Philippe Rambach, Chief Artificial Intelligence Officer at Schneider Electric
In the wake of significant advancements in Generative AI (GenAI), there is a feeling that GenAI is present everywhere. Both the media and analysts have extensively covered its myriad applications in business, highlighting its untapped potential. Yet, the promised transformation is only unfolding gradually. Why is this? Responsible AI implementation requires a meticulous approach to mitigating risks associated with ethics, sustainability, and cybersecurity from the outset. Additionally, it is crucial to recognise that while GenAI presents new possibilities, it does not encompass all the diverse landscape of existing AI technologies.
Prior to deploying GenAI at scale, companies must make strategic decisions and undergo organisational changes. At Schneider Electric, we proactively launched the AI Hub even before GenAI hit the market. Despite this early initiative, we prioritised the establishment of a comprehensive review process involving internal and external stakeholders to identify early application opportunities for this cutting-edge technology. Importantly, most of these applications align with the core mission of our company – to make a positive impact on the planet by empowering our customers and partners on their sustainability journey.
Fulfilling ESG Commitments: A Spectrum of AI Solutions
In the recently launched Resource Advisor Copilot, we employ GenAI-based Natural Language Processing (NLP) technology to offer a convenient digital companion. Through a chat interface, you can ask Copilot to retrieve real-time data, perform enhanced analytics and visualisation, or tap into our industry knowledge and system information to provide decision support and optimisation hints. With it, we wanted to help customers move toward carbon neutrality by providing answers to their questions like:
- What are the total scope 1 emissions for our US site over the past six months?
- What does scope 3 emissions mean?
- How much did we spend on electric power in 2022?
- Can you detail a decarbonization plan to achieve my emissions goals in the next five years?
But GenAI is not the only AI technology that can support companies with their ESG commitments. Let’s take a look at some others.
Machine learning
Machine learning (ML) allows us to analyse large datasets, identify patterns, and make predictions. It can also automate predictive analysis and make data-based decisions quicker. ML directly supports the transition to renewable energy — one of the most effective decarbonisation strategies. It does this notably by working on the demand side of the demand/supply equation. It enables to optimise the usage of renewable energy by analysing multiple data sources and detecting inefficiencies to optimise energy usage.
For example, Ecostruxure Microgrid Advisor software connects to your distributed energy resources. It automatically forecasts and optimises how and when to consume, produce, and store energy. With this solution, our customers like Citycon shopping centre in Lippulaiva got closer to net zero. Here we used a ML algorithm that constantly analyses data from energy generators, EV charging stations, batteries, back-up generators, HVAC systems, lighting systems, UPS, combined heat, and power (CHP), and utility metering.
Deep Learning
Deep Learning (DL) is a subset of ML. It uses neural networks with multiple layers to extract complex features from data. DL works in various domains, like demand response optimisation, renewable energy forecasting, and energy grid management. These areas are seeing the biggest progress in AI industrialisation.
DL can help optimise energy use in a new or a newly instrumented building with limited or low-quality data available at the start. In a recent paper titled “Cold Start Methods for Building’s Energy Consumption Forecasting”, our data scientists worked with IMT Atlantique to apply DL in Smart Energy Management Systems (SEMS) — reducing energy waste in the early building deployment phase. SEMS are crucial for minimising emissions, optimising energy consumption in buildings, and creating management strategies. The key to unlocking their value is accurate forecasting.
How can you train forecasting models for real estate development with little data? A Cold Start approach uses historical data from buildings with similar characteristics — before applying this knowledge to the new task. The result is reduced energy waste from the get-go. This is good for the environment, saves money, and makes buildings more valuable.
Creating responsible AI solutions
When assessing the environmental impact of AI, it is crucial to recognise that traditional ML and DL often demand less computation that GenAI. This results in substantial energy savings. However, determining whether AI is truly beneficial for the planet hinges on the specifics of each use case adopted by businesses and organisations. Our guiding principles are rooted in the belief that the energy saved through AI implementation must surpass the energy required to power the AI models. It is essential to prioritise energy efficiency by using a dedicated decision framework that is crafted for such considerations. What is more, this permits us to uphold the pillars of responsibility, ethics, and meaningful purpose in our pursuit of advancing AI technology.
Author Bio:
Philippe Rambach is the SVP, Chief Artificial Intelligence Officer at Schneider Electric. He joined Schneider Electric in 2010, and has more than two decades of experience in strategy, innovation, and business responsibility in many industries.
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