Utilising Smart Home data to reduce
energy demand from space heating
Mike Coleman, Tom Kane, Vanda Dimitriou,
Steven Firth, Tarek Hassan, Jing Liao
Presented by Jing Liao
REFIT: Personalised Retrofit Decision Support Tools for UK Homes
Using Smart Home Technology
Background
– Funded by Research Councils UK – the RCUK Energy Programme
– £1.5m funding over 3.5 years, May 2012 to October 2015
– Employs 5 researchers 100% Full Time
Presentation overview
This presentation will explore how smart home
data can be used to identify opportunities to
reduce space heating energy demand in homes
– Analysis of sensor data to explore patterns of
space heating in a sample of REFIT homes (n=5) –
what can it tell us?
– Description of practical issues that may influence
the quality of data from Smart Home
technologies
Research overview
1) Identify poor heating control
2) Assess the energy saving potential of
improved heating controls
3) Develop heating control algorithms that
could be used by advance heating controls to
reduce periods of wasted heating identified
in point 1
Types of Smart Home technologies
Z-Wave Vera box / CurrentCost
British Gas Hive
Active Heating™
Space heating:
TRV radiator control
Whole house
electricity
Appliance
monitoring and
control
Feedback
information
RWE Smarthome™
Space and water
heating:
boiler control
Security: door and
window sensors,
motion detectors,
smoke detectors
Smart heating control
• The occupants could control their heating in
the following ways
– Boiler timings using the Hive system
– Individual radiator temperature profiles using the
RWE system
– Timer settings on individual electric heaters using
the VERA smart plugs
Data analysis – key sensor data
• RWE radiator and room thermostats
– Nominal temperatures (occupant settings)
– Actual temperatures – measurements
• RWE motion and door/window sensors
– Occupancy patterns
– Ventilation of building/rooms
• CurrentCost IAMS and Z-wave smart plugs
– Use of portable electric heaters?
Method
Identify periods of poor temperature
control by comparing user profiles (settings)
with measured senor data
Example 1 – user profile
• House 3 – lounge radiator
Example 1 – sensor data
Time lag in
reaching
temperature
Excess heating
-- need for
optimization
Manual control – to
reduce high temperature
Example 2 – user profile
• House 19 – living room radiator
Example 2 – sensor data
Temperature reached
after occupant setting
Boiler starts heating
or radiator at the end
of the system?
The radiator is unable
to provide the
necessary heating
requirement
-- Undersized radiator?
-- Poor insulation?
Example 3 – sensor data
30
Occupant setting
25
Actual temperature
Temperature (°C)
Motion
20
15
10
5
Unoccupied heating
0
Key findings
This work has identified three areas where smart
home sensors can be used to identify energy
savings
(1) Heating system characteristics – times when
temperatures do not follow occupant settings (set
point overshoot, heating lag)
(2) Identifying excessive heat loss – undersized
radiators or poorly insulated rooms
(3) Unoccupied heating – times when the heating is
in use by the room is unoccupied
Limitations
• Installed systems not fully compatible i.e.
smart home heating controls did not control
boiler on and off times
• Sensor deployment is not always widespread
enough
• Accuracy of temperature measurements (link
to where temperature is measured i.e. at the
radiator valve)
Future work
• Assessment of the energy saving potential of
the systems installed by identifying and
reducing periods when energy is wasted
• Development of algorithms to address
improve heating control to reduce energy
demand while maintaining thermal comfort
Practical issues
Radiators covered – leading
to unexpected sensor data,
i.e. lower measured
temperatures during heated
periods
Sensors not fixed – leading
to poor sensor data, i.e.
motion sensors obstructed
or location of sensor is
unknown
Acknowledgements
• REFIT is a consortium of three universities - Loughborough, Strathclyde and East
Anglia - and ten industry stakeholders funded by the Engineering and Physical
Sciences Research Council (EPSRC) under the Transforming Energy Demand in
Buildings through Digital Innovation (BuildTEDDI) programme.
• For more information
– visit our website: www.refitsmarthomes.org
– email me: Steven Firth s.k.firth@lboro.ac.uk
• REFIT is a founding project of the TEDDINET network, a RCUK funded network of
£22m research project funding in the area of transforming energy demand
through digital innovation www.teddinet.org
Questions