Published: July 22, 2024

Interval Data Analysis

Our upgraded interval data processing system makes it easier than ever... Here's how it works:

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Interval data analysis has long been a keystone of Solar Proof. After all there is no better way to project energy impacts and savings than to start with a strong, data-based understanding of a client's current energy needs. Here we are going to look at:

• What is an interval data file?
• How does Solar Proof interpret different data files?
• What makes this analysis so great?

So let's take a look at how Solar Proof analyses data files to provide accurate consumption and savings estimates for our clients. You can use our Free Interval Data Report tool to test any data files you have, and get a fancy PDF like this one:

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What is an interval data file?

If you're new to the solar/energy industry, you might be wondering what interval data is. It is different to an energy bill! A power bill usually only tells you a) How much energy you use on average each day (or in a given period of time) and b) How much you need to pay for that energy.

Interval data on the other hand is, as the name suggests, data about your energy use given as intervals.

This data is not always available since it depends on the type of metering at your premises. When it is available then it can be provided as a spreadsheet (csv or excel) to you (the customer) or an authorised representative.

Interval data may be provided in a number of formats and at varying intervals. Commonly it may be given as 30 minute intervals but it may come in 1 hour, 30 minute, 15 minute or even 5 minute intervals.

What about logger data?

Solar Proof is not optimised to accept logger data

If you put a data logger on a premises to obtain interval data it may be recorded in a very high resolution (1 minute or less) which is not ideal for Solar Proof since our purposes only really require a resolution of 1 hour data.

You may be able to adjust the settings on your logger to obtain 5 minute or 15 minute intervals. If not, you can also create an Excel spreadsheet to transform the data for you. We can also help with this process but will require a minimum of 7 full days of captured data.

So, Solar Proof has to be ready and capable of handling data in a huge variety of formats of data file. And it is!

How does Solar Proof interpret different interval data files?

When you upload a data file to Solar Proof, it will first make sure the format is acceptable (e.g. CSV or similar). Then it makes a distinction between Horizontal data (e.g. NEM12 data) and Vertical data. This allows us to interpret the data correctly.

Next, we make some determinations to assess the time interval which is being provided.

Then we do the rest of the magic! Sum the necessary intervals to form hourly data and check for data validity. If there is a full year of data (or more), we take the most recent 12 months. If there is not a full year, we attempt to extrapolate the data using weekday averages to create a full year.

What makes this analysis so great?

For a whole bunch of data file types, Solar Proof will look at it and automatically create a compatible 12 months of data to be used in the platform for analysis.

We have just (July, 2024) launched a giant update to ensure our data interpretation is second to none!

The analysis tool will accurately interpret 22 different data file types. Plus, we've tested the data accuracy of these files to be 99.95% accurate.

The reason for some very slight inacurracies creeping in can be due to data collation, rounding, and a few other spooky anomolous data phantoms.

Example Interval Data Files (Tested)

Here are some data files we have collected with permission to demonstrate the compatible formats in Solar Proof:

Summary

While there may be infinite interval data formats out there, Solar Proof is setup to handle more than 20 different types with speed and accuracy. Average processing speed is about 16 seconds although this depends on the complexity of the task.

If you'd like more information on how to obtain interval data, check out this article.