Solar plant production report automation

Case study · Report automation

Automating solar plant production reports

Each month’s grid (ESO) CSV files are automatically combined, cleaned and turned into a single self-refreshing Excel dashboard. A new report is ready in about a minute.

Power Query Excel dashboard From Folder import Refresh workflow Energy
~1 min
report refresh
5 → 1
files combined automatically
3 359
rows of data
0
manual copy-paste
Context

The challenge

Solar production data arrives every month from the grid operator as separate CSV files — with many technical columns the report doesn’t need. The goal: a clear view of how much was produced, the trends, and when output peaks — without the repetitive monthly manual work.

Before

  • Several CSV files opened and merged by hand
  • Many unneeded columns: user codes, addresses, meters
  • Date and hour formats not analysis-ready
  • Every month — the same work from scratch

Solution

  • Import „From Folder“ — all files read automatically
  • Power Query keeps only the production data needed
  • Date split into year, month, day, hour
  • New file → „Refresh All“ → the report updates
How it works

The data path — from CSV to dashboard

CSV
Grid files
Monthly hourly exports
→
PQ
Power Query
Combine · clean · dates
→
XL
Excel model
Clean table · KPI · Pivot
→
↻
Refresh
One click
→
▦
Dashboard
Updates automatically
Result

The report dashboard

Solar plant production report Reporting period: 2026-03-01 – 2026-07-27
Refresh
Total output
2 940 kWh
Avg. daily output
21 kWh/d
Avg. hourly output
0,88 kWh/h
Days analysed
140 d.
Data up to
2026-07-27
Output by day
kWh per day · 2026-03 – 2026-07
010203040MarchAprilMayJuneJuly
Output by month
kWh · total
491,10March662,16April510,17May686,84June589,23July
Output by hour
average kWh · daily profile
0123036912151821
Data source: grid (ESO) CSV files
Refresh: Power Query
Built by: analytics.bi
Under the hood

Clean structure, repeatable steps

All source files live in one folder. The Power Query steps are recorded once and then replay automatically with every new file.

📁 ESO Solar Reporting
│
├── 📁 Data
│   ├── 📄 2026-03.csv
│   ├── 📄 2026-04.csv
│   ├── 📄 2026-05.csv
│   ├── 📄 2026-06.csv
│   └── 📄 2026-07.csv ← new file
│
└── 📊 Solar_Report.xlsx
New CSV → Data → Refresh All → dashboard updates
Power Query steps
1
Import From Folder · all CSVs read together
2
Combine & Transform · files merged into one table
3
Removed Other Columns · technical fields dropped
4
Changed Type · date → DateTime, qty → Number
5
Inserted Year / Month / Day / Hour · date parts
6
Filtered Rows · production only (P-)
7
Load to Table · „Data“ sheet · 3,359 rows
Impact

Before & after

Before — manual work
~30–45 min / mo.
Open several files, copy, merge, clean columns, refresh charts — every month from scratch, with room for error.
After — automated
~1 min / mo.
Drop the new CSV into the „Data“ folder, hit „Refresh All“ — KPIs, charts and period update themselves. Same steps, same result.
In numbers

What the report tracks

2 940 kWh
total output for the period
21 kWh/d
average daily output
12–13 h
peak output time
140 d.
period analysed
Used

Technologies & methods

Power Query From Folder Combine Files Data Cleaning Date Transformations Refresh Workflow Pivot Reporting Dashboard design Reporting Automation

Do you prepare similar reports by hand?

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Note: the example uses demonstration data; no real client information is shown.