The Energy Made Easy interface

In 2017, power contributed to 3.04 per cent of Australian’s annual household spend, with electricity costs rising by 9 per cent and gas and other fuels by 21 per cent in the previous six years.

As Australia’s energy market continues to fluctuate and transform in response to national and worldwide change, finding the best energy plan is crucial, but it’s just as important to ensure that process is as uncomplicated and accurate as possible.

The release of a new algorithmic-powered system is now making finding the best energy plan easier, more accurate and future focussed. Developed by the Web Geospatial Systems Group at CSIRO’s Data61 using advanced analytics and predictive modelling science, the Australian Energy Regulator-hosted system can now provide bill estimates based on past usage and evaluations of several new pricing structures.

The new system uses data provided by consumers’ unique National Meter Identification number to accurately calculate and estimate usage and billing on another plan provided by a competing company.

“Multiple new features that were not previously available now are with this update to the system, such as the option to import smart metre data,” explains Data61 co-developer Paul Haesler.

“The new system empowers home and business energy users to find the best pricing structure for their needs and provides a forward-looking platform that can keep up with a rapidly evolving energy market.”

The updated platform, called Energy Made Easy, is the Government’s primary tool to provide Australians with reliable and trusted information on a range of comparable energy plans.

Smart meter, Gas meter, Light globes, Power poles, solar panels and flowers. Photography by Quentin Jones. August 10, 2018.

The estimation process works by collating and analysing essential data provided by the customer either in the form of a bill or a smart metre download.

Using this information, the algorithm builds a realistic usage model that incorporates and benchmarks usage patterns based on daily and seasonal variations typical for the consumer’s area and climate.

A model of the plan’s billing structure is also created, incorporating data on days and times where particular tariffs apply, such as peak and off-peak times, along with fees and discounts.

The two models are then combined to calculate a feasible and comprehensive estimate.

“Each individual part of the calculation is quite simple,” says Haesler. “However, the challenge lies in ensuring all the elements work together consistently.”

While the methodology of this system is closely tied to the regulatory frameworks of the energy market in Australia, the approach could be adapted for use in any regulated market with a volumetric billing structure, such as internet and mobile provider plans.