In past decades, diverse types of Artificial Intelligence (AI) have come to permeate most sectors of societal existence by monitoring phenomena, processing data and reimagining processes. The energy sector makes no exception, especially considering the increased pressures to simultaneously control consumer cost and achieve environmental and business goals. [1] With the exponentially worsening state of environmental degradation at an unprecedented rate [2] and progressively more imperative necessity for environmental sustainability [3], AI presents an opportunity to provide practical solutions for energy transition, [4] while also arguably posing the thread of becoming a problem in of itself by infringing on democratic rights and universal ethics [5]. What’s more, while trying to solve existing problems, we need to make sure that the means by which we attempt to do so do not surreptitiously necessitate additional production activity that would not balance out the positive intention and design. [6] The following short exposé enlists some of the most promising aspects, in which AI can facilitate and accelerate the transition to a lower-carbon future for power generation and sustainable dynamics of supply for a growing demand while championing availability, resiliency, reliability, affordability, and optimal efficiency.

Intelligent wind power

One of the most popular and widely adopted renewable energy source island and offshore turbines. AI-powered solutions have the capacity to enable the turbines within a farm to “communicate with each other to generate more power, more efficiently.” [7]

“Just building more wind and solar is not going to solve the challenge of the energy transition,” says Charles D. McConnell, Executive Director of the Carbon Management and Energy Sustainability center at the University of Houston “It’s a part of it, obviously, but also it’s the ability to integrate that into the grid, to be able to feed forward load patterns, to understand the dynamics of the wind, the demand, the demand changes and all of the dynamics of a grid system.”[8]

Furthermore, AI algorithms can be employed to make data-driven forecasts re the maintenance cycle of the machinery, making said maintenance more efficiently executed. [9]

Smart power plants & energy systems

Another way, by which AI-enabled systems can positively affect the energy transition towards maximized operating efficiency, is the automation of sensor-connected traditional power plants capturing massive amounts of insightful big data, including predictive maintenance and automatic adjustment of power generation reactive to the energy demand dynamics. [10]  For example, Mitsubishi Power developed “a customizable suite of user-driven, digital power plant solutions fueled by cutting-edge analytics” called Tomoni, which “turns a mountain of big data into valuable actionable insights that increase your power plant efficiency and profitability” through harnessing and leveraging data analysis for collaboration and optimization. [11] Going one step further with intelligent automation Mitsubishi Heavy Industries Group in building a natural gas power plant in Takasago, Japan: 

“MHPS will also be training its AI apps, allowing T-Point 2 to eventually become the world’s first autonomous combined cycle power plant. This will catapult power generation into a future where digital technologies are fully integrated into plant operations, allowing plant owners to leverage data to optimize performance, enable condition-based predictive maintenance for equipment, selectively automate operation and maintenance (O&M) decision-making, and reduce risk.”[12]

According to E.ON, the intelligent approach of development of self-learning mechanisms in regards to anticipating grid maintenance needs “can reduce the number of outages in the grid by up to 30% compared to a conventional approach. This results in better security of supply and better network quality for our customers and partners.” [13] 

Moreover, big tech companies have also been actively entering the energy market by deploying machine learning technology, providing AI-based computing solutions for environmental sustainability projects. For instance, the newly established joint venture named Avanade that has Microsoft and Accenture partnering up “to help utility and energy companies change their energy systems and reduce the cost of decarbonizing the supply and demand of electricity in the country.”[14]

Weather forecasting

Despite the considerable advances made in renewable energy technology and processes, both solar and wind-generated alternatives are highly dependent on nature. Irrespective of the capacity to harness solar and wind energy, ultimately, panels and turbines require sun and wind for renewable facilities to operate. This makes AI and machine learning tools for predicting site-specific weather patterns linchpin factors in forecasting power generating potential ahead of time and design integrating variable sources of electricity. [15] Advanced weather forecasting increases predictability by boosting system stability and system planning and hikes upward the value of the renewable industry. [16] One such tool that attempt to meet the renewables intermittency challenge [17] and guarantee stable supply capacity is Google’s DeepMind [18]: 

“Xiaoqin Ma, head of technology at ONYX InSight, a provider of predictive maintenance for wind turbines, said: “In recent years, a variety of initiatives have brought AI to the renewables industry, from blade inspection to weather forecasting and predictive maintenance. This development from Google’s DeepMind is the latest proof for operators that modern data use can bring substantial benefits to the renewable energy industry.” [19]


Waste management

An industry within the energy sector that could potentially reap the benefits of AI-driven solutions is waste management. The Waste-to-Energy incineration plants produce electricity from household waste by processing the generated heat that drives steam turbines. AI and IoT technologies have been proved to excel at the monitoring and predictive calculation of the furnace combustion. [20]

Another application of AI within waste management is the detection of energy waste in facilities and buildings. [21]

“While the ability to quickly detect and remediate faults or energy waste in buildings is important to facilities managers and owners on a practical level, there is a more global imperative for minimizing such waste, says Akinci.
“We talk about energy shortages and energy consumption,” she says. “Buildings are one of the biggest sources of energy usage, and HVAC systems are one of the biggest sources of energy usage in buildings.”
The United States Department of Energy states that between 25 percent and 60 percent of energy used in the HVAC systems of commercial buildings is wasted.” [22]

Three examples, respectively from the US, the Netherlands and the UK exemplify how the efforts for energy transition and sustainable practices are bringing together public and private institutions, researchers and business trailblazers:
Stanford’s Precourt Institute for Energy, the Stanford Institute for Human-Centered Artificial Intelligence (HAI), and the Bits & Watts Initiative will fund two new research projects on using AI and machine learning “to make energy systems more sustainable, affordable, resilient and fair to all socioeconomic groups.” with a budget of $1.2 million. [23]
2. Netherlands-based startup is “taking a big leap forward in determining the quality and maintenance status of residential and commercial real estate” [24] by using “a mixture of Internet of Things (IoT), Artificial Intelligence (AI), Drone Imaging Technology to give data-driven insights on both an operational and a strategic level. The result is a digital inspection product that aims to make building inspection and maintenance more efficient.” [25]
3. The energy sector in the UK, for instance, is boasting a growing number of private equity deals. One such deal is Maven Capital Partners’ investment of £2 million in London-based Guru Systems which provides B2B IoT hardware, software, and analytics solutions to energy companies looking to reduce their carbon footprint.[26] The way it functions is that:

“Guru’s hardware can be fitted to new-build developments or retrofitted to capture data from existing heat networks and other onsite energy systems. Its software then uses AI-driven analytics to provide complete visibility over the system from a bird’s eye view all the way down to the performance in each individual dwelling. This helps its client’s identity performance issues, improve efficiency, and reduce carbon emissions.” [27]

Considerable efforts are also being put into better understanding the impact AI can have on waste recycling. One such project is the Austria “AI-Waste” aiming to employ digitalization to optimize waste treatment processes. [28]

A somewhat novel facet of exploration is the usage of AI and big data is the direct mechanism of material discovery as well as the following indirect potential: 

“- Reduce chemical footprints of products, supply chains, and manufacturing;
 – Apply machine learning to design techniques for lead-free panels; and 
 – Use big data tools to rapidly characterize chemicals and identify safer solvents.” [29]

Knowledge-based economy and Smart City Development

The integration of artificial intelligence into renewable energy systems corresponds directly to the needs of the knowledge-based economy. What’s more, smart cities have proved to be successful hosts of integrated approaches to energy saving and diverse urban improvements in respect to residency and modes of mobility. [30] Current trends indicate that modern urban end-users validate the drive for adoption of smart appliances and interactive networks in their daily lives. [31]


The COVID19 pandemic has been recognized by many researchers as the ultimate digital catalyst that triggered innovation and served to lower the cost of AI around the world. The energy sector is auspiciously positioned to embrace the potential benefits of AI-fuelled solutions, while conscientiously engaging any related concerns and threats. To conclude, Johannes Sedlmeir, researcher at Munich-based Fraunhofer FIT, addresses one of the most prominent concerns “that training complex AI models consumes a lot of energy, but the savings generated from optimizing processes can outweigh consumption.” Similarly, while a lot has been said about energy consumption of blockchain technology, “industry projects typically use blockchains with negligible energy consumption”. [32]

Written by Angela Sarafian, COO

The above excerpt is part of a research paper on the Korean Smart Energy Transition to be presented at the 26
th World Congress of the International Political Science Association, 10-15.07.2021. 


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[32] Matalucci, S. (2021) Coronavirus spurs energy transition through artificial intelligence. DW. Available at