Hitachi Energy debuts Nostradamus AI, which delivers fast, accurate renewable energy forecasts, load predictions, and market pricing insights.
Nostradamus AI is one of the first AI forecasting tools designed specifically for the energy sector. Hitachi says it offers forecasts that are over 20% more accurate than some industry targets. It helps optimize energy investments, improve trading strategies, streamline operations, and ensure regulatory compliance. Hitachi Energy’s tool integrates grid performance data, market forecasts, and asset monitoring, all powered by machine learning to provide reliable forecasts.
Outdated forecasting methods are often costly, limited, and labor-intensive. Nostradamus AI aims to change that by enabling more accurate data processing and better decision-making, which is crucial for managing renewable energy sources and growing energy demands.
Hitachi Energy’s AI tool is cloud-native, scalable, and flexible, capable of handling predictions ranging from a single wind turbine or solar farm to thousands of energy load points. It’s algorithm-agnostic and today supports several of the highest-performing AI models, making deployment and scaling straightforward for energy operators, traders, and developers.
“Embracing AI in the power sector is not just a choice; it is an imperative. That’s why it’s critical that organizations have a dedicated tool designed specifically for analyzing the massive amount of data generated across an evolving power grid,” said Andy Howell, Hitachi Energy’s global head of enterprise software solutions product group, business unit grid automation.
In November 2022, Electrek reported on Hitachi Energy’s launch of wireless Spark Prevention Unit indicators that help prevent wildfires by enabling remote monitoring. The company is investing billions to expand and upgrade the electricity grid in multiple countries, including the SunZia project in the US.
What do you think about the use of AI forecasting tools in the energy sector? Let us know in the comments section below.
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