The Impact of Artificial Intelligence on Renewable Resources
Renewable resources are one of the key ways to meet the demand for energy while not destroying the planet. They are literally any energy source that renews itself at a rate that is equal to or faster than it is spent. What is helping scientists, engineers, and environmentalists around the world harness these resources more effectively than ever is artificial intelligence. This isn’t a real shocker where there is new and advancing technology there is usually some AI influence. What is interesting is how AI application has impacted the renewable resource industry.
The Scale of AI in Renewable Energy
AI already does a lot for renewable resources such as modelling and parameters estimates, net forecasting, line loss predictions, predicting equipment failures, and even process or grid operations. To get an idea of the impact of AI on the renewable energy industry it’s best to look at three examples. The expertise of AI’s have gained and what it means, how AI can make weather predictions and the modelling AI does for integral components of renewable energy devices.
Why AI Is a Powerful Tool
To better understand the power of AI’s ability its best to understand that there are different types of AI. AIs used most by scientists in renewable resources are often referenced to as an ANN, artificial neural networks. Not all AI’s are ANN’s but many that deal with weather predicting, modelling, and energy consumption data all fall under the ANN title.
These ANN’s are given years of meteorological data, complex formulas, and the processing speed to retain and apply each and every aspect available to every problem given to them. They have in a sense become experts, this expertise then allows them to make the most informed decisions. Think of an ANN as a room full of experts from different specialized fields coming to decisions on intricate blueprints or the placement of new solar power plants with the speed, efficiency, and confidence that an average person shows when deciding what they will have for lunch.
ANN’s are now making quick decisions with error rates in the single digits. Their decisions can often affect issues like where to send and store collected energy, the placement of windmills, and even which generator to switch off due to a full capacity. The power behind these programs has given researchers and scientists an easier way to access solutions that would take much longer without them.
Despite the speed and power of ANN’s, there will always be a need for human experts. They use critical thinking to find a solution otherwise not used before, create new inventions and techniques never thought of, or use existing ideas in new ways. Humans are as necessary as ever in order for renewable energy to continue to grow, ANN’s are just an incredibly powerful tool.
Examples of AI Models Efficiency
One prime example of the impact of AI is to look at the modelling they do. A study written by researcher Soteris A. Kalogirou for Cyprus University of Technology going over AI’s in renewable resources stated that when ANN’s are used to model intricate equipment like solar steam generators they, “have been able to calculate the intercept factor with a difference confined to less than 0.4% as compared to the much more complex estimation of the energy deposition computer code.” This means that artificial neural networks are the most efficient and accurate computer modelling based on creating blueprints of highly specified and integral machinery.
It’s not just the creations of blueprints ANN’s can also run simulations. Kalogirou mentions that, “ANN’s have been used also to model the starting-up of the system.” He goes on to say, “it is very important for the designer of such systems to be able to make such predictions because the energy spent during starting-up in the morning has a significant effect on the system performance.” This shows that an ANN can not only be accurate with modelling equipment but find out the optimal time for machines to operate, saving time and money for those in charge by cutting out the need for real world testing.
Examples of AI Predictions
When a program can give an accurate prediction of the weather it can give any renewable resource plant a big advantage. For Google’s Deepmind it did just that. Deepmind is an AI designed to learn and better predict weather patterns to set up for the maximum amount of energy received and stored in Google’s wind power farms. According to Deepmind’s own team “to date, machine learning has boosted the value of wind energy by roughly 20 percent.” It’s even been stated that the AI program can predict windstorms up to 36 hours ahead.
This has allowed Google owned wind farms to set dates of when they could deliver on wind based energy output, as well as properly set everything up ahead of time. According to Google having their program accurately predict wind outcomes has given them an increased value that has not only supplied much needed power to local grids but also made Google a lot wealthier.
Despite Google success there isn’t a one size fit all plan for ANN predictions. In a 2019 study by Dr Tayeb Brahimi’s of Effat University it was revealed that, “in some locations and under certain atmospheric circumstances, some models may perform well in predicting the wind speed; however, the same models may generate false previsions under other circumstances.” Despite what it sounds like ANN’s are still getting predictions right, they just have to be adjusted and retaught depending on their location.
What’s important to take from this is that powerful machines are learning, predicting the weather, and making the renewable resource industry stronger than ever. This is letting green energy make an impact on the world. Without AI’s it could be possible, just a lot harder, costly and more time consuming.
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