Interval meter data is a record of how much electricity a property imports, and exports, in every short time block, usually each 30 minutes and sometimes each 5 or 15 minutes. A quarterly bill tells you how much energy you used; interval data tells you when you used it. That timing reveals the constant background load, the appliances that cause spikes, how usage changes between weekdays and seasons, and how well a solar system or battery would match the way a household or business actually uses power.

What the data looks like

At 30-minute resolution there are 48 readings a day and 17,520 in a normal year. Each reading is the energy in kWh that passed through the meter during that interval. The data is commonly supplied as a CSV file in a standard national format known as NEM12, and it can usually be downloaded from a retailer's online account or requested from the retailer or network.

A few points make the file easier to read:

• Import and export are separate channels. In NEM12 files, import channels are commonly labelled with an E and export channels with a B.
• Controlled load may appear separately. An off-peak hot water circuit often has its own channel.
• Quality flags matter. Readings can be marked as actual, estimated or substituted, and estimated periods should be treated with caution.
• kWh per half-hour converts easily to average power. Multiply by two: 0.6 kWh in a half-hour means an average of 1.2 kW across that interval.

How smart meters capture import and export in the first place is explained in smart meters and solar.

Finding the baseload

Baseload is the floor that usage never falls below, typically visible in the small hours when the household is asleep. It comes from refrigerators and freezers, network equipment, standby power, security systems, pool pumps on timers and anything else that runs around the clock.

Small numbers add up. A baseload of 0.3 kW, which shows up as about 0.15 kWh in every overnight half-hour, equals 7.2 kWh a day and around 2,600 kWh a year. Comparing the overnight minimum over several weeks can reveal a second fridge, an old pool pump or equipment left running. For a battery, baseload sets a large part of the overnight energy that storage would need to cover.

Reading spikes and patterns

Above the baseload, the data shows when larger loads switch on. Some common signatures are:

Laying 365 days side by side as a heat map, with time of day on one axis and date on the other, makes seasonal patterns stand out immediately. For a business, the same method shows start-up peaks, after-hours equipment left running and the half-hours that set demand charges.

An illustrative day, read step by step

Consider a hypothetical home's winter weekday, read from its half-hour import readings. The figures are illustrative only:

• Midnight to 6 am: steady readings of 0.15-0.2 kWh, a baseload of roughly 0.3-0.4 kW.
• 6:30 to 8 am: readings jump to 0.8-1.0 kWh as heating, the kettle and breakfast appliances run.
• 9 am to 3 pm: readings fall back close to baseload because the home is empty.
• 5 pm to 9 pm: readings of 1.0-1.5 kWh with heating, cooking and entertainment.

The evening block alone is around 10 kWh (eight half-hours averaging about 1.25 kWh), and overnight baseload adds a few more. That shape describes a household whose benefit from solar depends heavily on storage or on shifting some loads into the middle of the day, because most of its energy is used when panels produce little.

What changes when a home already has solar

Once solar is installed, the meter only sees the net result at the boundary of the property. Solar used directly inside the home never passes through the meter, so import data alone understates true consumption.

To rebuild the full picture, combine meter data with generation data from the inverter's monitoring: consumption equals solar generation, minus exports, plus imports. The result shows how much solar is being self-consumed, how much is exported at a low rate, and how much evening energy is still imported at full price, which is exactly the gap a battery is designed to fill.

Using the data to size solar and batteries

Interval data turns sizing from rules of thumb into evidence. Typical steps include:

1. Annual and seasonal energy. Total yearly use, and how much it swings between summer and winter, sets the starting point for solar size.
2. Daytime share. The proportion of usage in solar hours shows how much generation would be used directly rather than exported.
3. Evening and overnight energy. Summing imports from late afternoon to the next morning on typical days indicates useful battery capacity. Sizing to the heaviest winter night usually leaves a battery under-used for most of the year.
4. Peak power. The highest half-hour averages indicate the inverter and battery power rating needed to cover evening loads.
5. Tariff fit. Overlaying the usage profile on time-of-use tariff windows shows what shifting energy is worth.

Limits of 30-minute data

Half-hour averages smooth out short events. A kettle, oven and air-conditioner running together for five minutes might briefly draw 8 kW, yet the interval may show an average of only 2-3 kW. That matters for backup design, where the battery inverter must handle instantaneous loads and motor start-up surges. Interval data also cannot name individual appliances, and gaps or estimated readings can distort averages. Short-term monitoring with current clamps on individual circuits, fitted in the switchboard by a licensed electrician, fills these gaps where detail matters.

Next steps

Twelve months of interval data is one of the most valuable inputs for designing a solar or battery system that fits how a property really uses energy. To have your data reviewed as part of a system design, request a free assessment from Blue Energy Solar. The energy market also lists a Battery Feasibility Study from $199 per property and an Interval-Data Analysis for businesses from $490 for 12 months of data; prices are indicative and confirmed after assessment.

Frequently asked questions

How much interval data is needed for a reliable analysis?

Twelve months is ideal because it captures summer cooling, winter heating and holiday periods. Three to six months can still support a useful first estimate if it is adjusted for the missing seasons using bills or typical patterns. Data covering a period of unusual circumstances, such as renovations, an extended absence or a new household member, should be flagged so it does not skew the result.

What if the property does not have a smart meter?

An older accumulation meter records only a running total, so bills show energy per billing period but not when it was used. Analysis then relies on bill totals, an appliance list and knowledge of household routines, which gives a rougher estimate. A meter exchange is normally arranged when solar or a battery is installed, and interval data starts building from that point.

Can interval data be analysed in a spreadsheet?

Yes. After converting the file into a simple table of dates, times and kWh values, a spreadsheet can average usage by time of day, total energy by month and chart typical days. The NEM12 layout groups readings in rows by day, so some reshaping is usually needed first. For larger sites or combined solar and battery modelling, dedicated analysis tools save time and reduce errors.