2 research outputs found
Data-driven model predictive control of buildings
Buildings account for 30% of the final global energy and 28% of the total carbon
emissions in the world. Heating, ventilation and air-conditioning (HVAC) systems can
consume up to 60% of the total energy consumption in buildings. Improving the energy
efficiency of HVAC systems is important in reducing carbon emissions and mitigating
risks associated with global climate change such as overheating of indoor environments.
Another benefit of improving the energy efficiency of HVAC systems is to save energy
cost for building owners. Many previous studies focused on the design and retrofit for
improving building energy efficiency, but few of them looked into how to improve the
building operation. As the primary building energy system, Commercial HVAC systems
are complex because of the interaction of a large number of sub-systems and
uncertainties resulting from the interactions of building mass, thermal inertia, weather
and occupancy. The application of Model Predictive Control (MPC) has received
significant attention in the last few years from researchers and the industry to the control
and management of building energy systems. Despite increasing research on using MPC
for improving the energy efficiency of HVAC systems, few of them utilise flexibilities such
as time of use (ToU) and killowatt Max (kWmax) control.
This research investigates how the control of building elements (such as windows) and
HVAC systems could improve energy efficiency and thermal comfort. This study has
been divided into two parts based on three case studies. The first part of the study
demonstrates a physics-based case study that assesses the impact of climates on the
indoor environment and how the control of window openings for natural ventilation can
reduce overheating risk in current and future climates. The results find bedrooms are
easier to suffer overheating risks than living rooms but increasing openings for natural
ventilation is more effective in reducing overheating hours in bedrooms. By opening 20%
of window area for natural ventilation, the results show that 2%, 17% and 45% of the
total 108 dwellings’ bedrooms are overheated in the 2030s, 2050s and 2080s, compared
to living rooms with 30%, 60% and 89%. In the 2030s, increasing the window opening
area ratio from 20% to 80% can reduce the number of dwellings with overheating risk in
bedrooms from 32 to 14, but find nearly no change in living rooms. However, the passive
control of building elements such as windows, blinds and overhangs has limitations in
adapting dwellings to climate change. With a maximum window area for opening plus
blinds and 2-meter overhangs, it can still not eliminate overheating risk in most UK cities
in the 2080s.
After demonstrating the limitations of the control of building elements in future climates,
the second part of the study introduces two case studies which turn to study the
iv
optimisation of controls for HVAC systems in a residential and a commercial building.
The research goes towards the development of data-driven MPC controllers for the two
buildings. A sensor network has been established for building energy metering and
environmental monitoring in the residential building to enable remote control of the
heating system with the MPC controller. It is found that the MPC controller can improve
thermal comfort by allowing more hours with room temperatures within the design
comfort band. In the commercial case study building, a data-driven MPC controller has
been developed, running optimal control of 9 indoor units per 15 minutes to maintain
indoor temperatures within the design comfort band. It proposes a demand response
method to minimize energy cost by integrating with ToU and kWmax use cases. The
study finds that MPC could take advantage of energy tariffs and flexibility by shifting the
loads from high-demand periods to low-demand periods. With the data-driven MPC, it
could reduce the peak energy consumption by up to 36% and the peak power by about
15%