Artificial intelligence could help Bhutan’s energy sector detect equipment faults before breakdowns occur, anticipate changes in hydropower generation and electricity demand, and reduce costly disruptions, technology experts said at DrukSmart Talks 2026 yesterday.
The discussions focused on the potential use of Industrial AI, Digital Twins and predictive intelligence across Bhutan’s power sector, where changing water availability, seasonal demand and the reliability of critical infrastructure have a direct bearing on electricity generation and supply.
DrukSmart, a Bhutanese IT and IT-enabled services company established in 2017, organised the event to explore how emerging technologies could be applied to industrial and infrastructure challenges in the country.
One of the key applications presented was predictive maintenance. Hydropower plants already generate large volumes of operational data from turbines and other equipment. AI-based systems can analyse this information continuously, identify unusual patterns and potentially alert operators to developing problems before equipment fails.
Such early warnings could allow plant operators to inspect machinery and schedule maintenance before faults result in unplanned shutdowns.
“Till now, our operators only monitor. As our AI technology develops, the industrial assets are getting lots of data. We analyse the data and build intelligence, so we can do predictive maintenance,” said Pema Tshering, CEO and Founding Partner of DrukSmart.
Predictive technology could also assist the sector in dealing with a broader challenge — matching electricity generation with changing demand.
Bhutan’s hydropower output is closely linked to seasonal water availability. During winter, reduced river flows lower domestic generation at a time when the country may have to depend more heavily on electricity imports.
By combining historical and real-time information on generation, water availability and consumption, predictive systems could give agencies an indication of likely changes ahead and allow them to plan electricity generation and supply accordingly.
“During winter, there are power shortages, and we have to import, which is costly. If we have AI monitoring, we can do proper planning and do accordingly,” Pema Tshering said.
The possible applications are not limited to generating stations. Similar systems could be deployed across transmission lines, substations and distribution infrastructure, allowing utilities to monitor the condition of different assets and build a more comprehensive picture of the electricity network.
Vikas Agrawal, CEO of Visionaize, said experience from the deployment of such technologies internationally indicated potential gains in both reliability and operational efficiency.
“Globally, where the products have been deployed in heavy industries and critical infrastructure, and the energy industry, plant operators have experienced lower downtime and efficiency increases, which reduces the cost of production,” he said.
Experts at the event, however, stressed that adopting AI is not simply a matter of installing new software. Organisations first need to identify specific operational problems that the technology can address, assess whether sufficient and reliable data is available, and test potential solutions through targeted pilot projects.
Developing local expertise will be another important part of the process if such systems are to be deployed and managed sustainably in Bhutan.
“There is a dual agenda to have these latest trending technologies implemented for our sector here in Bhutan, as well as having the local manpower trained,” said Avinash Sanap, Chairman and Partner of DrukSmart.
For Bhutan’s energy sector, the potential value of artificial intelligence could therefore lie less in automation for its own sake and more in improving the quality and timing of decisions.
If successfully deployed, predictive systems could help operators identify emerging equipment problems earlier, prepare for fluctuations in generation and demand, reduce unplanned downtime, and make more efficient use of the country’s existing energy infrastructure.
