Machine Learning and its relationship to climate change mitigation

Content provided by David Moret from DPM Consulting Group

Flood impact and risk assessments are critical reviews which need to be analysed to decrease and mitigate damages to people, community, and infrastructure. The recent flooding events in New South Wales and Queensland have shown how our environment is becoming increasingly susceptible to the impacts of climate change.

Heavy rainfalls and severe weather conditions between February and April 2022 led to loss of life, significant economic costs, fuel, food, and water shortages.

The latest report from the Intergovernmental Panel on Climate Change shows that although greenhouse gas emissions have slowed down in recent years, but have continued to grow, leading to a continuous rise of the average temperature of the planet which has an impact on the rainfall intensity.

As demonstrated by recent events in Queensland and New South Wales, our climate is becoming warmer, characterised by a decrease in the frequency of rainfall events and, simultaneously, experiencing an increase in the intensity of rainfall. Based on the data collected in the past and during the recent flooding events, professionals are able to determine the potential flood impacts of proposed developments and results show the potential change in variables, such as velocities, flood depths etc.

Due to the increased risk of flooding related to climate change and to population growth near coastal or riverine areas, it has also become fundamental to develop quickly and accurately estimate the probability of flooding in real time. It has been demonstrated that machine learning is at the forefront of providing a tool for hydrologists, modellers, and the industry to accurately analyse large amounts of data and provide results which can be easily used by practitioners to better inform hydrological and hydraulic models.

But what is machine learning? As defined by Sara Brown from Massachusetts Institute of Technology “Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behaviour. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems”.

Different machine learning models can be used depending on the situation and outcome that the user requires. For example, real-time river height and gauges are fundamental in determining flood forecasting during a rainfall event. However, this information is often inaccurate and affected by different issues (such as loss of data, sensor noise etc.). Machine learning is able to analyse and clean this data, find hidden patterns and make flood predictions which can then be provided to the public as well as emergency bodies.

Machine learning offers a wide range of opportunities, solving complicated issues more efficiently, with less costs, and demonstrating an excellent performance in flood prediction and flood risk assessment¹.

Machine learning provides a fundamental tool which should be considered within the development process to not only accurately providing real time warning and predict flooding which could potentially save lives and money, but also offers an instrument to further develop and investigate various aspects of flood impact and risks. Combining machine learning with the traditional methods and techniques of the stormwater industry, allows professionals to offer accurate and robust results which can be used for emergency purposes, minimising costs and saving lives.

¹Ighite, EH, Shirawaka, H & Tanikawa, H 2022, ‘Application of GIS and Machine Learning to Predict Flood Areas in Nigeria’, Sustainability, vol. 14, no. 5039.

Delivering a zero-emission neighbourhood

Songbird Oxley, an EnviroDevelopment certified project in Brisbane, Queensland, is being delivered as one of Queensland’s first zero-emission communities. What does this mean? The energy harnessed from each home will be greater than the energy used by the households that live in them. In addition, to amplify this effect, all homes will be grid connected so they can export any excess energy and bank the renewable energy from the grid.

As part of the Songbird Design Guidelines, every house must be equipped with the minimum renewable energy system specifications of:

• 6.2 kW solar
• 13.5 kW/h Tesla Powerwall 2 battery
• Fronius single phase inverter
• On/off grid backup capability
• Virtual Power Station ready

A $5,000 Performance Rebate is available to eligible buyers who install the system with the preferred supplier, Natural Solar. The benefits of a single provider were to deliver cost competitiveness from a group buy and to ensure compliance with standards as set by Energex, the electricity network provider to enable a 100 percent solar / battery neighbourhood. Each renewable energy system has an export limitation of 4 kW (to help provide for the 100 percent local solar uptake in the electricity grid).

The cost of the system based on group buy by Economic Development Queensland’s (EDQ) deal with Natural Solar for homeowners at Songbird is less than half the market rate.

While every home and homeowner’s energy consumption varies, modelling of a typical family home show savings of around $1,800 to $2,000 per year, meaning a best in market return on solar plus battery investment for home buyers. Resilience is another benefit.  When there is a grid blackout due to a storm, most solar and batteries stop working. However, this is not the case using the Tesla Powerwall. The Tesla Powerwall is a battery that stores energy, detects outages and automatically becomes a home’s energy source when the grid goes down.

As the number of homes with solar increases, the value and return on home solar through the feed in tariff can only decline. The Tesla Powerwall with Virtual Power Station functionality, however, gives homeowners the option to join a trading platform that provides them with a choice to trade their excess solar and battery for a higher price. In order to provide the highest possible upload of excess solar energy across every home in Songbird, the inverter export is required to be capped to 4 kW. The Virtual Power Station though will help maximise a home’s excess energy export.

It isn’t just on renewables that the Oxley Songbird sings on sustainability. Songbird homes in addition to solar PV and battery storage also come with heat pump hot water systems, efficient WiFi air conditioning and will be electric vehicle ready with a dedicated circuit to the garage.

Inflation and Sustainability

Written by Marcus Brown, Bull & Bear Economics

As we all learnt in the Federal election, the annual rate of inflation as at the March quarter of 2022 had reached 5.1 percent (or 3.7 percent if adjusted for volatile movements).  Over the past 12 months, fuel prices have increased from $1.60 per litre to as much as $2.20. The Australian Energy Regulator announced in late May 2022, that the Default Market Offer (DMO) price for electricity would rise by as much as 8.2 percent above inflation after 1 July 2022.  This increase in the DMO price has been driven by increases in wholesale electricity prices in New South Wales and Queensland of 40 percent-50 percent respectively.

Energy is a major input into nearly every product or service we purchase.  Rising energy prices are influenced by myriad factors many of which are unrelated, for example COVID supply chain friction and the invasion of Ukraine.

Historically, the threat of energy cost inflation has been a key driver in the adoption of solar panels, with consumers looking to de-risk their energy bills through onsite generation.

Natural gas has traditionally been seen as a cost-effective alternative to electricity for cooking and heating, however gas prices are experiencing significant increases as the global demand for gas surges, particularly with sanctions on Russian energy exports, and a pattern of almost annual write-downs in proved and probable natural gas reserves by Queensland’s major LNG producers.  While many householders will continue to prefer gas for cooking purposes based on familiar or cultural experience, the shift away from gas for heating will accelerate.

Economists tend to view things differently to other professions.  From an economist’s perspective a major driver of consumer behaviour is self-interest.  Households are not commercial enterprises, but householders seek to minimise costs and maximise private benefits.  The key outcome from consumer research we have undertaken indicates that many householders make investment decisions based on four key factors, which we like to call the four C’s:

• Cost
• Comfort
• Convenience
• Certainty

The priority of each of these factors varies based on where households sit on a life cycle.  For example, those on fixed incomes (e.g. retirees and pensioners) tend to prioritise ‘certainty’.  In other words they will take steps to de-risk their future costs, consistent with the uptake of solar panels by this group.

Measures related to home insulation or cross ventilation are viewed as ticking the box in reducing an ongoing ‘cost’, but also delivering ‘comfort’.  ‘Convenience’ relates to how easily a measure can be adopted.  For owners of existing homes where sustainability measures require retrofit, ‘convenience’ or lack thereof can be the definitive impediment to the adoption of such measures.  The sales of water tanks collapsed when they ceased to be mandated as part of new builds, but they continued to fall away once businesses stopped providing a ‘one stop shop’ for tank installation.

In the immediate term, high energy inflation could be one of the most significant drivers of the adoption of energy saving measures in the home and adoption of electric vehicles.  For nearly 30 years, inflation has not been a significant issue for the Australian economy to address, however now that it has returned as a policy challenge, the risk it poses to household budgets and business costs will invariably prompt energy consumers to investigate means of mitigating price risk and reducing costs to maintain a level of comfort and productivity.