Artificial Intelligence (#ai) will play a major role in lowering the cost for #decarbonization. Easy, trusted and secure access to #energy data is an essential ingredient for AI. We must reduce the ‘time to AI’ significantly.
Creating more data interoperability, data collaboration and energy data ecosystems will enable this. Which approaches and technologies can stimulate this? This presentation provides a #siliconvalley perspective.
It all starts with our CLIMATE……
WE MUST ACCELERATE THE ENERGY TRANSITION TO MEET
OUR 2050 CLIMATE GOALS
DECARBONIZING THE POWER SECTOR IS THE STARTING
POINT
BASED ON CLEAN ELECTRICAL ENERGY, THE MOBILITY
AND HEATING/COOLING SECTOR WILL ALSO DECARBONIZE
OVER TIME (SECTOR COUPLING)
SHARE OF GLOBAL ENERGY DEMAND MET BY ELECTRICITY
IS PROJECTED TO GROW BY 60% UNTIL 2050
(ELECTRIFICATION OF EVERYTHING)
CLEAN ELECTRICL ENERGY WILL ENABLE GREEN
HYDROGEN FOR INDIRECT ELECTRIFICATION (HEAVY DUTY
TRANSPORTATION, SHIPPING, AIRPLANES, SOME ENERGY
INTENSIVE INDUSTRIES)
Why AI for Energy?
Energy is a real time system with a lot of
complexities:
intermittent renewables
decentral energy assets (storage)
new roles for distribution grids (balancing)
from central to (more) decentral power dispatch
sector coupling
complex underlying financial transactions
cost of energy key economic and political factor
What are the key drivers for ‘AI for Energy’?
Future energy systems impossible to
manage with traditional capabilities and
approaches
Real-time data, advanced analytics and
automation needed
Speed, low costs and system
connections required: Digitization and
AI can do that
Predictions, forecasts
Automated decision making
Add comment