In recent years, artificial intelligence (AI) has gained prominence in a wide variety of industries. Most recently, AI technologies have developed in the energy sector and hold great potential for future energy system design.

rom demand forecasting to asset maintenance, the application of AI in the energy sector could bring gains on many fronts.

Digitization of data

One of the prerequisites for increased use of AI in the energy system is the digitization of the sector. This is because energy companies have a lot of data to manage. With the help of AI, they can store, process, and manage data more quickly and cost-effectively.

The implementation of innovative technology can help the energy sector become more competitive in unstable economic conditions and develop better operational methods than those currently available.

In addition, AI data management can reveal new insights that can change the way the industry operates.

Key applications of AI in the energy sector

• Predictive analysis

Innovative AI technologies enable energy companies to improve their predictive analytics methods to reduce costs and prepare for changing conditions.  Indeed, with the help of machine learning and deep learning, it is now possible to accurately predict climate change and capacity levels.

Prior to the exploitation of AI, most forecasting techniques relied on individual weather models that provided a narrow view of the variables that affect renewable energy availability. Now, AI programs have been developed combining self-learning weather models, historical weather datasets, real-time measurements, sensor networks and cloud information derived from satellite images.

With an increasingly large data set available, forecasts can now go far beyond the weather to train algorithms to predict more remarkable outcomes.

• Reduce maintenance costs

The cost of error is high in the energy sector, so avoiding it or predicting it early saves maintenance costs and limits machine downtime.

AI algorithms can automatically detect disturbances in real time of a mechanical failure, improving the reliability and efficiency of the power system.

Metroscope is an example of the successful adoption of artificial intelligence in the energy market. This startup detects early on the hazards that may affect thermal circuits in order to solve them as quickly as possible.

With these intelligent AI-based predictive mechanisms, energy providers will be able to better allocate their resources.

• Facilitate energy storage

As the amount of energy to be stored continues to grow, new management systems are needed. Artificial intelligence can help industry players optimize their energy storage.

Renewable energy storage is quite problematic, as the production of this energy is periodic. Uniting renewable energy with AI-powered storage can greatly facilitate energy storage management, increase business value, and minimize power losses.

IA a technology that is itself energy-intensive

Processing large amounts of data consumes a lot of electricity. When using AI for energy system transformation, it is crucial to also analyze how to design data centers that are themselves energy efficient and as climate neutral as possible. Possible solutions to this dilemma include the physical proximity of data centers and renewable energy generation plants and the postponement of energy-intensive IT operations to times when plenty of power is available.

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