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ABB has partnered with California-based artificial intelligence firm Verdigris Technologies to help owners and operators of commercial and industrial buildings to optimise their energy management.

Verdigris Technologies is bringing its new machine-learning applications to ABB’s products to predict unplanned surges in power consumption for building owners.

Read more about energy efficiency here

ABB is launching a new digital energy-app store and Verdigris’s AI technology is their first app.

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The app has outperformed common open-source energy demand forecasting platforms in studies conducted by ABB and Verdigris.

Such technologies with connected sensor networks and advanced monitoring and analytics combined with adaptive and autonomous controls can save a wasted 3.31 quads of energy or $43 billion annually, according to curbing 270 million metric tons of greenhouse gas emissions annually by 2030 in just US Commercial Building stock alone.

Jonathan Chu, Verdigris CTO said: “DOE Studies show with accurate sensing and predictive analytics we can reduce building energy consumption by up to 30%. With AI we have the building blocks to go further in developing adaptive and autonomous buildings, and eventually optimise networked fleets of generators, energy storage systems, EV infrastructure and smart buildings.”

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Mark Chung, Verdigris CEO said: “We chose to partner with ABB because they are the leading supplier of electrification products to our core customer segments. This collaboration represents an avenue to accelerate our objectives of meeting growing customer demand for energy resilience and delivering a global-scale coordinated response to the threat of climate change through responsive energy intelligence.”

Andrea Temporiti, Digital Leader for ABB’s Electrification business, said: “Our use of AI to help customers make better energy management decisions demonstrates ABB’s commitment to innovation in our products and quality in our services. With the new Energy Forecasting and Smart Alerts apps, AI drills down into the facility’s power data to pinpoint actionable opportunities for productivity improvements and energy cost savings.”