This case study is designed to help you understand
eligibility requirements for the R&D Tax Incentive.
(Wind sounds)
WindWake has produced a range of software
modelling products that it uses to provide wind
farm consultancy services.
Noting public concern about wind farm noise,
WindWake searched the market for a software
modelling product that it could use to more
accurately predict wind farm noise.
It found that existing noise modelling software was
designed for aircraft noise and not very accurate,
and WindWake decided to develop the software itself.
(Electronic sound)
WindWake tried to design the predictive algorithms
that would power the software but quickly ran
into technical problems.
(Error beeping)
The company's engineers and software developers
didn't know any algorithms they could use.
(Mouse clicking)
WindWake conducted thorough investigations
and consulted experts in noise modelling, but
couldn't find an answer.
(Email notification sound)
WindWake set about planning a body of work...
(Projection screen being pulled down)
...to experimentally develop the algorithms.
The company reviewed the guidance material for the
R&D Tax Incentive
(Clicking keyboard sounds)
on the business.gov.au website
and self-assessed
(Ringing sound)
that some of its experimental software
development activities would be eligible.
(Mouse clicking)
(Clicking keyboard sounds)
(Cogs moving)
While WindWake knew that some of the work would be
routine software development using
existing skills and knowledge, developing the
predictive algorithms in the model could not be
done without experimenting.
WindWake undertook a systematic progression
of experimental work to develop algorithms to
analyse the components and layout of the wind
farm equipment, local landforms and objects and
weather conditions to predict the intensity and
coverage patterns of noise from wind farms.
WindWake developed hypotheses about how
these factors combine to produce particular
patterns of noise and developed experimental
algorithms to test these hypotheses.
The predictions were tested against noise maps
generated from a small existing historical set
of wind farm noise data.
(Marker pen being drawn)
The outcomes of these tests were evaluated and
the hypotheses and algorithms progressively
revised and improved.
Through this experimentation WindWake
improved the prediction accuracy of the
algorithms against the historical data until it
was confident that its revised hypotheses about
the causal factors were a good match.
WindWake's systematic progression of
experimental work included the four
interrelated parts required by the R&D Tax Incentive.
Hypothesis, experiment, observation
and evaluation, and logical conclusions.
(Electronic computer text and beeps)
Before WindWake could develop its new
algorithms it needed noise maps of wind farms.
A small set of existing data was available and WindWake
used this to electronically generate noise maps.
This activity did not require an experiment and
therefore couldn't be assessed as a core
R&D activity, however because the activity was directly
related to the experimentation in core
R&D activity one, it was self-assessed by the
company as a supporting R&D activity.
(Wind sounds)
WindWake undertook another progression of
experimental work (beeping sounds)
to extend the testing of the algorithms
developed in core R&D activity one.
In this activity WindWake tested the algorithms
against maps of noise levels and local
conditions recorded at a range of wind farms.
While WindWake knew its algorithms now matched
well against the small set of existing wind farm
data, it could not be sure that they accurately
predicted noise across the broad range of
current wind farms without further testing.
(Printing sounds)
Predictions were generated by entering the
relevant variables into the experimental
algorithms and compared against the noise maps.
The process followed the same pattern of
hypothesis to experiment, observation, evaluation
and drawing of logical conclusions that was used
in core R&D activity one.
(Wind sounds)
Before core R&D activity two was conducted,
WindWake had to collect data from wind farms.
To do this the company set up an extensive
network of noise meters, and other measurement
equipment at wind farm sites.
The data was collated and used to generate noise
maps of the various wind farms.
While this activity was not itself an
experimental activity, it directly provided the
data that made the experimentation in core
R&D activity two possible.
For this reason, it was assessed by WindWake as
being directly related to the core R&D activity and
thereby eligible as a supporting R&D activity.
WindWake kept documentation to verify
the eligibility of its activities and
substantiate its claim for the R&D Tax Incentive
including all of the relevant technical
scoping documents, results of test runs of
the algorithms including failed tests where
predictions were poor, an issues log that recorded
detailed comments on the progress of the algorithm
development, testing and improvement, including
how the problem was identified, who worked on
the solution and what solutions were tried.
All key emails and correspondence including
those related to any related software
development and code repository/pseudo code
tickets, proposed modelling systems and
validation testing reports, documentation of
the methodology, results and analysis for the
testing undertaken in the field and records of cost
estimation for undertaking the R&D activities.
In conclusion, by identifying core and
supporting R&D activities following the
R&D Tax Incentive guidance and keeping
records WindWake was able to fulfil the
eligibility requirements for the R&D Tax Incentive
and move forward with their work.
(Pushing documents aside)
For more information visit business.gov.au or call 13 28 46.