The most important concepts that have come into our lives with digital transformation are unquestionably artificial intelligence and machine learning. While these technologies are developing at a stunning pace, they are transforming many sectors, and one of the main areas of activity is the energy industry. Many energy companies that have been successful in catching up are prioritizing investments in artificial intelligence and machine learning technologies. They take actions that inspire the world by using AI technologies in production, business processes, procurement, health and safety at work.
But how did artificial intelligence and machine learning technologies find their way into the energy industry? Where are these technologies most used? Which advantages do they offer? Let's examine what these innovative methods have brought to the energy industry.
Renewable Energy Solutions
Renewable energies are increasing their share of energy production every day. According to the latest report published by the global renewable energy community REN21, the total installed renewable energy capacity globally grew by 11% in 2021, reaching approximately 3,146 GW. But the ratio needs to increase further to achieve the net-zero target by 2050. At this point, artificial intelligence can serve as a guide to energy companies. How?
Artificial intelligence can create an intelligent coordination network during renewable energy generation, transmission, and use. Monitoring data from these processes provides insights that help to model and predict potential outcomes over time. This ensures that activities are conducted with the lowest possible margin of error.
The contributions of AI to network operations and optimizations are too important to be underestimated. Various outages and interruptions can happen on the grid while electricity is sent to users. Especially in renewable energy, it is complicated to estimate the electricity generation capacity of the grid due to factors such as wind and sunlight. However, AI algorithms make it possible to measure the network's voltage, current, and frequency in real time.
Early Detection of Risks
Energy activities require intense security and monitoring, from operational processes to field exploration and procurement. Identifying systems requiring maintenance and repair, optimizing devices and equipment, and ensuring employee safety requires intense effort. These processes can be monitored via sensors and become predictable through artificial intelligence and machine learning algorithms. As a result, while offering high safety against process risks, maintenance, and repair costs are reduced.
Artificial Intelligence Support in Emission Reduction
The energy industry is inherently an emissions-intensive industry. Therefore, it has a large share in reducing greenhouse gas emissions. Many companies prioritize monitoring their environmental impacts while meeting their responsibilities to nature. For this reason, projects using artificial intelligence technologies are highlighted in emissions measurements. Drones equipped with artificial intelligence technologies enable the simultaneous detection and measurement of greenhouse gas emissions.
Machine learning makes another contribution. These intelligent technologies allow the analysis of current and historical meteorological data so that the operating time of energy systems, especially in renewable energy production, can be managed with accurate forecasts.
Optimizing Geothermal Energy
One of the difficulties encountered in geothermal energy production is continuously monitoring downhole pressures and temperatures. With artificial intelligence and machine learning, that monitoring can be done at a much lower cost and more efficiently. The pressure and temperature in the liquid tank can be monitored simultaneously by algorithms created by these intelligent technologies. This allows production scenarios in geothermal facilities to be predicted in advance with consistent data.
Our AI Project in Diesel Production
With our “T95 Diesel Optimization” project, we aimed to use machine learning techniques to continuously monitor the T95 value, which determines the quality of the diesel product we produce in our facilities. This allowed us to minimize the margin of error, capture data in real-time, and provide quick and analytical solutions during production.
The T95 value is a standard for product quality and regulatory optimization. In traditional methods, this value is managed manually by examining the samples taken from diesel production pools every 8 hours, applying the experience and knowledge of the personnel in charge, and various trial-and-error methods are used. However, with the optimization model created by our Digital Transformation team using advanced analytics and machine learning techniques, we can collect data simultaneously in production, run simulations by processing this data, and directly offer our staff the improvements they can make to the system. The bottom line is that we are saving a lot of time and cost.
AI in the energy sector has allowed energy companies to analyze the data they need. Many risks have also become predictable through the monitoring and storing of generated data and algorithms. On the other hand, it has become possible to optimise processes with maximum efficiency and minimal cost. With these developments, it is inevitable that artificial intelligence technologies, particularly in the energy sector, will increase their importance in many industries in the future.