AI can analyze vast datasets to predict energy demand more accurately and optimize the balance between renewable and traditional energy supply.

Predictive analytics is one of AI's most impactful applications in energy management. Through machine learning algorithms, AI systems can analyze historical energy consumption data alongside real-time inputs to forecast future energy needs accurately. 

Integrating AI in grid management revolutionizes how energy is distributed and consumed. Smart grids powered by AI facilitate the efficient distribution of renewable energy, ensuring the energy supply is stable and sustainable.

For large industrial power users in Mexico, the CFE's GDMTH tariff isn't just a line item—it's a significant operational risk. High demand charges, grid instability, and rising costs can erode profitability. However, one Tier-1 automotive supplier in Querétaro turned this challenge into a competitive advantage.

During a comprehensive energy audit, we identified and tagged over 80 leaks of varying sizes throughout their facility. The cumulative impact of these leaks resulted in an estimated annual energy waste exceeding $26,000. Repairing them required minimal capital outlay and delivered a payback period of less than three months.

The GDMTH tariff, applicable to most large-scale industrial users, is intentionally designed to penalize inefficient use of the national grid. It does this by levying heavy charges not just on the amount of electricity consumed, but on the intensity of that consumption at specific times.