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From forecasting energy consumption to monitoring smart grids, AI is changing the way we work with energy, and helping to move us toward a more efficient and sustainable future.
According to the IEA, in 2022 the industry sector was directly responsible for emitting 9 Gt of CO2, accounting for a quarter of global energy system CO2 emissions – and that’s not including indirect emissions from electricity used for industrial processes. To be on target to achieve Net Zero by 2050, as set out in the Paris Agreement, industrial emissions will need to fall to about 7 Gt CO2 by 2030.
With AI technology becoming more accessible than ever, operators of energy-intensive industrial processing plants are particularly well positioned to harness its power to turn their data into tangible results, such as the optimization of energy consumption to reduce carbon emissions and operational costs. In fact, a recent research by McKinsey & Company showed that those already leveraging AI in their industrial processing plants have reported a 10-15% increase in production and a 4-5% increase in EBITA.
But while nearly 94% of industrial business leaders believe digital transformation will greatly impact their operations in the next few years, the shift to new technologies is still relatively slow. It seems the post-pandemic economic recovery has created an atmosphere of caution when it comes to long-term investment in upgrading operations. Yet, the fact remains – digital transformation and the further adoption of the latest technology is a critical piece of the sustainability puzzle.
So, how can these energy-intensive industrial businesses leverage digital technology to make the most of their data, and achieve significant improvements in their energy efficiency in the short-term?
Unlocking the power of Predictive AI to optimize energy consumption
Reducing plant energy consumption is a great way to reduce costs, carbon emissions, and waste.
Generally, 50% of a plant’s energy is used in the production process and this is typically one of the first things that is optimized by maximizing production throughput, yield quality, and overall efficiency. The remaining energy is consumed is by plant utility systems, such as chilled water production for plant cooling, or steam generation and use for process heating. Optimizing utility energy use is another excellent opportunity for significant energy and cost savings, yet one that is still vastly untapped in the industrial sector.
In another of my recent blogs, we took a look at the huge upsurge in the use of advanced analytics software, and how, when teamed with AI technology such as Machine Learning (ML), predictive models can quickly turn the large volumes of data produced by connected technologies (the IIoT) into powerful recommendations for operational improvements. In the context of energy, consumption data can be monitored and collected across the process, and predictive analytics ML models then used to highlight anomalies, recommend actionable insights for optimization, and forecast future energy use patterns.
One of the AI-powered solutions we now offer at Schneider Electric,EcoStruxure Industrial Advisor – Predictive Energy, does exactly that – employing predictive learning ML models to optimize the energy consumption of plant utility systems.
Leveraging the CONNECT Ecosystem and a part of our innovative EcoStruxure Industrial Advisor range, EcoStruxure Industrial Advisor – Predictive Energy follows five distinct steps to do this simply and securely:
EcoStruxure Industrial Advisor – Predictive Energy has been specifically designed to help energy-intensive industrial companies optimize energy use in their plant utility systems without impacting the core functions of their manufacturing process.
The plants expert workforce can be empowered during this optimization process, using their valuable expertise to review and validate the AI-recommended settings prior to executing any changes. Once satisfied the settings are performing as expected, they can also enable the advisor to work autonomously, if desired.
Because it can be quickly deployed across multiple types of equipment, EcoStruxure Industrial Advisor – Predictive Energy offers a system-level perspective, capturing utility energy use data enterprise-wide. This energy consumption can be reduced by as much as 10% and associated carbon emissions by up to 40%.
Our team of expert consultants in automation, analytics, and AI technology customize the solution to achieve the best outcome for every customer. We can provide rapid ROI, with payback in less than 3 months, and give reliable pre-rollout energy saving estimates.
The results of using EcoStruxure Industrial Advisor – Predictive Energy speak for themselves. One customer in the semiconductor industry has achieved some very impressive outcomes:
- $1M US in energy savings and 10,000 tons of carbon emissions reduced per plant, per year.
- The customer is on track to meet sustainability targets and decrease their emissions by 40%.
- ROI for the first site was achieved in less than 6 months and the solution is now being deployed in other plants.
Schneider Electric is committed to helping our customers optimize their energy efficiency and contribute to Net Zero by 2050
Reducing energy consumption and optimizing industrial operations for energy efficiency is a big part of achieving these ambitious climate action goals.
EcoStruxure Industrial Advisor – Predictive Energy is a great example of how to leverage digital technologies such as IIoT and AI Machine Learning to drive the efficient use of energy, accelerate decarbonization, reduce costs, and improve sustainability in energy-intensive industries. Taking the steps to adopt these kinds of digital technologies now is key in achieving sustainable industries, and a more sustainable world overall, and it really is a case of the sooner, the better.
Want to know more?
- Browse the EcoStruxure Industrial Advisor – Predictive Energy Brochure
- Find out more about our EcoStruxure Industrial Advisor range
- Learn about the CONNECT Ecosystem
- Read the AI & Sustainability Report ( Artificial Intelligence for Energy Transition)
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