Generative Artificial Intelligence (AI) is rapidly transforming every industry. The energy sector, with its inherent complexity and data intensity, is particularly wellpositioned to benefit from AI applications that enhance efficiency, reduce costs, and enable the production of cleaner energy.
Yet, while the advantages of AI in improving production, optimization, and predictive maintenance are significant, its adoption also brings a less discussed challenge: the substantial increase in energy demand required to power AI models and the data infrastructure behind them.
For electrical engineers and energy professionals, this creates a paradox: AI simultaneously acts as both a driver of energy efficiency and a consumer of considerable energy resources.The objective of this paper is to explore this “AI–Energy Paradox” by assessing the balance between the gains AI delivers and the costs it imposes on the energy system.
The paper will be structured in four sections:
1. Definition and Use Cases — Introduction to AI,with a focus on generative capabilities andconcrete applications across the energy sector.
2. Production Benefits — Quantitative estimationof efficiency improvements, productionincreases, and cost savings enabled by AI.
3. Energy Demand Impact — Calculation andestimation of the additional electricity demanddriven by AI workloads and infrastructure.
4. Net Assessment — Comparative analysisweighing AI’s energy savings and productivitygains against its energy consumption, withimplications for strategy and sustainability in theenergy sector
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