AI and Energy Demand

Context:

The growing adoption of Artificial Intelligence (AI) globally and in India is significantly increasing energy demand, raising concerns about data centres’ impact on power grids.

AI and Rising Energy Demand

Global Perspective:
- International Energy Agency (IEA) 2024 report projects AI-driven data centre energy demand to more than double by 2030 (~945 TWh).
- McKinsey:Global data centre capacity demand may rise 19–22% per year (2023–2030), from 60 GW to 171–219 GW.
India-Specific:
- Data centre demand projected to rise 2 GW (2024) ? 4.5 GW (2030) due to AI and digital adoption.
- Key hubs:Mumbai (41%), Chennai (23%), NCR (14%).
- AI-driven data centres may consume additional 40–50 TWh/year by 2030.
Energy Footprint Comparison:
- Current:1–2% of global power; projected: 3–4% by 2030.
- For perspective: Steel industry consumes ~7% of total energy.

Challenges and Pressures

Electricity Demand: High computational requirements of AI intensify grid pressure.
Water Usage: Cooling servers in data centres increases freshwater demand.
Carbon Emissions: Despite renewable energy integration, AI-related energy use may increase emissions.
Cybersecurity Risks: AI increases sophistication of cyberattacks on energy utilities, impacting energy security.

Opportunities for AI in Energy Management

Efficiency Gains:
- AI optimisesenergy delivery, utilisation, and resource management.
- Predictive load management, demand forecasting, and renewable integration.
Smart Real Estate:
- AI-enabled HVAC optimisation, smart lighting, building automation reduces energy use by up to 25%.
- Green certifications (GRIHA, LEED) encourage AI-driven monitoring.
Renewables Integration:
- AI aids hybrid solar-wind-battery plants, ensuring 24/7 renewable energy availability.
- Real-time load forecasting reduces outages and grid stress (examples: Tata Power ReNew, BESCOM, Uttar Pradesh smart meters).