Half-Hourly Electricity Data: What It Shows and How to Analyse It
A practical guide to turning a dense electricity CSV into a clearer picture of how a site uses energy — and what that may mean for solar, battery storage and future energy decisions.
An annual electricity total can tell you how much energy a site used. A monthly bill can show how much that energy cost. But neither necessarily explains what happened between the two.
Half-hourly electricity data can.
For every day of the year, the meter records consumption in 30-minute intervals. That means a non-leap year can contain up to 17,520 half-hourly readings — enough detail to show when energy is being used, not simply how much is used overall.
The difficulty is that the data usually arrives as a spreadsheet or CSV file. It may be perfectly usable by a computer while being almost impossible to interpret at a glance. Rows of timestamps and settlement periods do not immediately reveal a morning start-up, an evening peak, a weekend shutdown or a substantial overnight baseload.
What is half-hourly electricity data?
Half-hourly data is a record of electricity consumption at 30-minute intervals. Each day is divided into 48 periods, with each reading representing the energy used during one of those periods.
Viewed in sequence, the readings create a time-based picture of demand. Viewed together, they can reveal recurring patterns that are hidden inside an annual total.
The file itself may contain dates and timestamps, half-hourly consumption values, meter or site references, settlement periods and, where available, import and export readings. The exact structure varies between files, which is why useful analysis needs to begin by checking what has actually been supplied.
Why the raw file is difficult to understand
A half-hourly file is detailed by design. It is not necessarily designed for human interpretation.
- Thousands of individual rows arrive with no visual summary.
- Dates and times are difficult to compare manually.
- Weekday, weekend and seasonal patterns may be mixed together.
- Large peaks can be easy to miss in a long table.
- There may be no obvious distinction between normal behaviour and unusual events.
- The connection between usage patterns and possible technology choices is not immediately visible.
A user may know that a site used a certain amount of electricity last year, but still not know whether demand is concentrated during working hours, continues overnight, rises sharply in winter or changes substantially at weekends.
The patterns hidden inside the readings
When half-hourly data is organised and visualised, several useful questions become easier to answer:
When does the site use electricity?
A typical day profile can show whether demand rises early in the morning, remains steady through the working day, peaks late in the afternoon or continues at a similar level overnight.
Is the site different at weekends?
Comparing weekday and weekend profiles can show whether the site closes down, operates reduced hours or maintains a significant baseload outside normal operating periods.
Does demand change through the year?
Monthly views can highlight seasonal variation, holidays and operational changes that an annual total conceals.
Are there pronounced peaks?
A handful of high-demand periods may point to equipment start-up, heating, cooling, production activity or other site-specific behaviour.
Is there useful daytime demand?
For a solar assessment, timing matters as well as total volume. Electricity used while solar generation is available may have a different value from electricity used overnight.
Could storage change the timing?
A battery does not create energy. Its potential value comes from shifting energy between different times — for example, storing surplus solar for later use, or charging during a lower-cost period where the tariff and system arrangement make that relevant.
From a CSV file to a useful report
The value of analysis is not just in producing more charts. It is in arranging the information so that important relationships become visible.
A useful report may bring together annual and monthly summaries, typical daily load profiles, weekday and weekend behaviour, import and export views, solar generation and self-consumption estimates, self-sufficiency measures, solar-only and solar-plus-battery comparisons, indicative battery behaviour and DUoS time-band observations where relevant data and assumptions allow.
These are decision-support outputs, not a substitute for a site survey, final system design or project-specific commercial proposal. A good first assessment should help identify the questions that deserve deeper investigation.
Why timing matters for solar and battery assessments
Two sites can have similar annual consumption and very different energy profiles. One may use most of its electricity during the middle of the day, when solar generation is available. Another may have modest daytime demand but a strong evening peak. A third may operate continuously, with a meaningful overnight baseload.
Half-hourly analysis allows generation and demand to be compared across the same time sequence. This can provide a more useful indication of how much solar energy may be used directly, how much may otherwise be exported, when a battery could potentially shift energy, and how much grid import remains after solar or storage is considered.
Start with the data you already have
If you have a half-hourly electricity file, it may contain more insight than its rows and columns suggest. Access Watt Time & Frequency analysis tool for a quick visual interpretation of the consumption trends it contains.