Sustainable AI

Renewable Energy and AI

 Written by: Khawja Labib

In our quest to fully depend on renewable energy, we must find ways to further optimize the process of harvesting energy efficiently. As the world endeavors to mitigate the effects of climate change and transition towards cleaner energy sources, AI emerges as a powerful ally in revolutionizing the renewable energy landscape. From optimizing energy production to enhancing grid stability and fostering energy efficiency, the integration of AI technologies is reshaping how we harness, manage, and utilize renewable resources. Continue reading this newsletter to learn how AI can be used in the realm of sustainable energy production.

What is Artificial Intelligence and why is it important for Renewable Energy?

"It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable." – John McCarthy in a 2004 paper [1]. Essentially, they are systems designed aimed to think and act rationally rather than think and act “like humans” [1].

Every day the demand for electricity is increasing, and with that, the complexity of power systems [2]. To add to that, in this age, grids have to communicate with several different users like EV charging stations and solar installations, which also increases the complexity of the communication between grids and users [2]. To combat this increase in demand and complexity, we need more tools; one of those tools is AI [2]. AI can be used to analyze the demand for electricity and react by supplying the required electricity based on the analysis [2].

AI and Energy Storage

To ensure a constant flow of energy to meet the demands, an efficient energy storage system must be used. Artificial intelligence has the potential to optimize the operation of such energy systems. Energy storage systems pose a significant challenge in terms of economic efficiency, and optimizing these systems using AI can greatly decrease the cost of maintaining energy storage [3]. Several different types of AI algorithms like neural networks, genetic algorithms, and machine learning techniques can be applied to improve performance [3]. These algorithms take into account different variables, and AI can use these variables to predict the required energy supply needed to satisfy the demand [3]. To achieve these goals, certain mathematical models and algorithms are used. It is out of the scope of this newsletter to describe them in detail. You can further learn about this idea in the attached references.

AI and Climate Change

Predictive models based on AI make it possible to make renewable energy sources easier to use while slowing down the negatives of climate change [4]. These models help to discover different ways to extract the most out of renewable energy sources, which results in fewer carbon emissions [4]. Algorithms like Random Forest, Support Vector Machines (SVM), and Deep Boltzmann Machine (DBM) are used for predictive modeling [4].

By accurately forecasting energy production from sources like solar, wind, and hydro, these models enable more effective integration of renewable energy into the existing energy infrastructure. This, in turn, reduces reliance on fossil fuels, which are major contributors to greenhouse gas emissions.

Moreover, AI-driven predictive models facilitate the discovery of innovative strategies to maximize the efficiency and output of renewable energy systems. For instance, these models can analyze historical data and real-time conditions to optimize the operation of solar panels, wind turbines, and other renewable energy technologies. By identifying optimal operating parameters and maintenance schedules, AI helps minimize energy wastage and improve overall system performance.

Furthermore, by leveraging advanced algorithms such as Random Forest, Support Vector Machines (SVM), and Deep Boltzmann Machine (DBM), predictive models can uncover intricate patterns and correlations within vast datasets related to renewable energy production and consumption [4]. This enables stakeholders to make data-driven decisions that lead to significant reductions in carbon emissions and other harmful pollutants associated with conventional energy sources.

In conclusion, the integration of Artificial Intelligence (AI) into renewable energy systems presents a promising avenue for addressing the challenges of climate change and energy sustainability. AI technologies offer opportunities to optimize energy production, enhance grid stability, and promote energy efficiency in the transition towards cleaner energy sources. While this newsletter provides a basic introduction to AI solutions in renewable energy, further detailed study can be pursued through the papers and references provided. By leveraging AI-driven predictive models and advanced algorithms, we can pave the way for a more sustainable future by maximizing the potential of renewable resources while minimizing environmental impact. Let us continue exploring and innovating in this critical intersection of renewable energy and artificial intelligence for a greener and more resilient planet. 

References

[1]      “What is Artificial Intelligence (AI) ? | IBM.” Accessed: Feb. 10, 2024. [Online]. Available: https://www.ibm.com/topics/artificial-intelligence

[2]     “Why AI and energy are the new power couple – Analysis - IEA.” Accessed: Feb. 10, 2024. [Online]. Available: https://www.iea.org/commentaries/why-ai-and-energy-are-the-new-power-couple

[3]     D. L. Chiorean, D. Bicǎ, C. Gorea, I. Vlasa, C. C. Hurducaci, and A. Mandis, “Optimizing the operation of established renewable energy storage systems using artificial intelligence,” Proceedings of 2023 10th International Conference on Modern Power Systems, MPS 2023, 2023, doi: 10.1109/MPS58874.2023.10187594.

[4]     Nawaf Alharbe and Reyadh Alluhaibi, “The Role of AI in Mitigating Climate Change: Predictive Modelling for Renewable Energy Deployment” International Journal of Advanced Computer Science and Applications(IJACSA), 14(12), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0141211