An exciting project called 'Smart Energy @ Home' is underway in the Danish municipality of Middelfart, where access to data - including public data - plays a key role.

1. Smart
Energy
@
Home
-­‐
a
project
that
lives
by
data
An
exciting
project
called
'Smart
Energy
@
Home'
is
underway
in
the
Danish
municipality
of
Middelfart,
where
access
to
data
-­‐
including
public
data
-­‐
plays
a
key
role.
The
focus
of
the
project
is
to
develop
scalable
methods
to
help
homeowners
save
energy
without
sacrificing
comfort.
This
is
done
by
examining
how
much
energy
2-­‐300
homes
can
save
by
installing
intelligent
energy
management
and
receive
remote
counseling.
Background
Denmark
has
a
political
target
that
electricity
and
heat
in
2035
will
be
produced
with
100
%
renewable
energy.
In
this
context,
it
is
a
base
assumption
that
the
total
requirement
for
heating
-­‐
in
spite
of
new
buildings
-­‐
must
be
cut
in
half
by
2050.
But
even
if
we
look
twenty
years
ahead
from
now
more
than
70%
of
the
building
stock
will
consist
of
homes
that
are
already
built
today.
These
buildings
have
a
much
higher
consumption
energy
than
the
buildings
that
we
build
today
and
that
will
be
built
in
the
future.
It
is
therefore
in
the
established
housing
the
largest
energy
savings
are
to
be
realized
in
order
to
achieve
the
goal
of
full
phase-­‐out
of
fossil
fuels.

2. The
Danish
Building
Research
Institute
has
estimated
that
200
billion
Danish
kroner
must
be
invested
to
halve
heat
consumption
in
existing
buildings.
To
nudge
homeowners
volunteer
to
make
the
necessary
investments
in
order
to
halve
energy
consumption
for
heating
there
is
a
need
for
very
active
and
educational
counseling
and
a
wide
range
of
credible
energy
efficiency
services
offerings.
Against
this
background,
the
project
goal
is
to
develop
and
demonstrate
new
concepts
and
offers
to
homeowners
which
proves
that
smart
energy
in
the
home
for
the
measurement
and
control
of
heating
systems
in
combination
with
a
resource
efficient
customer
dialogue
and
counseling
to
homeowners
provides:
• Verifiable
and
sustained
"automatic"
savings
and
• Activate
homeowners
and
increases
their
desire
to
change
consumption
behavior
and
implement
new
energy
investments.
Intelligent
energy
management
The
home
automation
system
used
in
the
project
is
called
PassivLiving
and
is
developed
by
PassivSystems,
a
leader
in
energy
optimization
of
private
homes.
PassivLiving
lowers
the
temperature
in
the
house
when
the
occupants
are
not
at
home
during
the
day,
when
they
are
on
vacation,
or
when
they
go
to
bed.
And
the
system
also
ensures
that
the
temperature
is
turned
up
again
when
needed.
In
contrast
to
standard
time
control
of
heating
systems
the
residents
do
not
have
to
guess
how
many
hours
the
heating
systems
must
be
on
for
their
house
to
reach
the
desired
temperature,
when
they
get
up
in
the
morning
and
come
home
in
the
afternoon.
This
adjusts
PassivLiving
itself,
so
all
that’s
needed
is
to
specify
the
temperature
desired
in
the
house
at
which
time.
PassivLiving
is
being
installed
in
2-­‐300
houses
in
Middelfart
municipality.
Remote
counseling
The
remote
counseling
will
try
out
new
IT-­‐based
concepts
for
user
involvement
and
resource-­‐efficient
advice,
where
measurements
and
advanced
algorithms
provide
energy
advisors
and
homeowners
a
particularly
good
basis
for
assessing
possible
measures
for
energy
optimization
of
the
property.
The
goal
is
to
make
it
better
and
cheaper
than
traditional
energy
consultancy.
• Better
-­‐
because
there
is
access
to
specific
and
detailed
data
on
the
condition
of
the
building
and
its
dynamic
energy
consumption.
• Cheaper
-­‐
because
there
is
no
requirement
for
an
an
expensive
consultant
to
inspect
the
property
on-­‐site.
From
data
to
value
The
diagram
below
illustrates
the
relationship
between
the
individual
homes,
the
various
data
sources
and
the
remote
counseling
service
in
the
project:

3.
A
wide
range
of
data
concerning
home
energy
consumption
are
measured,
including
• Energy
input
to
the
home
heating
system
(remotely
read
in
conjunction
with
the
relevant
utility
where
possible)
• Amount
of
heating
water
produced
• Hot
water
consumption
• The
homes
temperature
These
measurement
data
are
supplemented
by
a
number
of
other
data
that
are
relevant
to
the
home
including
• Weather-­‐measurements
and
forecasts
–
made
available
to
the
project
by
the
Danish
Meteorological
Institute
• Building
and
Housing
Register
(BBR),
public
data
about
building
size,
type
of
accommodation,
historical
energy
consumption,
etc.
• Additional
master
data
for
the
property,
such
as
number
of
occupants
and
their
age,
already
completed
renovations
such
as
window
replacements,
etc.
-­‐
This
data
is
gathered
through
questionnaires
or
from
other
registries
By
combining
these
data
sources
much
useful
information
can
be
derived
about
each
individual
property,
eg
• The
thermal
profile
of
the
house
• The
efficiency
of
the
heating
system

4. • Key
figures
for
heating
consumption
of
kWh
per
square
meter
and
kWh
per
occupant
and
comparison
with
the
average
for
homes
of
similar
type
• Household
behavior
in
relation
to
family
life,
housing
type,
etc.,
which
can
be
used
to
consider
different
customized
smart
energy
solutions
to
various
segments
of
residents
and
types
of
buildings.
• The
heating
or
cooling
rate
for
the
house,
in
conjunction
with
weather
data
The
last
bullet
can
give
specific
information
about
which
parts
of
the
house
that
can
benefit
from
forms
of
insulation
–
for
example
if
it
is
determined
that
the
house
is
cooling
faster
than
usual
by
strong
easterly
winds,
it
appears
beneficial
to
insulate
the
cavity
wall
or
replace
the
windows
on
the
east
side
of
the
house.
Similarly,
knowledge
of
the
heating
rate
by
sunlight
combined
with
weather
forecasts
can
be
used
to
control
heating
-­‐
so
the
heat
production
is
turned
down
when
there
is
a
prospect
of
sunshine.
The
above
examples
provide
a
good
illustration
of
the
possibilities
that
arise
from
being
able
to
combine
different
detailed
data
sources
with
an
hourly
or
daily
granularity.
Note
-­‐
this
is
not
just
interesting
knowledge,
this
is
information
that
motivates
and
provides
actionable
knowledge
to
homeowners
about
what
kind
of
improvements
and
behavioral
changes
that
can
reduce
energy
consumption
in
the
home
of
this
individual
home
owner.
In
the
above
example,
access
to
public
data
in
the
form
of
weather
reports
and
forecasts
is
critical
to
provide
the
necessary
basis
for
cost-­‐effective
decision
making.
Similarly,
there
are
many
other
public
data
sources
such
as
the
BBR
registry
and
other
registry
information
which
in
conjunction
with
easy
access
to
home
consumption
data,
enables
the
creation
of
new
innovative
greentech
solutions.
The
‘smart
energy
@
home’
project
kicked
off
in
2012
and
will
run
through
three
heating
seasons
until
2015.
The
project
is
made
possible
through
a
grant
from
Realdania
-­‐
a
philantropic
association
supporting
projects
in
the
built
environment
–
and
supplemented
by
investment
from
the
project
partners
in
terms
of
hours
and/or
money.
The
project
partners
are
beyond
Realdania:
• Middelfart
Municipality,
pursuing
an
ambitious
strategy
for
green
growth
• PassivSystems
provides
the
leading
edge
home
automation
system
used
• Bolius
–
The
homeowners
Knowledge
Center
is
responsible
for
for
the
remote
counseling
and
ongoing
knowledge
transfer
to
the
participating
homeowners
• Danish
Building
Research
Institute
(SBi)
process
and
analyze
the
data
collected
in
the
project
from
a
research
perspective.
You
can
read
more
–
in
Danish
–
about
smart
energy
@
home
at
www.seih.dk

5.
Ansvarlig:
Søren
Peter
Nielsen
Publiceret:
08.01.2013
http://digitaliser.dk/resource/2432118