In 1882, Thomas Edison opened the world's first hydroelectric power plant in America at Pearl Street Station in lower Manhattan to deliver power to 59 customers. The customer base has since skyrocketed to hundreds of millions of users, but the overall structure is yet to receive a mo
In 1882, Thomas Edison opened the world's first hydroelectric power plant in America at Pearl Street Station in lower Manhattan to deliver power to 59 customers. The customer base has since skyrocketed to hundreds of millions of users, but the overall structure is yet to receive a modern overhaul. It consists of a vast network of power plants, transmission lines, and distribution centers.
An additional challenge is the rise of distributed generation, where private users generate and use their own electricity from renewable sources, such as wind and solar. This complicates supply and demand and forces utility companies to buy excess energy from private users, who generate more electricity than they use and send the excess energy back to the grid. Since early 2010, solar use has more than tripled, and this trend is poised to continue into the future as photovoltaic cells, the devices that generate electricity from sunlight, decrease in cost and increase in efficiency.
It's no secret that the energy industry is facing a major disruption. Not only are established companies like Shell and Exxon Mobil investing in AI, but startups across a wide range of fields are also looking for ways to use AI to help solve the world’s energy problems. The reasons are clear: AI offers significant benefits over existing technologies, including greater accuracy, efficiency, and predictive power.
It is predicted that by 2040, artificial intelligence (AI) will account for more than 75% of all demand in the energy sector.
AI in the Energy Sector can help evaluate, analyze, and control the data of the various participants (consumers, producers, storage facilities) connected to each other via the grid.
In particular, AI is present in the field of intelligent networking of electricity consumers and generators across sector boundaries. With the increasing decentralization and digitalization of the power grid, it is becoming more difficult to manage the large number of grid participants and keep the grid in balance. This requires evaluating and analyzing a flood of data. Artificial Intelligence helps process this data as quickly and efficiently as possible.
Smart grids are another area of application. These networks transport not only electricity but also data. Especially with an increasing number of volatile power generation plants such as solar and wind, it is becoming more and more important for power generation to react intelligently to consumption (and vice versa). AI can help evaluate, analyze, and control the data of the various participants (consumers, producers, storage facilities) connected to each other via the grid.
In addition, the AI can stabilize the power grid by, for example, detecting anomalies in generation, consumption, or transmission in near real time, and then develop suitable solutions. Initial research projects in this field, such as at the Fraunhofer Institute, are already underway.
Further, AI can help coordinate maintenance work and determine optimal times for the maintenance of networks or individual systems. This helps minimize costs and loss of profit as well as disturbances of the network operation.
With the global energy system not immune to the COVID-19 pandemic, renewed emphasis has been placed on upping economic efficiency. To that end, market players are using machine learning to improve predicative capabilities, increase transparency in energy trading, integrate renewable energy sources, manage smart grids and storage, and give life to unmanned drones.
The convergence of strong AI and the energy sector will have dramatic and sweeping impacts for global consumers.
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