorological Organization’s Greenhouse Gas Bulletin in 2017, the average global atmospheric carbon dioxide level in 2016 was 403.3ppm
The energy consumption was highest at 46,782kWh when AHU DAT was 12°C, and energy consumption was lowest at 39,038kWh when AHU DAT was 17°C
A significant reduction of approximately 17% was achieved when AHU DAT was 17°C compared to 12°C as the energy consumption of AHU Supply Fan and Relief Fan increased while the energy consumption of other equipment decreased

Summary

Introduction

According to the World Meteorological Organization’s Greenhouse Gas Bulletin in 2017, the average global atmospheric carbon dioxide level in 2016 was 403.3ppm. As a result, recognizing the worldwide severity, we have agreed to reduce greenhouse gas emissions, including carbon dioxide, through the 2015 Paris Climate Change Convention.[2]. There is no energy saving guideline for the heat pump control that is applied to most office buildings. About 57% of the cooling energy uses electricity, which causes greenhouse gas increase.[4]. This study proposes a new control guideline for AHU(Air Handling Unit) DAT(Discharge Air Temperature) in heat pump. This study aims to analyze cooling energy reduction effect during summer according to AHU DAT optimal control by using ANN model

Objectives

Methods

Results

Conclusion