engineering and technology

Science|Business: Creating a Digital Crystal Ball

Woman standing in front of Balti Jaam Market_Rasmus Jurkatam

TalTech’s Centre for Intelligent Systems is using AI to push forecasting forward.

As more and more products consume electricity (think of all those gadgets you have plugged in at the moment), it is becoming harder and harder to accurately forecast power consumption. Power producers need accurate forecasts to fine-tune their plants to increase efficiency, reduce energy consumption and limit greenhouse gas emissions and pollution.

Arriving at such forecasts is made all the more difficult by the upheaval taking place in electricity markets with the expansion of the digital economy and the Internet of Things, and the growing reliance on electric motors in vehicles, bikes and scooters. “Electricity is increasingly the ‘fuel’ of choice in economies that are relying more on lighter industrial sectors, services and digital technologies,’” says the International Energy Agency (IEA).

Ironically, the digital economy could also be part of the solution: artificial intelligence (AI) and machine learning could help the power sector get a grip on fluctuations in demand. Employing sophisticated algorithms and control systems, the Centre for Intelligent Systems (CIS), based at Tallinn University of Technology, along with the university’s Department of Software Sciences, has developed an electricity-consumption prediction model for the Estonian energy producer Alexela Energy SA. By analysing a vast amount of usage data, sometimes down to hourly consumption figures, the model accounts for possible changes in the number of Alexela’s clients, the fact that customers are distributed around Estonia and how temperatures vary in diverse climate conditions.

“It’s a dynamic process; it’s constantly changing,” says Eduard Petlenkov, professor of Intelligent Control Systems in TalTech’s Department of Computer Systems. “The conditions are always changing. Consumption is always changing; environmental conditions, weather conditions, everything is dynamically changing.” Dealing effectively with such dynamic conditions is one of the key goals of the systems the CIS is developing. It is trying to create control systems that can learn from data, adapt to rapidly changing environments and handle high levels of uncertainty.

Read the full article by Jones Hayden in Science|Business.

Photo by: Rasmus Jurkatam. 

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