Artificial intelligence, or AI, could one day lead to power plants that run themselves. These smart power plants would use AI to make decisions and operate without much human help. Knowledge graphs, which are like big charts of information, are very important for making this idea work. They help the AI understand lots of data about the power plant. This is indeed a rise of Data Science in energy making industries. Some smart applications for power plants using AI have already been made. They show that this technology can bring big advantages.
For example, it can make power plants more reliable and efficient. It can also help find problems before they get serious. This means power plants can work better and use less fuel, which is good for the planet. AI can also make power plants safer because it can watch for dangers all the time. This is just the start, and there’s a lot more that AI can do for power plants in the future.
Imagine a situation where a pump and a valve are situated near each other inside a power station. They don’t work together directly, but if they both stop working at the same time, it’s likely not just by chance. There could be a hidden problem that the power station’s usual monitoring systems haven’t noticed. Maybe there’s a small leak in a pipe causing water damage, or a fire starting that hasn’t been detected by the smoke alarms yet.
An artificial intelligence (AI) system in power plant could suspect this and send an alert to the emergency team. This would only happen if the AI had been trained with a map of knowledge about the power station. This map, called a knowledge graph, is a special kind of database that a computer can understand. It doesn’t just list what each part of the power station does; it also shows how all the parts are linked, even the links that don’t do anything. For the pump mentioned before, this map would include information about everything else that’s nearby.
Jan Weustink believes that knowledge graphs as a powerful tool in AI for managing a power plant. They serve as the foundation for creating an autonomous guide for intricate, vast power stations. Such a guide demands the use of artificial intelligence for its operation. Training an AI to manage a whole power station in one go is challenging, unlike instructing humans. Weustink, a strategist and technologist at Siemens Energy, suggests a more manageable approach. He proposes dividing the power station into smaller, understandable segments with the help of knowledge graphs.
Dividing the power station into smaller parts, could make each segment accessible to machines. Afterward, these segments can be overseen by interconnected artificial intelligence systems, ensuring efficient operation. This method simplifies the complex process, making it easier to implement AI in controlling large-scale energy systems. So Data Science can do it’s job the best way. It’s a step towards the future where technology and human expertise combine to enhance the reliability and efficiency of power distribution. By breaking down the power station into smaller parts, AI can learn and adapt more effectively, leading to smarter energy solutions for power plants.
This innovative approach by Weustink and his team at Siemens Energy could revolutionize how we manage and distribute power, making it safer and more sustainable for everyone. It’s a clear example of how visionary thinking and technological advancement can reshape industries and improve our world.
Today’s technology for creating knowledge graphs in AI systems is not advanced enough to make self-running systems for big power plants in energy industry. Making these maps takes a lot of hard work. Also, we still need to make some important tools that can ask questions and get answers from these graphs. There are parts in making these tools that Mr. Weustink can’t do alone. But, he doesn’t have to do it alone. This is because knowledge graphs are useful for more than just self-running systems. They can help make better designs for new power plants and help with their upkeep. Mr. Weustink has many friends at Siemens Energy who are just as excited as he is to use knowledge graphs for power plants.
Exploring the workings of a power station is like taking a journey into a vast and intricate world. Imagine a map that shows how different parts of a city are connected, but instead of roads and bridges, there are streams of data linking various sections of the power station. These connections form a network as complex as the web of neurons in our brains.
In a section of a power station where gas and steam work together to generate electricity, you can find around 10,000 pieces, each playing a role in this energy-producing puzzle. They’re all joined by over 50,000 links that help them communicate and function as one big, efficient machine. When you think about the whole power station, the numbers grow even bigger, reaching into millions of pieces and connections.
This massive network is not just a bunch of wires and numbers; it’s a living, breathing system. It’s like a symphony where every instrument must be in tune, and every musician must know their part. The power station’s ‘music’ is the electricity that powers our homes, schools, and offices. And just like a symphony, it takes a lot of practice, coordination, and knowledge to make sure the performance goes smoothly. Here it goes for the knowledge graph that works as a tool of Data Science.
Understanding this complex system is crucial because it helps us keep the lights on and our lives running without a hitch. It’s a fascinating world, full of challenges and wonders, and it’s essential for our everyday life. So next time you switch on a light or charge your phone, remember the incredible journey of energy that’s happening behind the scenes at the power station.
Saskia Soller, brimming with enthusiasm, is a dedicated engineer with a focus on energy and environmental engineering. She, like her colleague Weustink, thrives on the intricate challenges presented by digitalization. Over the past few years, Saskia and her team have been hard at work crafting a system that merges data using knowledge graphs. This system gathers data from various places, infuses it with significance, and connects it to other information that carries the same significance. Moreover, it streamlines and unifies the way this information is accessed and viewed. As a result, the information becomes easily searchable by people using simple queries similar to those on Google, and it’s also structured in a way that allows computer programs to analyze it automatically. This AI powered system could lead to an autonomous power plant in near future.
Siemens Energy has created an internal database that consolidates information from 50 different power station projects. This database draws from 12 unique data sources and utilizes a data integration system to organize the information effectively. The knowledge graph, which is a part of this system, currently holds an impressive total of around 500 million individual pieces of data. Soller, an expert in the field, has shared that this vast collection of data points is a significant feature of the database.
More than a thousand users have access to this database, and they have expressed their satisfaction with its performance. They particularly appreciate how the unified data presentation allows them to find answers to intricate queries much faster than they could with older databases. Weustink, who is particularly enthusiastic about the database, has praised it as truly outstanding. He is keen on connecting to this database through his autopilot system to integrate the data he collects into the broader database network that could be used by AI for a self-running power planets.
The future holds great promise for the autopilot system, but Weustink must wait a bit longer to see his dream become true with the help of knowledge graphs and Data Science practices. Today, it’s achievable to create an artificial intelligence (AI) system that can pinpoint a local problem when a pump and a valve fail at the same time, thanks to the system that combines data in a complex environments like power plants.
Yet, Weustink points out that this is not sufficient for the autopilot to function fully. To make the autopilot work perfectly, it’s necessary to feed more data and more details into the knowledge graphs. This includes information from the system that controls operations, the setup of electrical and pipeline networks, and the history of their upkeep and repairs. By adding this information, the autopilot will have a comprehensive understanding of the system, allowing it to make smarter decisions and operate more efficiently. This advancement in technology will not only enhance the reliability of the systems but also pave the way for more innovative solutions in the future.
Recently, a new system for combining data has been put to use in a project for a customer. This project is about planning the building of a new power station in Hong Kong using computers to connect all the work by the aids of AI. This system is the first step in using a special computer program called Building Information Modeling (BIM). BIM is not just used for designing the outside shape of the building but also for planning where everything inside goes, setting it all up, and making sure it works – this means the whole power station.
Now, customers can watch how their building is coming along with great detail. Before, it was hard to be this exact with big projects. Soller mentions, “This new way helps customers keep their building work on time and not spend more money than planned.” This is important because it helps avoid delays and extra costs. It’s like having a detailed map and a clear plan for the journey of building a power station. It’s a big help for everyone involved, aided by the Data Science.
6:30 PM 6/26/2024
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