Energy management: How AI is changing the electrical industry
The energy transition means a profound change that requires new, intelligent solutions. Digitization provides the necessary tools – for example in the form of artificial intelligence (AI).
It is intended to help meet the high demands of a more climate-friendly and efficient energy system. The possibilities for the use of AI systems range from smart grids to building automation in the home. In fact, there is hardly an area of the energy sector where AI cannot provide meaningful support.
The transformation of the energy sector is digital
Digital tools for the energy market
They can be found in all areas of the energy system, for example
• as smart grid components such as smart meters or control units for energy flows;
• in the communication infrastructure to be able to process data from the smart meters;
• in data analysis, from signal and condition analyses to system monitoring and the evaluation of consumer data;
• in automating load management, storage management, and other functions.
Artificial intelligence plays a key role in this. There is no question that the new energy system is becoming much more complex and its management is becoming increasingly important: Data must be collected, analyzed and converted into decisions in order to ensure a stable energy supply and to use electricity as efficiently as possible.
Requirements for the modern energy supply
How can energy consumption and energy generation be synchronized in time and place? In other words, how can we ensure that renewable electricity is available in the right place at the right time?
Managing the energy sector with AI
The answers to these questions are becoming increasingly complex in an energy system that is to be largely based on renewables. For example, more external influences that can affect power generation must be taken into account – such as the changing day-and-night cycle or the weather. At the same time, it is important to cover growing energy consumption. By 2040, the global figure could be almost 27 percent higher than in 2017, estimates the International Energy Agency (IEA).
This means that energy flows must be controlled faster and more flexibly to optimize energy consumption. This is only possible with a number of different data. Grid utilization, consumption, storage capacities, weather conditions and much more must be considered in context. This makes the decisions a complex matter.
Data analysis for a clear picture
Building automation with artifical itelligence
Of course, energy management and artificial intelligence are not only important for the energy transition at the level of the power grids. In the building sector, they perform the same functions and ensure greater efficiency.
Regardless of whether it is a private home or a functional building for trade and industry. After all, electricity can be saved everywhere or distributed more efficiently. But how exactly does the AI do it?
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Networked energy management
Networking in energy management does not just mean that the technical systems in a building correspond with each other. Rather, it is also about linking energy and process data in a meaningful way.
This connection does not only provide answers to questions about the consumption (of machines or electrical devices) but can directly analyze the energy efficiency of the running processes. The prerequisite for this is that the corresponding data must be supplied.
The Internet of Things (IoT) is therefore the most important basis for intelligent building automation and the associated energy management.
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Forecasts & Analyses
In fact, comprehensive building automation can take into account far more details than energy and process data. A digital recording of a building opens up far-reaching possibilities in this respect:
Room sensors, in combination with occupancy protocols, can help to better adapt the heating or cooling of the rooms to their use.
Irregularities or defects of energy consumers in the building can be identified and analyzed.
Based on all this data, the AI can make predictions about when which consumers need how much energy.
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Flexible KI
Just as at the power grid level, the same applies to energy management for buildings: Decisions must be made quickly and flexibly. This is all the more true if the energy demand is at least partially covered by self-produced, renewable electricity.
Despite the complex requirements, the AI maintains a better overview and can not only automatically evaluate the data, but also automatically take over the control on the basis of it.
This makes it possible, for example, to avoid peak loads during the operation of systems and devices, because the demand can be adapted to the available energy – for significantly greater energy efficiency.
More possibilities with artificial intelligence
Through the use of AI, the application possibilities of energy management can be expanded even further. Classic fields for energy management include load management and – especially in connection with renewable energies – storage management. Charging management for e-vehicles, both in the private and commercial sectors, is also becoming increasingly important.
AI solutions for the future of building automation
However, building automation for greater energy efficiency means more than just the intelligent control of energy flows. With sufficient data and artificial intelligence, sector coupling can already be implemented at the building level. What does that look like? For example, like this:
• Based on data from room sensors, the AI recognizes which rooms are used and which are not. CO2 sensors are just as capable of this as motion or presence sensors.
• At the same time, the AI receives information about where for example process heat is currently generated in a production facility.
• By linking such data, energy – in this case heat – can be used more efficiently.
AI Solutions for Efficient Building Cooling
• Latent heat storage systems, for example, use materials that can absorb and store heat by changing their physical state. This cools the room as temperatures rise. When the temperature drops again, the syste, releases the stored heat again. As a result, also temperature fluctuations are reduced, so that the internal temperature remains at the same level throughout.
• Another example are heat pumps, which are not only efficient for heating. At the same time, they are ideal for cooling purposes and if necessary, dissipate excess heat from the building to the environment.
• In both cases artificial intelligence can be used to detect temperature differences at an early stage and initiate the appropriate measures. Constant checks on the room thermometer and manual settings on the thermostat are therefore no longer necessary – the building technology regulates the room temperature completely automatically.
With the examples presented, the potential of artificial intelligence in energy management is far from exhausted. Application opportunities await along the entire value chain. From generation to trading to billing, AI applications can help make the energy sector more efficient.
