Developing cheaper and safer non-weather-based AI models for microgrid demand and supply forecasting in rural areas
This study examines how to make microgrid scheduling using AI forecasting methods simpler, cheaper, and safer using alternative sources of data other than weather data, in order to accelerate the spread of solar power in rural areas.
Posted by John Lee on Tuesday, May 30th, 2023 in May 2023, Artificial Intelligence, Machine learning, microgrid forecasting, renewable energy, rural areas
Providing Recommendations on Combating Climate Change Through Data Driven Analysis
This study applies data science techniques in order to recommend further action toward combating climate change.
Posted by John Lee on Friday, May 19th, 2023 in May 2023, climate change, Machine learning, Sustainability