A hidden data asset in solar energy

Italy’s energy agency, the GSE, publishes what it pays to solar plants: 6.8 million payments between 2014 and 2025. The rest of the raw material comes from commercial sources. As published, none of it tells a buyer very much. Which plants perform, what they are still owed, who owns them and what they are worth is not in any single file. Answering that takes ten sources and a nine-stage pipeline, reconciled plant by plant.

• Is this plant performing?Its measured yield, set against comparable plants.
• What is it still owed?The incentive still to come under its contract.
• Where is it?Each plant’s address located and checked.
• Who owns it?The likely owner of the land — not always the company that receives the incentive.
• Who is the business behind it?Company registry details and credit scores.
• Has its contract changed?The GSE letters of 2015–16 rescheduled many plants’ incentives; they are read one by one.
• What is it worth?An estimated value for the plant.

The deepest layers, land and ownership, are built for the larger plants.

plants in the register

about 74,000plants in the register

fields per plant

77fields per plant

of the capacity paid under Italy’s first four Conto Energia schemes

78%of the capacity paid under Italy’s first four Conto Energia schemes

public data points in, data points out

68M → 3Mpublic data points in, data points out

More than twenty data points go in for every one that reaches a buyer, before counting the contracts and the imagery. Every step has checks: where a match is uncertain, the field stays empty rather than guessed. Behind it sit some 140,000 lines of code — Python, TypeScript and SQL, a quarter of them tests — and a database of 80 tables.

Currently in use by an independent power producer, investment funds and research companies.

Many businesses hold a version of this: data that is public, scattered or unused, which becomes an asset only once it is joined up and made to answer a buyer’s question. monetaise.com’s advisory work starts there — finding the hidden data asset in a business, and judging what it would take to build it and sell it.