Energy efficiency is now a top priority for businesses, governments, and individuals alike. With growing concerns over today’s environmental challenges, reducing energy consumption and minimizing carbon footprints are more critical than ever—especially in the context of climate change and the 2050 Net Zero Agenda for sustainable development. Additionally, the European Commission’s Green Deal offers incentives to companies that choose to adopt this environmental protection strategy.
In this landscape, Artificial Intelligence (AI) emerges as a game-changing tool. Its ability to analyze vast amounts of data, learn from it, and provide actionable insights can significantly transform how we use energy. From optimizing industrial processes to enhancing the efficiency of electrical grids, AI is already making a tangible impact, offering energy consumption reductions between 10% and 25%, according to McKinsey &Company.
How can AI optimize energy consumption?
Here are some AI-driven solutions that have proven effective in boosting energy efficiency:
Smart Grids
AI can predict energy supply and demand in real-time by analyzing historical data, leading to more efficient energy usage. One example is Vehicle-to-Grid (V2G) technology, which allows energy sharing between electric vehicles and the grid when vehicles are idle. AI enhances this process, not only improving energy efficiency, but also providing greater stability to the electrical grid.
Predictive maintenance
AI can forecast equipment or facility failures, reducing downtime and improving operational efficiency. The larger the facility or machinery park, the greater AI’s contribution. Through operational research and real-time data analysis, AI enables “prescriptive maintenance” plans, cutting unnecessary costs associated with traditional maintenance approaches like corrective, preventive, and predictive methods. This minimizes energy waste and ensures optimal equipment operation, while also reducing the frequency of maintenance interventions.
Logistics optimization
AI can optimize transportation routes to reduce distances traveled, minimize the number of vehicles needed, or a combination of both, thereby increasing fleet energy efficiency. With current limitations in electric vehicles, such as shorter driving ranges, AI can also help optimize energy consumption by factoring in surrounding conditions and specific journey needs.
How does optimization-oriented AI work?
Beyond the examples mentioned, optimization-focused AI is a versatile tool that can be applied across a wide range of processes and systems to enhance energy efficiency. Given its adaptability, it’s crucial to have a multidisciplinary team of experts guiding the entire process.
Understanding the problem
A clear understanding of the problem at hand is vital. Knowing the appropriate techniques for resolution and understanding each method’s pros, cons, and implementation requirements is essential. The most sophisticated technique isn’t always the best fit; understanding the full spectrum of available techniques is crucial.
From Big Data to Smart Data
To create AI models that truly add value, high-quality data is essential. With numerous often unstructured data sources, separating useful data from irrelevant information can be challenging.
Tailored AI algorithms
The architecture of an AI model depends on the specific use case, the problem, and often the company’s unique needs. Generic models or off-the-shelf solutions may not yield the
best results for specialized problems.
Seamless integration
A robust AI solution must be accessible, integrate smoothly with existing systems and processes, and be easy for the team to use. Integrating these solutions within a company is a critical process that requires careful attention.
Let’s take a look at a specific use case: Opti-AI in a multinational industrial gas production company:
The challenge
Gas production is a highly automated process, where the primary cost is the electricity consumed by the air separation plant equipment.
The goal was to meet demand while minimizing production costs and energy consumption—a complex decision requiring the consideration of factors such as demand forecasts, energy price predictions, storage capacities, planned supply interruptions, and bonuses for continuous consumption.
Additionally, the solution needed to accommodate various plant operating regimes and be scalable across the company’s facilities.
The solution baobab was selected as the technological partner to develop an AI-driven solution incorporating advanced mathematical models:
Advanced predictive models:
- Price Forecasting: Provides electricity market price predictions.
- Demand Forecasting: Delivers highly accurate product demand predictions.
Optimization models: These models maximize gas production while minimizing energy consumption on an hourly basis, selecting the appropriate operating regime for each case.
The result
The implementation of this AI solution led to a measurable reduction in electricity consumption, translating into monthly savings of approximately €300,000 to €500,000 per plant. Additionally, it streamlined processes and supported better decision-making.
Optimization-oriented AI and sustainability
In summary, AI has become an invaluable tool for managing energy resources more intelligently and efficiently. Its versatility allows it to be applied across nearly all sectors, offering innovative and sustainable solutions that can significantly enhance energy efficiency. Having a team of experts involved at every stage of the process is crucial.
At baobab, we can provide the guidance and support you need to embark on this journey toward a more efficient and sustainable future.
