How machine learning is helping green industry
While technology gradually makes business processes simpler, smarter and more efficient, it begs the question around whether it can help industries become cleaner too.
Machine learning is one such technology which is being applied to optimize our operations— so it can also play a key role in reducing our carbon footprint.
Since the nature of machine learning is to study provided data by an underlying data-generating system, it opens up more channels for us to study where can we optimize cost, utilization and possibly, reduce greenhouse effects as a result from our business.
It’s not an overnight process, in fact, it requires businesses and industries alike to revisit their operations. But for those that take the step, it could not only help position them as ‘progressive’ and ‘conscientious’ leaders but also cut back on energy consumption costs overall.
Smarter transport management
A quarter of global energy-related Co2 gas emissions are attributed to transportation— and that makes perfect sense considering that transportation is what’s keeping this world running, hence, the biggest area that machine learning can contribute for the betterment of our environment is by improving the way fleets are managed.
It is one of the hottest areas of study right now for the application of machine learning is in transportation as automation researches actively take place. But that’s not the only part that machine learning can be applied to, it can also be used to increase overall vehicle efficiency by managing fuel consumption, optimize battery usage and improve travel time in a smarter way for businesses that have fleets. This, in turn, can help reduce cost and carbon emission as the vehicles have been optimized to travel efficiently while controlling fuel usage.
But internal-combustion engines are not the only one being studied at the moment. Research is being done on hybrid and electric vehicles as well to optimize battery power while the car is being operated. Despite still being an experiment, the fruitful results it has shown thus far may prove to be beneficial for future transportation.
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Smarter power management
Electricity too is regarded as another big contributor to a large amount of greenhouse gas emitted by us humans; accounting to one-quarter of the total greenhouse gas generated today.
Realizing this, and still needing to adapt to the increasingly industrialized world, more companies and people at large are moving towards adapting lower carbon-emitting solutions to generate electricity. Among the ways that we leverage on now is by harvesting solar or wind power to produce electricity. However, performance and reliability vary because solar panels and windmills rely on the weather to generate electricity, hence they would not be able to provide constant results all the time. Solar panels would not function under the cloudy or rainy sky, nor would windmills function when there’s no breeze.
Here, machine learning and AI can play a part too. To get better weather prediction, control and easier scheduling for the operations of this equipment, machine learning and AI can be applied to this equipment in order to manage and optimize power harvesting rates. What’s more, it could also help with better power storage management in order to prevent wastage, which is another cause of global warming.
More efficient agriculture
It’s not just power plants and engines that emit greenhouse gases. The global agriculture industry is a great contributor to the world’s carbon output, owed to largescale deforestation and irrigation.
But agriculture is becoming a data-rich business, and farmers can now aggregate a mass of data points— such as weather, satellite and drone imagery, sensors on machinery— to gain new insights into how they can make operations more efficient and less wasteful, so less produce needs to be grown or reared in order to meet demand.
IBM offers one such platform, with a user commenting that “the ability to better anticipate rain not only saves me money but also helps me save precious natural resources.”
Possibilities are endless when it comes to the application of machine learning to our daily lives. Being a self-learning program, all that it needs is proper programming and the right in-feed of data to get the job done.
These three areas of application described above are just a small part of machine learning’s bigger contribution to the betterment of our environment. And with more research, machine learning may, and could be the answer to address global warming.