Energy platform MCP · API · Python SDK

Energy Data

From raw I90 files to AI-ready market intelligence — we build the data infrastructure energy companies run on.

1.3B+ records
5.0k+ units
13 programs
through 2026-01-21 23:45:00+01:00
ARRU1 PDBF 78.0€ ↑
CAST3 RT3 14.2€
FVGNRA RT3 -50€ ↓
AMRB1 PHF1 22.5€ ↑
CTGN2 PDBF 65.3€ ↑
BES4 BS 8.7€
EALFOZ RT3 -88.2€ ↓
SBG3 PDBF 45.1€ ↑
SR15 RR 12.3€
SROQ1 PHF3 31.8€ ↑
ARRU1 PDBF 78.0€ ↑
CAST3 RT3 14.2€
FVGNRA RT3 -50€ ↓
AMRB1 PHF1 22.5€ ↑
CTGN2 PDBF 65.3€ ↑
BES4 BS 8.7€
EALFOZ RT3 -88.2€ ↓
SBG3 PDBF 45.1€ ↑
SR15 RR 12.3€
SROQ1 PHF3 31.8€ ↑

The problem

From Excel to data infrastructure

Energy companies drown in data they can't use. I90 files arrive daily in 30-sheet Excel workbooks. Billing calculations live in spreadsheets that one person understands. Market signals hide in CSVs that nobody has time to analyze.

I90_2026-05-21.xlsx
I90DIA00
I90DIA01
I90DIA02
I90DIA03
I90DIA04
I90DIA09
I90DIA12
...
I90DIA00: Book Contents
Hoja
Descripción
I90DIA01
PBF Hourly Program
I90DIA03
Daily Market Constraints
I90DIA04
Secondary Market Prog.
I90DIA09
Constraint Prices
I90DIA12
Intraday Energy
...
+ 25 more sheets
⚠ 30+ sheets per file · 1 file per day · unnormalized data
SELECT * FROM i90 WHERE date = '2026-05-21'
date
hour
unit
program
omie_€
rt3_€
2026-05-21
14
ARRU1
PDBF
22.5
14.2
2026-05-21
14
CAST3
PDBF
26.1
14.2
2026-05-21
14
AMRB1
PDBF
21.8
14.2
2026-05-21
15
ARRU1
RT3
18.9
19.5
✓ 211M+ rows · 0 nulls · queryable via SQL, MCP, Python

We turn that raw data into queryable, automated, AI-ready infrastructure. Every tool we build works with your AI agent via MCP, or standalone via API and Python.

Enrichment

From codes to business context

I90 files only contain unit codes. We cross-reference REE and OMIE catalogs to add company name, technology, installed capacity, and market participant.

SELECT unit, company_name, technology, power, market_participant FROM i90
unit
company_name
technology
power
market_participant
ARRU1
Iberdrola
Combined Cycle
800 MW
IBERDROLA GEN.
CAST3
TotalEnergies
Combined Cycle
400 MW
TOTAL GAS Y ELEC.
AMRB1
Castleton
Combined Cycle
800 MW
CASTLETON COMM.
FVGNRA
Capital Energy
Solar PV
50 MW
CAPITAL ENERGY
✓ Pre-joined from REE + OMIE catalogs · no manual JOINs · ready for GROUP BY company

Real cases

What you can do with the data

RT3 Winners
Curtailment
Intraday Trading

Who benefits from RT3?

GROUP BY company_name reveals which companies concentrate the technical constraint costs.

SQL
SELECT company_name,
       SUM(energy) AS total_mwh
FROM i90
WHERE program = 'RT3'
  AND sign = 'Subir'
GROUP BY company_name
ORDER BY total_mwh DESC
LIMIT 5
Iberdrola 1,247k MWh
Endesa 894k MWh
Naturgy 612k MWh
TotalEnergies 445k MWh
EDP 298k MWh
→ Iberdrola concentrates 32% of RT3
Read full analysis

Practical cases

Built with real data

Spanish Energy Market Database (I90 Files)

Challenge

Spanish energy market data arrives daily in I90 files — Excel workbooks with 30+ sheets detailing programming units, energy prices, and market operations. The Excel format made it nearly impossible to analyze trends or integrate with other systems.

Solution

We designed a relational database that transforms 30 sheets into a single queryable table. Our ETL pipeline runs daily, processing new I90 files automatically. The schema captures relationships between programming units, time periods, and prices.

Outcome

Data scientists write SQL queries instead of wrestling with Excel. The system handles new file formats automatically. The same architecture powers AI-assisted analysis through MCP servers.

Try datons.esios →

Energy Company Calculation Automation

An energy company performed daily tariff calculations, billing reconciliations, and regulatory reporting in Excel. Each calculation took hours, was error-prone, and depended on one person who understood the spreadsheet formulas.

See Energy Billing →

Factory Energy Optimization System

A manufacturing facility with multiple energy assets — solar panels, batteries, grid connection, and variable production loads — needed to optimize energy consumption and costs in real-time.

Who this is for

Who this is for

Energy companies analyzing market data in Excel
Utilities drowning in manual billing calculations
Factories needing real-time energy optimization
Teams wanting AI agents that query energy data via MCP

Ready to turn your energy data into infrastructure?

Let's discuss your energy data challenges.

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