How to analyze your electricity interval data

Prepare your meter export

You can bring a meter export as it comes from your metering portal. The analyzer supports:

• CSV and XLSX files
• One combined timestamp column, or separate date and time columns
• kW average interval demand, or kWh per interval
• 5, 10, 15, 30 or 60-minute readings
• Start-stamped or end-stamped timestamps
• Local timestamps interpreted in a meter timezone you select

You do not need to reformat or clean the file before starting. How timestamps and values are interpreted is described under interpreting intervals.

Step 1 — Map data

After you open a file, the analyzer proposes a mapping of its columns and you confirm or correct it. The choices are:

• Timestamp layout: a combined timestamp column, or separate date and time columns
• The value column, and whether it holds kW or kWh per interval
• Whether timestamps mark the start or the end of each interval
• The meter timezone
• The date order and number format, where the file needs them
• The reading interval

The reading interval is detected automatically when possible and can be overridden if you know the meter's actual cadence. These choices determine how every row is interpreted, so check them before selecting Analyze. See interval interpretation and timezones and daylight saving time.

Step 2 — Check quality

The analyzer reports data-quality issues before presenting any figures. Some issues are reported while analysis can continue; others block analysis when proceeding would require guessing or could produce misleading results.

• Missing intervals. You do not need to fill missing intervals before analysis. They remain missing and are reported rather than interpolated.
• Duplicates. Identical duplicates are removed and reported; conflicting duplicates — the same timestamp with different values — block analysis because the analyzer will not guess which reading is correct.
• Unreadable values or timestamps. Rows that cannot be parsed are excluded and reported; a high failure rate blocks analysis.
• Incomplete timestamps. A timestamp with a date but no time, or a time but no date, is reported rather than completed by assumption.
• Daylight-saving ambiguity. When clocks go back, repeated local times are never guessed — you choose per group whether they belong to the first or second pass, or are excluded.
• Cadence changes. If the reading interval changes part-way through the file, the analyzer reports the detected segments and offers to analyze one segment separately.

The rules behind each message are documented under data quality, daylight saving time and cadence changes.

Step 3 — Review analysis

Once quality checks pass, the analyzer presents the results. In practical terms:

• Total energy, average demand and maximum interval demand over the analyzed period
• Load factor — how close average demand runs to the maximum
• P95 and P99 — the demand levels exceeded only 5% and 1% of the time
• The chronological load profile
• The load duration curve — the same intervals sorted from highest to lowest
• Monthly maximum interval demand
• The weekday and time-of-day demand pattern
• The top intervals by demand

Definitions and formulas are documented under summary metrics and percentiles and the load duration curve.

Step 4 — Test scenarios

Two optional what-if tools work on the same analyzed data.

The peak-limit simulator reports how often and by how much demand exceeds a limit you choose, and the energy above it. It analyzes the measured profile, or the active additional-load scenario when one is applied. Nothing is shifted or reduced.

The additional-load simulator adds a constant load in kW over selected weekdays and a local-time window; overnight windows are supported. Only observed meter intervals are changed — missing intervals are not filled — and the schedule coverage figure shows how much of the requested time actually has meter observations. Energy is calculated from real elapsed time, so a window crossing a daylight-saving change lasts exactly as long as it does on the clock. See additional-load scenarios and peak-limit analysis.

Export normalized data

After a successful analysis you can export the normalized interval records as a CSV file, generated locally in your browser. The export carries the resolved interval timing and the converted demand and energy representation, ready for use elsewhere. The exact representation is documented under normalized export.

What you do not need to do

• No account is required.
• The meter file does not need to be uploaded to a server.
• Missing intervals do not need to be manually filled.
• No scenario setup is required to get the base analysis.