Data and artificial intelligence can help anticipate actual industrial hydrogen demand more accurately and optimize the planning of production, storage, infrastructure, and contracts.
When it comes to hydrogen, attention tends to focus on the cost of the electrolyzer, the price of renewable electricity, or infrastructure. The decisive asset, however, may be something else: knowing precisely how, when, and for what purpose the industry will consume hydrogen.
High demand does not guarantee a viable project. What matters is its hourly distribution, process flexibility, the cost of an interruption, and the ability to shift consumption. Two factories with the same annual volume may require different production, storage, and contracting models.
Data and AI make it possible to turn theoretical demand into real operational demand. Integrating production data, energy prices, renewable forecasts, inventories, and logistical constraints makes it possible to simulate scenarios, size infrastructure, optimize contracts, and coordinate producers and consumers. It also helps identify which consumption can adapt to renewable availability and which requires reserves.
This changes the sector’s logic: Before building capacity, it may be necessary to build knowledge. The main bottleneck for hydrogen may not be technology or a lack of renewable energy, but making multimillion-euro decisions based on an overly aggregated view of demand. Sometimes, the most valuable data point will not be how much hydrogen an industry needs, but how much it can avoid consuming at any given time.
By Miguel Ángel Rodríguez López
Subject Matter Expert, SDG Group Spain