Bangladesh is once again confronting a familiar energy crisis. Gas shortages are limiting electricity generation, load-shedding has increased, and the government has had to ask shops and markets to close earlier in an effort to conserve power.
Yet there is a deeper contradiction at the heart of the crisis. Bangladesh now has an installed generation capacity of 32,322 MW, while the highest electricity generation ever served was 17,200 MW on May 20, 2026, according to Power Cell data. The country has invested heavily in generation, transmission and distribution, but whenever gas supply falls, a significant share of that capacity becomes unusable.
That suggests our problem is not simply a shortage of power. It is also a question of how intelligently we produce, distribute and consume the energy already available to us.
This is where artificial intelligence could make a practical difference.
Bangladesh’s gas demand is around 3,800 mmcfd, while recent supply has fallen to roughly 2,100 mmcfd. The Daily Star recently reported that the electricity supply gap at one point this month reached 3,592 MW, with demand at 17,523 MW against supply of only 13,931 MW.
We cannot produce an additional 1,500 mmcfd of gas overnight, and we cannot build a new power plant overnight. What we can do is make better decisions with the gas, electricity and infrastructure we already have.
One of the most immediate applications is demand forecasting. Electricity demand is not random. It changes with temperature, humidity, working hours, industrial production, irrigation, holidays, Ramadan, air-conditioning use and even the times when households charge batteries and other devices.
An AI-based forecasting system can analyse large volumes of such data and estimate demand hour by hour. If system operators can see at 2pm that demand is likely to cross 18,000 MW at 8pm, they have six hours to prepare. That is far more useful than responding only after the shortage has already begun.
A second opportunity lies in the use of scarce gas. When gas supply is tight, allocation should not simply follow yesterday’s schedule. The better question is where each unit of gas can produce the greatest value for the power system.
AI can continuously compare the efficiency, heat rate, operating condition and fuel requirement of gas-fired power plants. It can then help operators determine which plants should run, at what level and in what sequence. If one plant can produce more electricity from the same quantity of gas than another, that difference should matter when fuel is scarce.
This is not a futuristic idea. The International Energy Agency estimates that widespread use of existing AI applications in power-sector operations and maintenance could generate up to US$110 billion in annual savings by 2035, largely through lower fuel use and operating costs.
Predictive maintenance offers another important benefit. Transformers, turbines, substations and transmission equipment often show warning signs before they fail. Temperature, vibration, voltage and current patterns can begin to change well before a major breakdown occurs.
An AI system can detect those changes early and alert engineers to inspect the equipment. Preventing a failure is far cheaper and less disruptive than waiting for a transformer or other critical asset to break down and leave thousands of consumers without electricity.
The same logic applies to Bangladesh’s transmission and distribution networks. The country now has nearly 18,000 circuit-km of transmission lines and more than 657,000 km of distribution lines. Managing a network of that scale is an enormous operational challenge. Power Cell data show that official distribution losses stood at 7.38% in June 2025.
By analysing real-time grid data, AI can help identify overloaded lines, abnormal voltage, technical losses and potential faults. The IEA estimates that AI-enabled fault detection can reduce outage duration by 30% to 50%. It also says that improved monitoring and AI-based grid management could unlock as much as 175 GW of additional transmission capacity globally from existing lines, without constructing new ones.
Bangladesh would not need to achieve the full global potential for this to matter. Even modest gains in efficiency, reliability and fault response could have a meaningful impact on a system operating under fuel and infrastructure constraints.
Electricity theft and abnormal consumption are another area where data can improve enforcement. With smart meters, utilities can compare the electricity entering a transformer with the amount being consumed and billed in the area it serves. Unusual patterns can be flagged automatically, helping inspection teams focus on places where the data indicates a genuine problem.
That is a much more efficient approach than trying to inspect every location with equal intensity.
AI can also help shift demand instead of simply cutting it. Not every factory, commercial building, water pump or battery charger needs to operate at maximum load during the same peak hours.
Some demand is flexible. Refrigeration cycles can be adjusted, EV and battery charging can move to off-peak periods, and commercial buildings can optimise cooling without compromising basic comfort or productivity. The objective is not to ask people to consume less at any cost. It is to use electricity at the right time and reduce unnecessary pressure on the system during peak periods.
The same intelligence can support renewable energy integration. As rooftop solar expands, AI can use weather data to forecast solar generation and coordinate it with battery storage, grid supply and conventional power plants. This becomes increasingly important as the power system grows more complex and more distributed.
Bangladesh has already begun to recognise this opportunity. In October 2025, the Asian Development Bank approved a US$1 million technical-assistance project to support AI-enhanced distribution grids in Bangladesh. The initiative is intended to pilot AI technologies and develop an AI-based energy management system for distribution companies.
The debate, therefore, is no longer about whether AI has a place in Bangladesh’s energy sector. The more important question is whether the country can deploy it at the scale and speed that the present crisis requires.
A practical next step would be a National Energy AI Platform connecting BPDB, PGCB, Petrobangla, gas distribution companies and electricity distribution utilities. Its initial priorities should be straightforward: forecast demand more accurately, optimise gas allocation, predict equipment failures, reduce grid losses and manage peak demand.
Over time, that platform could evolve into a national AI-powered energy control centre capable of running ‘what-if’ scenarios before major operational decisions are made. System operators could test the likely impact of a gas shortage, a plant outage, a heatwave or a sudden rise in demand before choosing how to respond.
None of this means AI can replace gas exploration, LNG imports, renewable energy, transmission investment or new generation capacity. Those investments remain essential.
But Bangladesh has already spent billions of dollars building energy infrastructure. The next challenge is to make that infrastructure work more intelligently.
For decades, the standard response to an electricity shortage has been to build another power plant. The energy crisis now gives us a reason to broaden that approach. The next major investment should not only add more megawatts. It should add intelligence across the entire power system.
In the energy system of the future, one of the most valuable ‘power plants’ may not be made of steel and concrete. It may be built from data, algorithms and better decisions.
- The writer is a policy analyst specialising in digital governance and public-sector reform
