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Schneider ElectricPhilippe Rambach, SVP and Chief AI Officer (CAIO) at Schneider Electric, explains how cutting edge artificial intelligence and distributed power generation technologies are optimising energy consumption for retail behemoth Lidl and his company’s North American R&D facility.
Store, Sell or Consume?
In the picturesque surroundings of Järvenpää, Finland, a state-of-the-art distribution centre, operated by the German retail giant Lidl, provides a striking example of how artificial intelligence (AI) can combine with distributed power generation to optimise ‘prosumption’—that is, the on-site production and consumption of energy by ‘prosumers’.
Powered entirely by renewable energy, the building’s microgrid incorporates a 1,600-panel solar plant and a huge battery storage system. Excess energy recovered from the centre’s refrigeration facilities helps to heat the water of 500 local homes. Its battery system balances consumption spikes and ensures Lidl and the larger community have greener, more cost-effective energy access.
Growing role of prosumers in the new energy landscape
Crucially, all this is coordinated through a sophisticated software system that leverages real-time data analysis and predictive machine learning algorithms to assist site managers in digitally optimising facility operations. In this sense, Lidl’s Järvenpää warehouse exemplifies AI’s ability to maximise the efficiency with which prosumers create and use energy.
And, as growing numbers of businesses and households turn to low-cost advancements in distributed energy technologies—from rooftop solar panels to small wind turbines—to reduce emissions and energy spend, and gain a degree of energy independence, the importance of AI for prosumers is only set to increase.
Businesses need support making large-scale decisions regarding cost efficiencies—especially as some organizations face closure amid spiralling energy prices. If they had the technology to predict peak energy prices and rely on their own energy production or storage, could they pause usage and resume at a cheaper time?
Leveraging AI to optimise energy consumption for prosumers
The answer is yes—if the prosumers know when those peak price times will happen, and if they can forecast their energy needs, then they can make adjustments to keep the business afloat using just the energy available outside those times. Gaining this knowledge requires a solid data foundation, with AI turning data into deep insights, predictions and trends. With AI’s support, prosumers worldwide can take one step closer to not only solving their own energy cost crises, but climate change itself. The accumulation of many “small” impacts for each prosumer can have a drastic impact on the well-known “energy peak” at the region or country level.
AI already supports prosumers in optimising their usage decisions.
By leveraging up-to-the-minute energy price analysis, AI already supports prosumers in optimising their usage decisions. By crunching historical data, AI can accurately forecast future energy needs and proffer informed recommendations on cost-saving measures like prioritising off-peak consumption.
Unlocking AI-powered supply-side efficiency for prosumers
But besides supporting prosumers in optimising their energy use, AI can also help them maximise the efficiency of their production and supply.
One of the great strengths of modern prosumption is that it’s focused on the production of electricity from renewable sources—which is fundamental to ending our dependence on fossil fuels.
Indeed, electricity is unique in that its cost and environmental friendliness go hand-in-hand. When electricity is cheap, it’s typically emanating from renewable sources. By that same token, during expensive peak times, it’s often carbon heavy.
At a microgrid level, AI already enables prosumers to optimise the moments at which they buy, sell or store the energy they’ve produced, thus prioritising cheaper, more planet-friendly tariffs.
And as technology evolves, AI will empower us to gather and investigate increasingly vast quantities of previously inaccessible and unstructured data. Prosumers—be they households, residential communities, companies, or other entities—need the ability to leverage these insights to their advantage.
[I]nstead of simply collecting data on energy efficiency, prosumers need to adopt a proactive mindset
Infusing AI into the heart of a building
Take buildings, which are the source of some 37 per cent of global carbon emissions. Technologies like smart meters already provide clear visibility into energy consumption and waste in the built environment.
But, moving forward, instead of simply collecting data on energy efficiency, prosumers need to adopt a proactive mindset and ask: “how can I make this data work for me?” Again, AI provides a solution.
In our North American R&D hub in Boston, for example, the facility’s advanced microgrid includes 1,379 solar modules, alongside photovoltaic inverters for on-site power generation.
And by leveraging cloud-based analytics from our EcoStruxture Microgrid Advisor platform, the facility also harnesses weather forecasts and other operational information to optimise energy performance across onsite solar, energy storage, and electric vehicle charging assets.
Consequently, the hub generates over 520,000 kilowatt-hours (kWh) of electricity per year. That’s the equivalent of removing annual greenhouse gas emissions from more than 2,400 passenger vehicles.
All this is to show that AI will be fundamental to ensuring that prosumers get the most out of the power they generate, consume, store, or sell back to the grid.
No other technology empowers us to make impactful, data-driven decisions on efficient energy use in real-time. Given the increasingly urgent planetary crisis we face, it’s time we embrace its full potential.
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