Artificial intelligence methods are essential components of any renewable energy action. As, in order to make renewable energies operational, efficient and viable it is necessary to model many complex phenomena and to optimize many processes.
There is, however, an area that has been neglected by researchers and industry: the ecological impact of artificial intelligence itself. Only recently some light has been cast in this direction:
- It has been forecasted that by 2030 half of the world’s electric energy consumption with be attributed to computing facilities.
- Recent studies show the design and training of a state of the art machine learning models produced the same amount of CO2 as six medium cars during their lifespan. This raises many concerns on how to make an ecologically-viable artificial intelligence.
The goal of this project is to conceive a systemic and multi-component approach to this problem that involves: cloud and mobile computing, transfer learning, model reuse, active learning, and evolutionary computing, among others. This is a topic that needs yet to be properly explored both from theoretical and practical points of view.