AI often gets a bad reputation regarding its environmental impact due to its power needs. Yet in many ways, today’s advanced AI may prove to be an essential tool for a smarter and greener future.

How can AI help the environment? First and foremost, AI-powered systems, if properly trained, can help optimize the energy grid.

AI forecasts energy demand

Previously, utilities tended to be reactive, responding to demand only after it appeared. Consequently, there was always a lag as the energy company struggled to adapt to the new conditions on the ground. If demand surged enough, utility companies brought their emergency “peaker plants” online, an energy-intensive process that usually relied on fossil fuels.

AI-powered data analytics can hopefully enable energy companies to predict when demand will spike. Machine learning refines this demand forecasting, balancing loads more and more effectively over time. Operators’ ability to make better predictions and attune the system more precisely means fewer carbon emissions. Indeed, deep learning models — particularly long short-term memory (LSTM) and transformer-based architectures — can hopefully make forecasts for even just a single building.

The algorithms do this by comprehending historical data on usage and combining it with other relevant factors like the weather, economic patterns, and common human behavior. (For instance, many people tend to stay up later on weekend nights than during the week.) Given current conditions, today’s AI can calculate what the best combination of different kinds of energy — solar, wind, or battery storage — would be, and automatically steer production in this direction. 

AI automations respond faster than humans can

When Internet of Things (IoT) sensors are added to systems, smart meters can add real-time grid performance to the analysis. If something unexpected happens, reinforcement learning and neural network models can adjust immediately, taking protective measures much faster than human staff would be capable of. This goes not only for changes to demand and the grid, but also for market fluctuations.

As a result, these multi-agent systems can not only keep power running smoothly and avoid blackouts, but also decrease waste. Electricity goes where it needs to go, and less is required to keep backup systems in readiness. 

But that’s not all. AI-driven systems can help manufacturers and industries of nearly all kinds to optimize operations and eliminate waste.

AI and machine learning can mean greater sustainability

AI can help heavy industries use resources more judiciously, translating into greater sustainability across the supply chain. 

For instance, cement and steel manufacturing are two of the biggest carbon emitters. Some in these industries have begun vetting advanced AI and neural networks to monitor and predict kiln and furnace performance. The system can rapidly change parameters to respond to anomalies or other environmental changes, working tirelessly to control emissions and reduce waste.

Agriculture is another notorious source of carbon emissions that AI is poised to help streamline. Computer vision and machine learning empower farmers to adjust irrigation and fertilizer levels, as well as pesticides, most advantageously given current soil conditions and crop health. For instance, AI-powered herbicide systems zero in only on weeds rather than blanketing the whole field, which decreases the amount of chemicals needed.

AI-powered visual recognition and robotics can one day improve recycling. These systems can improve sorting, recovering more recyclable plastics, metals, and electronics than conventional methods. 

AI and machine learning can enable preventive maintenance

Preventive maintenance is another important area in which AI can help the environment. AI can track maintenance timelines and monitor the condition of equipment, which means minor problems can be addressed before they devolve into major ones. It can also schedule routine replacements and stress tests.

AI and machine learning models can also be trained on data from machines like vibration, temperature, and sound to avoid equipment failure. This saves the company money from energy-intensive emergency restarts, leads to decreased scrap rates, and avoids unnecessary part replacements.

AI-enabled preventive maintenance can extend the lifespan of critical infrastructure, keeping more gear out of landfills before its time. It also ensures operations hum along reliably, rather than suffering from unexpected bouts of downtime. Another benefit is that fewer defective products are created and subsequently thrown in the trash.

For example, Airbus’s Skywise platform uses flight telemetry data to schedule maintenance more efficiently. As a result, parts are replaced at the best time, and fewer planes are delayed due to emergency repairs. This approach also optimizes the performance of the aircraft, decreasing the amount of fuel they consume.

Google even uses an AI system called DeepMind to cool its data centers. Instituting AI-driven preventive maintenance for HVAC systems has resulted in a 40 percent reduction in energy usage.

AI and machine learning can be essential to integrating renewable energy

With the help of AI, the grid can transform from a clunky, slow, wasteful system into a responsive, data-driven, efficient ecosystem. For this reason, AI technology can prove to be essential in the quest to integrate renewable energy at scale. 

At the same time, today’s AI and machine learning, if properly applied, can help reduce carbon emissions and waste throughout the entire supply chain. Decision making can become much faster and effective, making industries of all kinds more sustainable.