The Scenario
You're a facilities manager responsible for 15 German sites, each running a heat pump. The directive from finance came in yesterday: cut electricity costs by scheduling high-draw devices during green and low-price windows. You've been handed a workbook with postal codes and asked to produce optimal 4-hour run windows by tomorrow morning.
The postal codes are in column A. The sites are in column B. That's it.
The bad version:
- Call Corrently's device schedule endpoint for site 1, parse the recommended window from the response, write the start time and end time into row 2
- Repeat for 14 more sites, being careful not to mix up which window belongs to which postal code
- Re-run the whole thing after an hour because the grid conditions shifted and your first few results are now outdated
Finance wants this in the budget proposal by 9 AM. You have 15 rows to fill and a Corrently API that returns a JSON object you've never worked with before.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads your site list, calls Corrently's device scheduling endpoint for each postal code, and writes the CO₂-optimized 4-hour operation windows back into the correct rows — recommended start time in one column, end time in another.
For each German postal code in column A, get the optimal 4-hour operation schedule for a heat pump optimized for CO₂ reduction from Corrently and write the recommended start time and end time into columns C and D
What You Get
- Column C: recommended start time (e.g., "06:00") for the optimal 4-hour heat pump run window at each site
- Column D: corresponding end time ("10:00") based on the Corrently CO₂ optimization model
- Sites where no optimal window was returned flagged with a note in column E
- All 15 rows updated against the same grid snapshot so the windows are comparable across sites
What If the Data Is Not Quite Ready
Different sites have different required run durations
Column B has the required run hours per site, not a flat 4-hour assumption.
For each postal code in column A, use the required run hours from column B as the window length, fetch the optimal operation schedule from Corrently optimized for CO₂, and write the start time and end time to columns C and D
You want to optimize for price instead of CO₂ for some sites
Column C has the optimization goal per site — "CO₂" or "price" — and you need to respect it.
For each postal code in column A, fetch the optimal device schedule from Corrently using the optimization goal in column C (CO₂ or price), using the run duration in column B, and write the recommended start time and end time to columns D and E
Device type varies across sites
Some sites need heat pump schedules, others need EV charger windows or industrial dryer slots.
For each postal code in column A, fetch the Corrently device schedule for the device type listed in column C, using a 4-hour window optimized for CO₂, and write the recommended start and end times to columns D and E
Full pipeline: clean data, fetch optimal windows, and flag conflicts
Postal codes need formatting, device types vary, and you want to flag any site where the recommended start time is before 6 AM.
Normalize the postal codes in column A, fetch the optimal Corrently device schedule for each site using the device type in column B and a 4-hour CO₂-optimized window, write start time and end time to columns C and D, and flag any row where the start time is before 06:00 with "EARLY START" in column E
Single ask. All the conditional logic lands in one place.
Try It
Get the 7-day free trial of SheetXAI and open your site workbook with German postal codes and device types, then ask it to generate CO₂-optimized run windows from Corrently for every row. For related reads, see fetching green power forecasts and the Corrently integration overview.