A truly sustainable future requires solar power, but trying to consistently maximize the energy harvested by panel arrays remains one of the industry’s biggest challenges. Unlike fossil fuels, solar power yields are dictated by the complex interplay of weather and atmospheric variables, as well as the sun’s own activity. This means it’s basically impossible to craft a universal prediction model, so localized solar forecast systems are a necessity.

While machine learning technology has significantly improved today’s forecast models, there is still a lot of room for improvement. But an artificial intelligence program is only as good as the data used to train it—and according to researchers at Australia’s Charles Darwin University, it’s tough to find a better solar forecasting dataset than First Nation seasonal calendars. Their new approach is detailed in a study published in the IEEE Open Journal of the Computer Society.

Present-day non-Indigenous cultures generally divide the year into four seasons, but that’s not the case for many past and present Indigenous communities. Solar calendars like the Aztecs’ were accurate enough to guide farming practices that fed millions of people, for example. In Australia, the people of the Tiwi Islands use a three-season calendar based on their local ecological knowledge. Darwin’s Gulumoerrgin (Larrakia) community recognizes seven principle seasons, while the Kunbarlanja (Gunbalanya) and Ngurrungurrudjba of the Northern Territory also possess nuanced calendars of their own.

“These calendars are closely tied to weather patterns and seasons. The deep understanding of local climate in these calendars enables First Nations people to make informed resource management and sustainability decisions,” the study’s authors explain. “As climate change affects weather patterns, knowledge of these calendars becomes crucial for adapting to environmental challenges.”

Additionally, unlike conventional calendar systems, Australia’s Indigenous cultures base their seasonal classifications on local ecological cues. These include plant and animal behaviors that closely relate to shifting sunlight and weather patterns.

With this in mind, the team broke down information into various datapoints from the Tiwi, Gulumoerrgin (Larrakia), Kunwinjku, and Ngurrungurrudjba First Nations calendars, along with a modern calendar known as Red Centre. Researchers then entered their First Nations Seasonal Metrics (FNS-Metrics) dataset into a novel machine learning model designed to detect large-scale patterns. From there, they tested the system against past solar power and weather information collected by the Desert Knowledge Australia Solar Centre (DKASC) in Alice Springs.

The results were striking: the First Nations Seasonal Metrics vastly outperformed many of today’s leading forecasting models. Compared to an already strong baseline model, the First Nations data were 14.6 percent more accurate while reducing the error rate by 26.2 percent—less than half the error rate of existing forecasting programs.

“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,” said Luke Hamlin, a CDU Ph.D candidate and study co-author who is also a member of eastern Australia’s Bundjulang nation.

Hamlin added that integrating Indigenous knowledge into predictive models can more closely tailor a system to reflect the more nuanced shifts in environmental conditions. This offers “more precise and culturally informed forecasting” for individual regions. The team says this strategy is also particularly promising for rural communities already home to larger First Nation communities. These same places could benefit the most from additional solar farms. And the approach isn’t just limited to solar power.

“In future work we’ll explore the applications of the model to other regions and renewable energy sources,” said Thuseethan Selvarajah, a CDU information technology lecturer and study co-author.

 

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