Big data and AI

By year 2025, over 75 billion devices will be connected by IoT and thus offer a vast outlet to obtain and consume data in the power sector. The use of AI is the most useful tool for decision making in complex systems with massive amounts of data, where more traditional data analysis tools may struggle to define actionable insights. As the power sector becomes increasingly interconnected, AI is needed to intelligently manage systems and derive value from data, as AI algorithms have the ability to produce more precise models. Most of the advances currently deployed in the field have been quite limited to renewable power generation forecasting and in predictive maintenance.

  • Self-learned weather models and renewable generation forecasting technology can integrate large datasets of historical and real-time data from local weather stations, sensor networks and sky imaging. Accurate power forecasting at shorter time scales can help generator and market players to better forecast their output and to bid in the wholesale and balancing market. This increased dispatch agility can therefore reduce the operating reserve required in the power grid.

  • AI can also improve safety, reliability, and efficiency in the power system by automatically detecting disturbances in the grid. AI models with historical and repetitive system outage data can gradually learn to distinguish between normal operating data and system malfunctions.

  • AI can predict not only network load but also consumption habits, which is even more relevant in the current deployment of DERs, such as in solar PV panels, electric vehicles, and heating systems, which change the traditional load shape drastically. Understanding the consumer end habits, value and motivation can greatly predict demand and thus bolster the balancing effectiveness of the smart grid.

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