Ai for Clean Energy
In late 2019, the EU parliament declared that our world was facing a global climate crisis. From rampant bushfires in Australia and California to black skies in Beijing, New Delhi and a disappearing Maldives, every edge of the globe is facing the consequences of climate change.
The rapid developments made in the arenas of Machine Learning and Artificial Intelligence, however, bring new hope to the world’s efforts to find and implement sustainable clean energy solutions.
For instance, a few months prior to the aforementioned EU declaration, DeepMind, a UK based artificial intelligence company, predicted the energy output of a wind farm 36 hours ahead of its true generation.
this graph depicts Google's energy savings when their DeepMind Ml Control was turned on to monitor the company's energy output
While seemingly minor, this development in the field provides a two-tiered benefit. Firstly, this prediction counteracts the intermittency of natural energy sources, thus providing a layer of security to further support their addition to the general mix of energy sources.
Secondly, and more importantly by having a prediction for the amount of energy a wind farm will be able to output, through an analysis of weather forecasts, cloud movements, air masses, and more, the wind farm will be able to optimize its machinery to cater to precisely the predicted output.
Further,AI can be leveraged to assist efforts in not only renewable energy solutions, but also sustainable energy usage. There are several examples of this today.
In an effort to reduce the amount of energy spent on cooling Google’s data centers, DeepMind conducted a thorough analysis and used neural networks to optimize the way that these centers were being cooled. As a result, it was able to reduce the amount of energy spent on cooling by around 30%, which corresponds to an reduction of almost 12% in overall energy consumed by these data centers.
Similarly, Vigilent, a California-based initiative, has partnered with several tech companies, including HP, Uber, and Salesforce, to eliminate wasteful energy consumption in their data centers, which could lead to a reduction in carbon emissions by 50 million metric tons.
On a more individual scale, ‘smart home’ technologies, which are becoming increasingly popular, can reduce household energy consumption by turning off unused lights and electric appliances, optimizing room temperature by consistent monitoring, and ultimately ensuring that no energy goes to waste.
While the road ahead to zero-emissions and sustainable energy is long and hard, it is clear that the innovative applications of AI can help speed up the process. Either by finding more efficient ways to generate clean energy or eliminating energy wastage, AI makes intelligent solutions to the energy crisis much more accessible.
Written by Vishal Dhileepan
Edited by Jason Kauppila, Corina Perez-Cobb, Karina Thanawala & Gihyen Eom