Reading a load profile: what 35,040 quarter hours tell you

A year has 35,040 quarter hours (35,136 in a leap year). Under registering capacity metering, the meter records a consumption value for every single one. That file, the Lastgang (load profile), is the most complete description a company has of its own electricity consumption. It shows far more than the annual consumption total, but only to someone who knows what to look for in it.

Where the data comes from

The load profile is supplied by the metering point operator, usually through an online portal or on request as a file in a standardised format. Under §61 MsbG, every connection user with a modern metering device or an intelligent metering system is entitled to access their own connection point’s consumption values, including the historical values of the last 24 months. A reliable analysis needs at least one full year, and preferably several, because a single month or week cannot distinguish seasonal effects, maintenance shutdowns or one-off events from a genuine pattern. An analysis of a short snapshot often mistakes an outlier for the normal case.

The annual load duration curve

Sorting all 35,040 values of a year by size, largest to smallest, instead of by time, produces the annual load duration curve. It shows not when a load occurs but how often a given load level is reached or exceeded. A curve that drops steeply at the start and then flattens out points to a few short peaks against an otherwise steady consumption. A curve that falls steadily points to operations without pronounced peaks. For sizing a storage system, this shape is often more informative than the raw peak value alone, because it shows how often a storage system would actually be needed.

Peakiness, base load and predictability

Three properties of a load profile largely decide its suitability for peak shaving. Peakiness describes the ratio between peak load and average load. A high value means a few short events dominate the capacity charge, which favours a storage system. Base load is the floor consumption practically never falls below, and shows what share of consumption occurs regardless of operating state. Predictability describes how similar load peaks are from day to day or week to week. Operations with regular, predictable peaks can be managed more precisely with a storage system than operations with irregular, rare outliers.

Shift patterns and day type

Splitting the load profile by weekday, shift and season shows whether peaks are tied to particular operating states, such as a production line starting up at the beginning of a shift, or cooling-load peaks in summer. These patterns determine how a storage system would need to charge and discharge to be effective, and how many cycles it goes through in a year. A pattern with a daily morning peak calls for a different operating strategy than one with irregular peaks spread through the day.

From load profile to storage sizing

Combining the annual load duration curve, peakiness, base load and shift pattern yields the two figures a storage sizing actually needs: the power the system must supply during a peak, and the energy content needed to sustain that power for the peak’s duration. Both figures can be derived directly from the load profile by checking how high and how long the peaks to be shaved are, and how often they occur in a year. A sizing that looks only at the single highest value often misses that several smaller, more frequent peaks together affect the capacity charge more than the one rare extreme.

Just as important is the number of cycles that follows from the load pattern. A storage system that catches a rare peak only a few times a year ages differently from one charged and discharged several times a day. That cycle count determines the cycle life a storage system must achieve over its planned service life, and belongs in every specification as a value derived from the company’s own load profile, not as a general rule of thumb from other projects.

Before any evaluation, a short plausibility check of the raw data itself pays off. Gaps in the time series, a meter change partway through the period under review, or isolated zero values from a brief communication outage in the metering system distort figures such as the annual load duration curve if they go unnoticed. A simple check for missing intervals, implausible jumps and the correct count of 35,040 values per year belongs at the start of every load-profile analysis, not at its end.

Common sizing mistakes

• Sizing based on annual consumption in kilowatt-hours alone, rather than on the actual peak load in kilowatts.
• Using a single sample week instead of the full annual series of 35,040 values.
• Ignoring planned operational changes, such as new machines or an added shift, that will change the future load profile.
• Failing to separate recurring peaks from one-off special events, such as restarts after a shutdown.
• Not checking whether an existing PV system already covers part of the load peaks before storage sizing begins.

Avoiding these mistakes grounds the storage sizing in a complete picture from several years of operating data, rather than a single figure. A load-profile analysis works through exactly these points on a company’s own data set before any specific storage size is discussed. As of September 2026.

Sources

1. §61 MsbG (consumption information for the connection user), gesetze-im-internet.de
2. Stromnetz Berlin GmbH, "Standardlastprofile ab 01.01.2025" (in German; 100,000 kWh threshold for standard load profile/RLM as current grid-operator practice)