In the energy industry in particular, Artificial Intelligence can be used at many points along the value chain. The use of AI is worthwhile, for example, for the mapping of customer inquiries and tickets, in predictive maintenance or in the field of smart meter services.
AI for Network Operation
Network operators, for example, can take advantage of further developments in the field of Artificial Intelligence: On the basis of AI, they can forecast exactly how busy their network will be. An important subdiscipline of AI is used here, machine learning (ML). Machine learning means that IT systems use algorithms to recognize patterns in data sets and can use the insights gained for new questions and solution strategies. The software "learns" independently and continues to develop autonomously.
Fixed threshold criteria vs. anomaly detection in time series
Until now, many network operators have often relied on systems that operate according to fixed threshold criteria for network monitoring. This means that the systems monitor whether the target variables under consideration are within a predefined range of values. If the company wants to permanently monitor an aspect of the network, such as the load flow, it must define fixed threshold values for this purpose that cover a large number of normal states as a whole. This means that state-specific deviations often do not trigger an alarm. Using AI processes, this monitoring is now much more sensitive and flexible: network operators can resolve the fixed threshold values as far as possible. This is because AI automatically identifies when which utilization is to be expected. This is based on time series measurement, which enables a distinction to be made between type days such as weekdays, weekends or hours and minutes. For example, the network load can be very low on Sunday nights, while peak values occur during the morning and evening hours on weekdays.
Based on the data collected - for example, through smart metering - the grid operator can then investigate and systematize behavioral patterns, while at the same time creating load profiles, such as industrial or residential profiles that take into account photovoltaic generation systems, for example. The expected behavior can then be mapped in fine granularity based on the various influencing variables. This also includes general correlations to the outside temperature or the weather.
The anomalies detected in the network are thus no longer based on previously defined threshold values, but on variable environmental parameters. If the value turns out to be lower or higher than expected, the network operator can react according to demand. The service independently learns the typical behavior of the data series, identifies unusual behavior and alerts critical situations at an early stage - the integrated forecasts are used for further planning.
Hello chatbot: AI in customer communication
Digital customer communication across all channels and at all times - this demand on energy supply companies can also be met with the support of Artificial Intelligence and machine learning. Customers want their questions answered quickly - which in fact include recurring standard information in over 90 percent of cases.
It is precisely these frequently asked questions that can be handled automatically by so-called chatbots. A chatbot is a text-based dialog system that allows chatting with a technical system. With increasing computer power, chatbot systems can access ever more extensive data sets more quickly and therefore provide intelligent dialogs for the user. Such systems are also known as virtual personal assistants, which also provide standard information about the company and products upon request.
Machine learning allows the chatbot to automatically classify queries and recognize similar requests. Requests with similar or identical results can then be fully automated.
Chatbots add value to businesses by making many processes much more efficient. Automating more and more tasks means optimized process costs, fewer human errors and 24/7 availability.
Optimization of effectiveness in power station operation
Companies can also rely on AI support in the operation of power plants with their complex system of components. Often, many different components from different manufacturers are used in the technical plants. These individual assets are optimized for themselves and are operated based on the individual maintenance recommendations of the manufacturers. So far, the control of the power plant thus requires consideration of complex dependencies that are individually introduced by the operator.
In the future, using Artificial Intelligence, ideal switching actions can be derived to most effectively meet the current and perspective energy demand. The operator will be supported in his decisions in order to achieve the economically optimal mode of operation for the power plant.