Artificial Intelligence is increasingly becoming an essential component of the energy industry. As world leaders get more serious about meeting climate goals, the energy industry is facing the mandate to completely transform the way it operates at an unprecedented scale which will require massive, complex and nuanced computing power. AI is already playing a major role in renewable energy forecasting, smart grids, coordination of energy demand and distribution, maximizing efficiency of power production, and research and development of new materials.
A 2021 explainer from the World Economic Forum laid out three key driving factors which are "huge strategic and operational challenges to the energy system and to energy-intensive industries," thereby making AI an essential component of the energy transition:
AI is therefore essential for the unprecedented demands of decarbonization, which will depend on an intelligent, responsive, and flexible computing system able to recognize and predict complex patterns of production and consumption. But there's a problem. While AI is necessary to curb emissions, AI itself requires vast amounts of energy to fuel the training and machine learning processes that make the model useful. Certain single AI training models have been shown to use the equivalent of 125 New York-Beijing round-trip flights, or the lifetime carbon footprint of five cars.
So, is AI a net positive for energy efficiency and greenhouse gas emissions? Not always, according to a recent report from Semiconductor Engineering. Using AI responsibly and efficiently requires a number of considerations and calculations. Starting with the simple question: does this system actually need AI? While artificial intelligence undeniably has much to offer to the energy industry, it can also be more seductive than strictly necessary in certain contexts. In the words of Semiconductor Engineering, "we can no longer afford to be profligate with our resources; we need to ensure that the benefit outweighs the cost."
If the system in question would indeed have net benefits from AI, engineers will next have to consider where the energy for the training is sourced, whether workloads are designed efficiently and effectively, calculate and consider embedded emissions, and maximize performance per watt.
If AI is optimized for maximum energy efficiency and trained using clean energy sources, it's a no-brainer for the energy transition. But making responsible, effective, and climate-conscious AI capable of catalyzing the clean energy revolution will require 'clear policy incentives,' but these have not been forthcoming, as AI is still relatively poorly understood and somewhat mistrusted in public spheres. Employing AI to its fullest potential will require a deep understanding of the enormously positive potential benefits it offers, in addition to its potential pitfalls.
By Haley Zaremba for Oilprice.com
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Haley Zaremba is a writer and journalist based in Mexico City. She has extensive experience writing and editing environmental features, travel pieces, local news in the… More
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