The Scenario
You're an energy consultant and you've just wrapped a scoping call with a new client who operates 40 leased sites across Germany. They want to run energy-intensive processes — server cooling cycles, EV charging batches, HVAC defrost — at the greenest hours of the day.
The sites are already in your Google Sheet. Column A has the postal codes. That part's done.
The bad version:
- Open the Corrently API docs, find the GrünstromIndex forecast endpoint, run it for the first ZIP code, copy the hourly output from the JSON
- Repeat for all 40 postal codes, manually deciding which hour has the peak green-power score for each
- Add the CO₂ intensity column by hand, then figure out that two of the ZIP codes returned empty responses and backfill them
Your client's first report is due Friday. That manual loop takes the better part of a day, and the data is already aging the moment you start row 2.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads your postal code list, calls the Corrently GrünstromIndex forecast endpoint for each row, and writes the results back — peak green-power hour and average CO₂ intensity — without any endpoint configuration on your end.
For each German postal code in column A, fetch the 24-hour hourly GrünstromIndex forecast from Corrently and write the peak green-power hour and average CO₂ intensity into columns B and C
What You Get
- Column B: the clock hour (e.g., "14:00") when the green power score peaks over the next 24 hours for each ZIP code
- Column C: the average CO₂ intensity in gCO₂/kWh across the full 24-hour forecast window
- Empty responses flagged with a note in column D so you know which ZIP codes need a follow-up
- All 40 rows updated in a single run, with timestamps matching the same API call window
What If the Data Is Not Quite Ready
The postal codes have inconsistent formatting
Some rows have a leading zero, some don't. Some have extra spaces from a paste operation.
Before fetching GrünstromIndex forecasts, normalize all values in column A — trim spaces and ensure each is a 5-digit string with leading zeros — then fetch the 24-hour forecast for each and write the peak hour and CO₂ average to columns B and C
You need the top 3 green hours, not just the peak
One hour isn't enough for scheduling. You want a window.
For each postal code in column A, fetch the 24-hour GrünstromIndex forecast from Corrently and write the three hours with the highest green power scores, in order, to columns B, C, and D
The site names and postal codes are on separate tabs
Your client sent the site registry on Sheet2, and your working tab only has postal codes.
Look up the site name for each postal code in column A by matching against column A of Sheet2, then fetch the Corrently GrünstromIndex forecast for each ZIP and write the site name, peak hour, and CO₂ intensity to columns B, C, and D
Full cleanup, fetch, and analysis in one shot
Postal codes need normalizing, you want the top green hours, and you need a flag on any site where the peak CO₂ intensity is above 400 gCO₂/kWh.
Normalize the postal codes in column A, fetch the 24-hour GrünstromIndex forecast from Corrently for each, write the top 2 green hours to columns B and C and the average CO₂ intensity to column D, then mark any row where column D is above 400 with "HIGH CO₂" in column E
The pattern holds: data normalization, retrieval, and conditional flagging all happen in one ask.
Try It
Get the 7-day free trial of SheetXAI and open the sheet with your German postal codes, then ask it to pull the GrünstromIndex forecasts from Corrently for every site. You'll also find related reads in how to fetch electricity prices by ZIP and the Corrently integration overview.