<
https://reneweconomy.com.au/first-nations-calendars-boost-solar-forecasting-accuracy-by-up-to-26-pct-study-finds/>
"A solar forecasting model using First Nations seasonal knowledge is as much as
26 per cent more accurate than using traditional prediction models, a group of
researchers in Darwin have found.
The Charles Darwin University team used machine learning to analyse seasonal
calendars to predict solar panel output in the future.
“Incorporating First Nations seasonal knowledge into solar power generation
predictions can significantly enhance accuracy by aligning forecasts with
natural cycles that have been observed and understood for thousands of years,”
the paper, published in
IEEE Explore, says.
“The enhanced prediction ability suggests that integrating various First
Nations seasonal information can significantly refine forecasting models.
Moreover, these results highlight the potential for incorporating diverse and
culturally relevant data to improve the performance of predictive analytics in
future energy applications.”
The results were between 14 per cent and slightly more than 26 per cent more
accurate when using that local knowledge.
The model used Tiwi, Gulumoerrgin, Kunwinjku and Ngurrungurrudjba First Nations
calendars, a modern calendar called the Red Centre, and created a new dataset
dubbed AliDKA using information from the Desert Knowledge Australia Solar
Center in Alice Springs.
The FNS-Metrics (First Nations seasonal) captured temperature, irradiance,
rainfall, and details of transitional weather patterns, while AliDKA covered
temperature, relative humidity, two readings for horizontal radiation, wind
direction, daily rainfall, and both global and diffuse tilted radiation.
The team found they could achieve an error rate that is less than half of that
in popular forecasting models used in the industry today."
Cheers,
*** Xanni ***
--
mailto:xanni@xanadu.net Andrew Pam
http://xanadu.com.au/ Chief Scientist, Xanadu
https://glasswings.com.au/ Partner, Glass Wings
https://sericyb.com.au/ Manager, Serious Cybernetics
