Greenforecast.au

's electicity is currently:
% Renewable

Today:

Max: %

Min: %

Tomorrow:

Max: %

Min: %

Best EV charging time:

Reduce energy usage:

Highest ever Greenness:

7-day Outlook for :

Greenness

% Electricity from Renewables

By Fuel Type

% Greenness:

⬤ Coal
⬤ Gas
⬤ Imported fossil
⬤ Rooftop
⬤ Solar
⬤ Wind
⬤ Hydro
⬤ Imported green

┅ Greenness % ⬤ Coal ⬤ Gas ⬤ Imported fossil

⬤ Rooftop ⬤ Solar ⬤ Wind ⬤ Hydro ⬤ Imported green

Weather

Wholesale Price

¢/kWh on spot market

Max:
%

Q&A:

What is "Greenness"?

Greenness is the percentage of electricity that comes from renewables. 100% means all electricity generation for the region (state) is coming from renewable sources; 0% means none.

Why is this useful?

You could use this data to reduce your carbon footprint, learn more about electricity/renewables and - in some cases - reduce energy bills.

Reduce your carbon footprint:

If you use electricity when Greenness is high, your carbon footprint is lower. This simple-sounding claim is surprisingly complex; read below for details. Nevertheless, here are some ideas that can reduce your footprint:

Implement smart heating/cooling that drives harder during high Greenness

Choose when to charge batteries or other storage systems

Choose when to charge electric vehicles

Schedule electric hot water systems, washers and dryers

Arrange when energy-intensive workloads at your workplace are schedulled

[Your ideas here!]

Can you use this data to reduce your energy bills?

Unfortunately, everyday households can't yet take advantage of this data to lower bills. There are some retailers that offer pass-through pricing for electricity but since the 2022 energy crisis they are significantly more expensive than standard options.

Some large commercial/industrial customers are exposed to wholesale price fluctuations. It's plausible those customers could save by scheduling loads during predicted low prices

Where do the forecasts come from?

Every 2 hours, GreenForecast.au servers grab data from , a weather forecast and two other minor sources. This data is fed into a custom Machine Learning (ML) model which has been trained on 10 years (roughly a million data points) of data. The current model is an "ensemble" of around a thousand specialised sub-models which predict price and greenness on a 7 day horizon for 5 NEM regions (states).

For technical details, see below.

How accurate is it?

Greenness is "a little better than a weather forecast"; price is a little worse. Unsurprisingly, accuracy for tomorrow is better (about 2x) than 7 days away. On a held-out validation set of data, the model predicts Greenness with an average error of less than 5% and price with average error of 2¢/kWh, depending on region.