AI is the future but is it going to be a greener one? This week, we take a
deep-dive into AI clean tech and how your business can make the most of it.

AI’s potential has grown exponentially across a wide array of industries, from digital media and content creation to weather forecasting.  The AI market is worth a staggering USD 135bn, and growing by 37% every year, according to GrandView Research. But how should people feel about this? And how can we ensure we enjoy the best benefits of AI’s rapid development?

Despite its rapid uptake, AI has garnered plenty of concern due to emergent issues of social manipulation, privacy violations and job losses. Yet this technology has a great deal of positive potential too, much of which is environmental. Through recording, analysing and predicting data, AI is able to cut emissions and improve health outcomes in numerous different ways. Here are just 7 of them:

1) Weather and energy generation demand prediction

One area of great excitement is AI’s role in predicting weather changes, and in turn predicting levels of demand upon energy generation. 

Inaccurate weather forecasts are still an issue in 2023, yet these forecasts bear enormous economic and environmental significance. As Tech Target explains, “Temperature forecasts are one of the primary inputs that utility companies rely on when planning for energy demand.” Despite this important use case for weather forecasting, it can and often is “imperfect due to sources of error in prediction models.”

More machine data, analyses, pattern detection and predictions mean that energy generation can be better tailored for weather forecasting. For example, if less heating is required because most people will be outside enjoying the sun, then less energy can be generated and potentially wasted. 

AI Time Journal argues that this will have multiple, positive effects. The journal states that AI’s ability to “improve energy storage, efficiency, and load management can assist in the integration and reliabilities of renewables. In turn, this will facilitate dynamic pricing and trading, resulting in market incentives.” With energy prices growing year on year, dynamic pricing and trading would certainly enhance consumer investment in renewable energy, creating a win-win for consumers and their carbon footprints.

2) Maintenance alerts and predictive maintenance in energy monitoring

Weather forecasting is not the only way in which AI can help us become more energy efficient; predictive maintenance also has great potential in this area. 

Predictive maintenance starts with granular energy data, through IoT or other hardware systems, such as GridDuck. That data provides value in numerous ways, one of which is for pattern recognition.

AI can identify anomalous data which signals breakages, equipment that has been left on or damaged equipment. Because broken or damaged equipment wastes more energy than fully functioning equipment, identifying these issues can save a lot of energy in the long run. For example, AI can be used to identify when a manufacturing compressor is consuming more energy, signalling it may have a leak. This is exactly what happened to GridDuck clients Silverline, who saved 5.4 MWh (approximately £35,000) on their energy bill by correcting compressor leaks found in a similar way. Not only was this a fantastic cost saving for the businesses, but it also saved a lot of unnecessary carbon emissions.

However, AI can also go even further, in what Ecocentric Energy describes as a “proactive approach… that involves using machine learning to predict when a piece of equipment is likely to fail”. Not only can AI identify when machinery is broken, but it can also identify energy usage spikes which signal how certain practices may be putting a machine under strain, or when a machine’s energy efficiency is so poor that it is likely to soon stop working. Using this data, businesses can change their practices to take better care of machines or evaluate the right time to upgrade to more efficient models, saving both material and energy waste.

3) More ecological farming

Food production is another vital area of sustainability, accounting for “8.5% of all greenhouse gas emissions with a further 14.5% coming from land use change.” As one Mckinsey article points out, currently crops withdraw “more from natural systems than [they] return, leaving waterways polluted and soils and agrobiodiversity depleted.” 

However, AI can use data about weather, crop health and soil quality from sensors, satellites and drones to calculate how farmers can “make smarter decisions on where to grow crops, how to optimise crop rotations, and when to sow, compost, and harvest those crops.” Reduced food waste, better quality food and a healthier natural food system are yet another sustainability win for AI.

4) Reducing Food Waste

Production is not the only food industry that can benefit from AI: food retail and hospitality can also be made more sustainable. One article published by Data Science Blogathon suggests that despite previous waste reduction efforts focusing on farming and supply chain management, AI has potential to revolutionise our understanding of demand for food.

One way this can be achieved is through using algorithms to “predict demand for products based on historical sales data and weather forecast data”. Another way is to make use of “computer vision techniques” in sorting food. In short, these technologies can be used to grade fresh products such as fruit and vegetables based on “size, shape and colour”, ensuring that the ripest ingredients are used first and waste is minimised.

5) Greener Transport and Logistics Systems

In a similar way, AI can use data about consumer demands to reduce emissions created within the transport and logistics industries. The blog re-work discusses this idea, stating that AI insights on “the delivery and non-receipt of goods” will give “logistics companies a better sense of what goods to ship and even craft energy-saving routes in response.” 

Personal transport can also be made more environmentally friendly through AI, namely by way of autonomous vehicles. Autonomous vehicles are not only more often electric (as in the case of Tesla) but they can prevent accidents, traffic and poor navigation due to human error, all of which have knock on effects for emissions. 

6) Solving Air Pollution

Much like food waste and transport emissions, air pollution is one of the greatest environmental threats facing modern cities. In fact, World Health Organisation data shows that pollution kills 7 million people each year. 

Environmental Health News features one exciting case of how AI could be used to tackle this issue, using data about emissions to predict hospital admission spikes relating to air pollution-related conditions such as strokes, exacerbated asthma and other pre existing respiratory conditions. 

Previously, atmospheric pollution could only be predicted 3 days in advance, but Professor Yunsoo Choi from the University of Houston, and his team have found a way of using AI to predict ozone pollution 14 days ahead of time. Like so, AI can help governments create emission warnings, help hospitals prepare better and help citizens avoid high emission zones.

AI World School describes a feedback loop in which AI is used to monitor, predict and analyse air pollution data, all of which is then used to learn more about automotive maintenance and create better road policies to minimise pollution.

Preserving biodiversity

Finally, AI has an exciting role to play in preserving biodiversity. By using satellite imagery, AI can detect “changes in land use, vegetation, forest cover and trace invasive species.” Vitally, it can also “help anti-poaching units plan patrol routes and analyse data in the aftermath of natural disasters”, protecting endangered species, many of which are vital to the wider ecosystem and the health of our planet. 

If you would like to learn more about the technologies available to make your business more sustainable, please book a 15-minute meeting with our Business Development Manager, Miles Browne using Calendly.