Artificial intelligence in energy and utilities sector can take many forms, but they are all universally aimed at optimizing the industry for contemporary challenges. For example, energy suppliers can discover failures before they lead to failures and hazards. And that’s what you can do with artificial intelligence.

New technologies aim to collect, synthesize, and analyze significant volumes of data to design a new system that can make timely decisions on intelligent energy allocation. Energy software providers, in turn, stand out in the design and implementation of frameworks for applying AI and ML solutions to existing energy projects. Their use cases can range from marketing strategies for renewable technology companies to predictive analytics models.

Smart City applications, based on exploiting the potential of IoT technologies associated with 5G network infrastructure and blockchain, they can be the turning point for making many emission-intensive sectors, such as energy, transport, manufacturing, and construction, more innovative and sustainable.

5G, in fact, today is one of the most efficient digital tools that companies can rely on to accelerate the decarbonization process. With the increasingly rapid technological development, there has been a substantial increase in mobile network traffic compared to the past.

With the most recent awareness of environmental issues, the new network not only aims to support the growing demand for data but also to help limit energy use. The key point, in fact, lies in the radio access network (RAN), where most of the energy consumption of mobile networks takes place.

Key benefits of AI in the energy sector

Some of the possible applications of AI in energy include, but are not limited to, smart grids, data digitization, forecasting, and more advanced resource management. Summarizing some significant advantages of AI in the energy sector:

• Digitization of data. AI has played a key role in the process of digitizing the energy sector in recent years. AI can help transform energy companies by automating network data collection and implementing analytics frameworks. With the vast amount of data existing in the power industry, converting it into reusable information for AI and Machine Learning algorithms is an option to choose from.
• Smart prediction. Even when it comes to renewable energy, forecasts are widely used to accurately determine energy production in particular geographical areas. Deep Learning AI algorithms have a greater predictive ability than all industry specialists put together. Forecasts can take various forms, ranging from forecasting demand and price trends to identify potential growth areas.
• Predictive analysis for renewable sources. Predictive analyses for renewable energies include identifying areas with the highest potential for artificial intelligence in the development of renewable energies, such as wind and solar panels. With an all-around analysis of the subject, suppliers can use artificial intelligence in energy production efficiently.
• Resource management. Artificial intelligence in energy and utilities relies heavily on the control, support, and supply of uninterrupted electricity. With AI-based resource management, suppliers can balance the proportions of traditional and renewable energy. Proper resource management can also optimize the network for optimal use or require maintenance in critical situations.

Looking at new energy costs every Mobile Network Operator has a plan to implement and manage mobile networks to break the growing behavior of energy consumption. Thanks to our know-how we work together with our customers to optimize the entire innovation process with “plug&play” solutions, contact us to learn more!