AI is wiring the future of energy: Can Bangladesh catch up?

At COP30, the world's biggest names in energy and technology spoke like people already living in the future. For them, AI in power systems is no longer a distant idea. It is already running forecasts, balancing grids, operating virtual power plants, and helping companies achieve 24/7 clean electricity.

From Schneider Electric to Google, everyone spoke about AI as if it were now a basic layer of modern energy systems. "There is no AI without electricity," said Manish Pant, EVP of Schneider Electric, explaining how digital grids are becoming the backbone of clean power.

Google's sustainability lead, Adam Elman, said the company already operates on 66% 24/7 carbon-free energy, with AI shifting computing loads hour by hour in response to renewable supply.

Across the Global South, India is quickly emerging as a leader. Platforms like ElectronVibe work with more than 20 utilities to test AI tools that reduce outages, integrate renewables, and improve demand response. Their model--small pilots, open calls, and constant digital upgrades--is now being closely watched by other countries.

The message from experts was clear: AI is becoming essential. It predicts solar and wind output, reduces losses, improves efficiency, helps data centres shift workloads, and allows utilities to plan upgrades faster and at lower cost.

And then there is Bangladesh--a country only beginning to explore what others have already embraced. Officials admit the work is still at its earliest stages. As the global gap widens, Bangladesh risks falling behind.

How AI helps — and why experts say it is now unavoidable

At COP30, the conversation around AI in the power sector was striking not for its ambition, but for its practicality. AI is no longer an experiment; it is the new operating system of the energy world.

Josh Parker of NVIDIA noted that AI is both a major consumer of energy and the most effective tool to reduce that consumption. "In the past decade, AI inference efficiency has improved 100,000 times," he said. "If car efficiency had improved that much, we'd drive to the moon and back on a gallon of fuel." NVIDIA's data centres, he explained, are now capable of reducing electricity and water use exponentially through AI-assisted optimisation.

Google's Adam Elman described the company's carbon-intelligent load-shifting system, which uses AI to match computing tasks with renewable power availability: "It automatically shifts jobs so we can make the best use of clean energy. During the European energy crisis, grid operators even asked us to reduce usage at peak times -- and AI made that possible."

"The only way to integrate renewables and manage resilience is through the digitalisation of the grid. Once you have the data, AI becomes the intelligence on top," Schneider Electric's Manish Pant explained from the utility perspective.

The point was repeatedly emphasised that AI optimises not just power supply, but also demand, storage, predictive maintenance, and long-term planning.

Professor Khondaker A. Mamun, PhD, Professor of AI at United International University and Founder of CMED Health, agrees that this shift is inevitable.

"AI allows us to operate the grid in real time, forecast faults, predict renewable fluctuations, and reduce wastage," he said. "Countries that do not invest now will fall behind in efficiency, reliability, and cost competitiveness."

For Bangladesh--where outages, system losses, and renewable curtailment remain persistent problems--Professor Mamun says the case for AI is not just strong, it is urgent. 

How other countries are doing it

Kenya, undergoing one of the fastest renewable transitions in the world, now generates over 90% of its electricity from clean sources, supported by advanced forecasting systems.

Chile uses Google X's Tapestry and DeepMind models to predict wind output 15% more accurately, reducing curtailment and improving dispatch.

Brazil operates data-heavy, AI-enabled grids in São Paulo and Ceará, where solar-wind hybrid parks use machine learning for stability and forecasting.

In India, utilities in Tamil Nadu, Delhi, and Odisha are trialling AI-enabled outage prediction, digital substations, and ADMS (Advanced Distribution Management Systems). The government's AI for Impact programme has created a pipeline of AI-ready tools, ranging from virtual power plants to automated grid mapping.

These examples demonstrate that, whether large or small, advanced or emerging, countries with AI-equipped power sectors are already enjoying higher efficiency and lower system costs. 

Where Bangladesh stands

Bangladesh has only taken baby steps.

"We have launched a research project through MST to build an AI-based digital twin system for solar plants," said Dr Hasan Mahmud, Director of Innovation at the Bangladesh Energy and Power Research Council (BEPRC). The pilot will model a 200-kilowatt solar array and later validate it on the 7.5-megawatt Sirajganj solar facility. It is a start--but only that.

Training programmes remain sporadic. "We did a two-day workshop... beyond that I cannot say what the ministry is doing," said Wahid Hossain, Chairman of BEPRC. When contacted, Ershad Ahsan Habib, Director General (Additional Charge) of Power Cell, inquired about AI initiatives in the energy ministry and said he "did not know" of any structured effort to prepare the power sector for AI deployment. Attempts to reach the Power Division Advisor and Secretary were unsuccessful.

Professor Mamun insists that Bangladesh does not need to invent anything new; it simply needs to build the same foundations that every successful AI-energy transition in the world has relied on. The first step, he says, is digitisation.

"AI cannot run on paper-based systems," he explained. For Bangladesh, that means a rapid push to install smart meters, upgrade SCADA networks, automate substations, and ensure real-time data collection across the grid.

Once that digital backbone exists, the country must build what he calls a national energy data platform. Today, utilities guard fragmented datasets in isolated systems, making meaningful AI learning virtually impossible. "You need a central energy data hub--anonymised but unified--before AI can learn anything meaningful," he said.

Mamun believes Bangladesh should then begin with targeted pilot programmes, similar to India's ElectronVibe model. "Start with three or four utilities, define problem statements, allow startups to propose AI models, and run controlled pilots," he said, adding that only real-world experimentation will show what works.

The success of AI in the power sector depends heavily on people who know how to use it. Bangladesh needs trained engineers and officials across BPDB, PGCB, and the distribution companies; otherwise, even the best tools will fall short.

As renewable energy grows, the grid must also be ready for the natural fluctuations of solar power. Strong forecasting systems and demand-response capabilities will be essential if the country wants to push renewable energy beyond the 15-20% mark.

Finally, Bangladesh will need policy and procurement rules. At present, there is no framework--no standards, no guidelines, no cybersecurity rules.

For Mamun, the message is simple. The longer Bangladesh waits, the harder the leap will become. "AI is becoming the operating system of modern power grids. If Bangladesh waits too long, the gap will become too wide to catch up."