A sustainable development pathway for our digital society requires new means to self-regulate the consumption of physical resources including energy.
In a new video showcasing his project, Economic Planning and Optimized Selections (EPOS), Dr Evangelos Pournaras (School of Computing) outlines his research on collective learning, a novel human-centred artificial intelligence paradigm, and its applicability to energy management.
Energy supply experiences significant risks and uncertainties, including from volatile renewables and unpredictable demand during times such as pandemic lockdowns. Systems must adapt to prevent catastrophic blackouts. The digital transformation promises new opportunities to combat such uncertainties.
The EPOS project has applied collective learning to power grids to prevent blackouts, transport systems to minimize traffic congestion and load balancing of bike sharing infrastructure and stations.
Via collective learning, several highly complex computational problems can be solved efficiently – crucially, while aligning with citizens’ social values, reducing system operators and utilities’ costs, and offering a decarbonising and sustainability pathway.
Watch the video:
Video transcript: Collective Learning — Human-centred Distributed Artificial Intelligence
And find out more about the EPOS project.