How AI can help with climate change
As we all know by this point, artificial intelligence is a tool that can be used for countless applications. We know that it can be used for advertisement companies determining their clients’ interest to better target them or for apps like Snapchat to show you what you would look like as a girl or boy. These are the types of AI applications we are used to hearing about in the media, but AI is also a tool that can revolutionize how we deal with energy expenditures and climate change. It can revolutionize the old energy grid system and monitor climate change to better prepare for natural disasters.
The energy grid refers to the electric grid which is a network of transmission lines, substations, and transformers that deliver electricity from a power plant to homes and businesses. The current electric grid that we use is old, inefficient, unreliable, and it does not protect against irregularities. The US Department of Energy has said that the electric grid in the US has was built in the 1890s and improved throughout the decades. However, since it was first created during a time when only affluent families used light bulbs and few people even needed electricity, there are many downsides to the current network system. Although there have been updates to the original structure, the network is scattered and has a structure similar to a quilt patchwork piece made by an inexperience grandmother. The Canadian energy grid is similar. Thus, there has been a shift in the energy sector to move away from our current power grid system and into smart grids.
Since the fundamental structure of the grid needs to be discarded, we will need to build a new grid from the bottom up. This new grid should be able to handle new digital and computerized equipment. It should also be automated and manage the increasing complexity of the electricity needs of today. Therefore, an intelligent and adaptive grid should be implemented. However, this new grid will cost a lot of time and money to build.
(But… whether the government will actually allocate enough money for it is the real question)
Currently, small scale smart grids have been implemented and they use rather simple machine learning algorithms that learn from data collected. However, there is currently research done to see neural network applications. Neural networks are used in smart grids create a space vector map which would help with fault pattern identification in a smart grid subsystem. Other concepts in AI, like expert systems, fuzzy logic, and adaptive neuro-fuzzy systems can be used. Intelligent systems will be able to account for energy fluxes associated with incorporating renewable resources.
At the University of Alberta, there are many profs working on research of smart grids, or SG. The main objective of research in SG is to utilize resources optimally, have higher energy efficiency, higher system reliability, higher security, and more economical electricity distribution, which involves the inclusion of solar panels, wind turbines, and other renewable energy sources.
Unlike their fossil fuel counterparts, renewable energy produced by wind turbines and solar panels are environment dependent. Electricity needs to be used the moment it is generated, so, there are issues with using fully renewable resources. Since if there is no wind, then the wind turbines will not produce electricity. And if there is no sun, the solar panel will not produce electricity. Therefore, we need a smart energy grid system that can gather data about the environment and act accordingly to allocate resources to better deal with the supply and demand of energy.
As the climate on Earth is changing and we are settling into the new epoch, Anthropocene, we cannot rely on the old tools of predicting weather catastrophes. Temperature rises are directly correlated to the rise in ocean temperatures, we are likely to have more typhoons and hurricanes than ever before. AI has helped researchers achieve 89% to 99% accuracy in identifying tropical cyclones, weather fronts, and atmospheric rivers. In India, AI has helped farmers get 30% higher groundnut yields by providing information on preparing the land, applying fertilizer and choosing sowing dates.
AI also plays a key role in analyzing climate change effects on biological ecosystems by making sense of huge amounts of data which humans cannot handle. Smart drones and autonomous mini-submarines monitor emissions by tracking illegal logging, fishing and poaching, and even pest control. For example, there is a robot, RangerBot, which is a submarine that can inject toxins into coral eating starfishes.
So, in conclusion to all that we have learned over the past few articles, we need AI now more than ever. We need it to solve the never-ending problems that we created. Hopefully, the new technology that we create will actually help rather than harm the environment.
This is the last of the AI series. Thank you for reading my articles and I apologize for my poor writing. I hope you gained some interest in artificial intelligence. 😊