Data Connectors

Electricity data in the tools you already use

Analysis-ready CDR electricity feeds. Cleaned, formatted, plug and play.

Get a feed API docs

  • Microsoft Excel

    Report and model in a Spreadsheet

  • Google Sheets

    Collaborate in Sheets & Looker Studio

  • Power BI

    Build Dashboards and Reports

  • Python

    Notebooks, Dataframes and Models

Raw to ready

We do the cleaning. You do the analysis.

Retailer data arrives nested and hard to analyse. We turn it into tidy tables you can work with, chart and model straight away.

  • One row per interval, with the tariff period attached
  • Substituted and estimated reads flagged
  • Same columns across every retailer
NMI 2001xxxx41 · YYYY-MM-DD
nmiinterval_startkwhtariff
2001xxxx41YYYY-MM-DD 06:300.412shoulder
2001xxxx41YYYY-MM-DD 07:000.587peak
2001xxxx41YYYY-MM-DD 07:300.633peak
2001xxxx41YYYY-MM-DD 08:000.498peak
2001xxxx41YYYY-MM-DD 08:300.355shoulder

Simplified, illustrative values.

What's in every feed

Core tables for every account.

  • Usage

    Interval reads from the meter, down to 5 minutes on smart meters.

  • Billing

    Line by line invoices: supply, usage, discounts and solar credits.

  • Plans

    Current plan, tariffs, contract terms and feed-in rates.

  • Sites

    Every NMI, its meters and any registered solar or battery.

Ready to connect

We've built connectors to Excel, Power BI, Google Sheets, Python and more.

Power BI report showing an electricity load profile, monthly energy and a demand heatmap
Power BI load profile
Excel pivot table of electricity bills by site and statement date
Excel pivot
Excel dashboard of daily energy by time-of-use band and bill history by charge type
Excel dashboard
Excel sheet with demand figures, a weekly heatmap and weekday against weekend demand
Excel demand analysis
Power BI report of five-minute usage coloured by tariff period, with spend by period
Power BI time of use
Power BI report of bills by month and charge type, with a bill-by-bill summary
Power BI billing
Google Sheets workbook of electricity bill line items for each account
Google Sheets
Python data feed ingestion 
import requests
import pandas as pd
import io

# declare the url properties
base_url = "https://api.voltdata.com.au/data-feed/data/"
datafeed_id = __DATAFEED_ID__
datafeed_token = __DATAFEED_TOKEN__

# create the url
url = base_url + datafeed_id

# make the request
payload = {}
headers = {
  'Authorization': 'Bearer ' + datafeed_token
}
response = requests.request(
  "GET", url, headers=headers, data=payload
)

# Convert response to a DataFrame
df = pd.read_csv(io.StringIO(response.text))

# Print the DataFrame head
print(df.head())

Questions and answers

About the data source.

1. What is the Consumer Data Right (CDR)?

An Australian Government scheme that lets people and businesses share their data securely with accredited parties. In energy, account holders can share usage, billing and account details. Volt Data is not accredited under the CDR; we are a CDR Representative of Fiskil, the Accredited Data Recipient that collects and discloses CDR data to us.

2. How does the account holder give consent?

They pick their retailer, log in to verify who they are, and choose what to share. It is done online and usually takes a few minutes.

3. How long does consent last?

Up to 12 months. After that the account holder can authorise again. They can withdraw consent at any time through their retailer, Fiskil's portal or by contacting Volt Data.

4. Are all Australian retailers available?

Large retailers are integrated. Small and medium retailers are integrated to varying extents. The CDR Register lists which retailers take part.

5. Is Volt Data available outside Australia?

Not yet. Feeds cover Australian electricity accounts with retailers that share data through the CDR. Sadly, this doesn't cover Western Australia or the Northern Territory.

Have any questions? Get in touch

Ready to connect your data?

Tell us what you're working on and we'll come back to you.

Get a feed hello@voltdata.com.au