AI is transforming demand-side management

We're at a crossroads in the world of energy. The landscape is shifting with the increasing role of renewables, growing demand and the need for resilience against extreme weather. How do we manage power effectively to keep the grid stable and efficient? Using AI to manage demand is one possibility. The role of artificial intelligence in energy management is an exciting development. It's set to transform how we predict, price, trade and use power, all while boosting efficiency and reliability.

Managing the grid is like solving a complex puzzle in real-time. The old grid, built for predictable loads, now grapples with erratic consumption and the fickleness of renewables like solar and wind. AI steps in here, using data and machine learning to improve efficiency and strengthen the grid. AI outperforms traditional models in forecasting. While these conventional models are valuable, they often miss the finer details which can lead to forecast errors. AI, on the other hand, adapts rapidly to real-time changes, enhancing the predictability of supply and demand at a detailed level.

For the first Interchange episode of the year, David Banmiller welcomes David Miller from Gridmatic to discuss the ever-evolving use of AI in grid management.

Together they explore how AI is transforming strategic forecasting, risk management and optimisation in energy infrastructure. What are the current challenges for the grid and how could AI help? What investment is required in infrastructure to optimise the grid? And what are the regulatory measures in place that are helping and hindering the rollout of smart grids?

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