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I'm Tao Hong from the University
of North Carolina Charlotte.
I'm the director of the Big
Data Energy Analytics Laboratory
or Big DEAL.
We conduct applied research to
discover actionable insights
from energy data.
A large portion of
our research project
is our energy forecasting
such as forecasting
the renewable generation
and the amount
and prices of
electricity and gas.
Electricity load f
forecasting, or better
known as load forecasting
by the power industry,
is the area I'm
most interested in.
This is a fascinating area not
only to the power and energy
society but also to the
forecasting community.
The primary product of the
power industry is electricity.
The power companies
move the electrons
through the grid to end users.
There are two features that make
the electric supply chain very
different from other supply
chains such as in manufacturing
and retail.
Number one, the electrons
travel really fast,
at the speed of light.
Number two, there's not
yet a practical solution
for bulk storage of electricity.
Therefore the supply
and demand has
to be balanced in real time.
To operate and plan the system,
we have to understand when,
where, and how much
the electricity is
spread throughout the system.
Without accurate
forecast, we as end users
may experience increasing rates,
brownouts, or even blackouts.
That's why load forecasting is
crucial to the power industry.
Obviously we need
the load history.
Because a large
portion of the load
is used for heating and
cooling, we need weather data.
Since consumption patterns
change based on the work
schedule, working hours,
holidays, and special event
information is also important.
When the system experiences
outages dragging down the load
level, we use the outage logs.
For long-term load
forecasting, we
need to add economic information
and sometimes demographic data.
To improve the
forecast accuracy we
can also leverage
hierarchical information
such as weather and
load data collected
at higher frequency timing
and more granular geographical
regions.
Compared to classic
demand or sales data,
the electricity demand data
can be much higher resolution
both temporally and spatially.
For instance, many
research activities
today are to forecast load
at the meter level which
utilizes 15 minute interval
data from thousands of meters.
At an accurate level, the
electricity amount typically
presents the signal by month
of the year, day of the week,
and hour of a day.
Typical residential loads
are highly driven by weather.
In contrast, some
industrial loads
may show very random
patterns that are not
much dependent on weather.
I believe that the only
way to measure the success
is whether the method has been
widely used by the industry.
I can name some of
those successful ones.
In late 1990s, the Electric
Power Research Institute
sponsored a project to see about
a short-term load forecasting
system based on neural networks.
The resulting product
is still being used
by many power companies today.
In the recent decade,
Rob Hyndman and Shu Fan
developed their methodology
from several projects
sponsored by alternative
energy market operators.
Their work is now in our
package called MEFM or Monash
Electricity Forecasting Model.
The research from my
doctoral dissertation
was sponsored by a
US utility company.
In the early 2010s,
it was commercialized
by the software company SAAS
and then deployed worldwide.
Most of my recent developmental
forecasting methodologies
are also being used
by power companies.
To help recognize and
promote the methods that
can potentially
be successful, we
organized two global energy
forecasting competitions
in 2012 and 2014.
The winning methods
and the data sets
were published by
the International
Journal of Forecasting.
We hope that researchers
can leverage those data sets
and use them for
benchmarking purposes.
I will say that each successful
method is a simple solution
to a real world problem.
Once the real world problem is
solved, the level of success
is dependent upon the
simplicity of the solution.
Note that simple does
not mean trivial.
A simple solution can
be quite powerful.
Some level of sophistication
is necessary to achieve
high accuracy.
But the industry may not need
the most accurate forecasts
anyway.
Those successful
methods typically
find a good balance between
simplicity and accuracy.
I believe that the key to
success in load forecasting
is partnership
with the industry.
The industry partner
can help ensure
the problem we are solving is a
problem of practical interest.
The successful method
I named earlier
were all born from
industry-sponsored projects.
Having a trustworthy
industry partner
will put you halfway
to the success.
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