Modeling and control design of residential HVAC systems for operating cost reduction

Abdul Afram

doi:10.32920/ryerson.14646519

Abstract

The residential HVAC systems in Canada can consume more than 60% of the total energy in a house which results in higher operating costs and environmental pollution. The HVAC is a complex system with variable loads acting on it due to the changes in weather and occupancy. The energy consumption of the HVAC systems can be reduced by adapting to the ever changing loads and implementation of energy conservation strategies along with the appropriate control design. Most of the existing HVAC systems use simple on/off controllers and lack any supervisory controller to reduce the energy consumption and operating cost of the system. In Ontario, due to the variable price of electricity, there is an opportunity to design intelligent control system which can shift the loads to off-peak hours and reduce the operating cost of the HVAC system. In order to take advantage of this opportunity, a supervisory controller based on model predictive control (MPC) was designed in this research. The residential HVAC system models were developed and accurately calibrated with the data measured from the Toronto and Region Conservation Authority’s Archetype Sustainable House, House B (TRCA-ASHB) located in Vaughan, Ontario, Canada. Since HVAC is a large and complex system, it was divided into its major subsystems called energy recovery ventilator (ERV), air handling unit (AHU), radiant floor heating (RFH) system, ground source heat pump (GSHP) and buffer tank (BT). The models of each of the subsystem were developed and calibrated individually. The models were then combined together to develop the model of the whole residential HVAC system. The developed model is able to predict the temperature, flow rate, energy consumption and cost for each individual subsystem and whole HVAC system. The model was used to simulate the performance of the existing HVAC system with on/off controllers and develop the supervisory MPC. The supervisory controller was implemented on the HVAC system of TRCA-ASHB and at least 16% cost savings were verified.

Highlights

Most residential heating, ventilation and air conditioning (HVAC) systems are controlled by the conventional controllers such as simple on/off controller or proportional-integral-derivative (PID) controller
The two outputs of the radiant floor heating (RFH), i.e., ‘zone temperature (Tz)’ and ‘temperature of the return water (Twret)’ were more challenging to predict for some models, i.e., artificial neural network (ANN), transfer function (TF), process and SS compared to others, i.e., grey-box and autoregressive exogenous (ARX)
The data driven models all perform better than the grey-box model based on the analytical metrics as seen through the research carried out in this chapter, they all suffer from the noise in the measured data and their performance degrades as the training and testing conditions change

Summary

Introduction

Ventilation and air conditioning (HVAC) systems are controlled by the conventional controllers such as simple on/off controller or proportional-integral-derivative (PID) controller. A balance between the good generalization capability and high accuracy is provided by the grey-box models which use the physics-based white-box model as the mathematical structure and measured data to estimate the parameters of the models. The examples of energy conservation strategies include thermal storage in the building mass [106] or floor heating mass [107], passive solar gains [107], thermal storage in tank water [105, 137], temperature reset during unoccupied hours [128, 138], night setbacks, pre-cooling during off-peak periods and set-point changes during peak hours [139, 140], optimum start and stop times [141], ventilation control [142, 143] and economizer cycle control [138, 144, 145]. In this chapter passive thermal energy storage in the building and floor heating mass is used to offset the load to off-peak hours

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Results

Conclusion