To date, the residential sector accounts for a major portion of consumption by consuming more
than 40% of the entire world's energy and producing 33% of the carbon dioxide emissions. In
North America, the residential sector energy consumptions are mainly related to heating,
ventilation, and air conditioning (HVAC) systems, which are not operating in the most efficient
ways due to existing on/off and conventional controllers. In Ontario, due to the variable price of
electricity, variation in outdoor disturbances, and new Ontario Government sweeping mandate in
overhauling the energy use in residential sector, there is an opportunity to develop intelligent
control systems to employ energy conservation strategy planning model (ECSPM) in existing
HVAC systems for reducing their operating cost, energy consumption, and GHG emission.
In order to take advantage of these opportunities, two model-based predictive controllers (MPCs)
were developed in this Ph.D. research. In the first MPC controller, a Matlab-TRNSYS co-simulator
was developed to fill the lack of advanced controllers in building energy simulators. This cosimulator
investigated the effectiveness of different novel ECSPMs on an HVAC system's energy
cost saving during winter and summer seasons. This co-simulator offered 23.8% saving in the
HVAC system's energy costs in the heating season. Regardless of the strong capabilities,
employing this co-simulator for implementing comprehensive/complex optimization methods
resulted in an unacceptably long optimization time due to the of TRNSYS simulation engine.
Therefore, in the second PMC controller, simplified house thermal and HVAC system models
were developed in Matlab. To design a grid-friendly house, this model was enhanced by
integrating on-site renewable energy generation and storage systems. A novel algorithm was
developed to reduce the MPC controller optimization time. The effectiveness of the novel MPC
model in the HVAC system's energy cost saving was compared with a Simple Rule-based (SRB)
controller, which itself is an efficient HVAC controller, while this controller offered 12.28%
additional savings in the heating season.