The AI way to net zero – by Lisa Balk

Many Australian manufacturers are wary of artificial intelligence (AI) systems, thinking they have little to offer, or are not yet ready for deployment. Here Lisa Balk looks at how AI can support manufacturers reach net zero.

The global manufacturing sector stands at a critical crossroads.

On one hand, it is an economic powerhouse that fuels innovation, employment, and international trade.

On the other, it contributes significantly to environmental degradation, particularly through carbon emissions.

As countries, including Australia, ramp up efforts to combat climate change, manufacturers face increasing pressure to reduce their carbon footprint while maintaining profitability and competitiveness.

Achieving net zero emissions has become an urgent priority for many industries, and in this journey, artificial intelligence (AI) has emerged as a key ally.

In Australia, where manufacturing contributes a substantial share to the economy, the path to net zero emissions is crucial for both environmental and economic reasons.

The country has set a target of achieving net zero by 2050, a goal that aligns with global efforts under the Paris Agreement.

The promise of AI

To meet this ambitious goal, the manufacturing sector must leverage innovative solutions – among these, AI offers transformative potential in optimising energy management, improving operational efficiency, and minimising waste.

AI refers to a machine’s ability to perform functions that are normally associated with human minds.

It is estimated that AI-driven technologies can help business reduce their CO2 emissions by up to 10 percent and cut energy costs by 10-20 percent.

It also has the potential to deliver energy savings of up to 20 percent in buildings, and 15 percent in transportation systems.

In the context of manufacturing, AI has already made significant strides, from automating tasks and improving quality control to optimizing supply chains.

However, its growing role in promoting sustainability and helping industries achieve their environmental goals is where AI truly shines.

AI and energy management

Traditional manufacturing systems often rely on static methods of energy management. Decisions are typically made based on historical data, and operations are adjusted only when energy prices fluctuate or as a reactive response to changes.

AI, however, introduces a more dynamic, proactive approach. Through predictive analytics and real-time decision-making, AI enables manufacturers to anticipate changes in energy demand, optimise the performance of assets, and minimise their environmental impact.

One of the key areas where AI can help is in energy management through AI-powered forecasting.

Electricity prices exhibit intra-day variability and can fluctuate within a day due to market dynamics including changing patterns of electricity consumption, the availability of different generation sources, and overall market conditions.

Generally during periods of high demand, electricity prices increase due to the need for additional generation capacity.

During periods of low demand prices may decrease as a result of lower consumption and reduced pressure on the generation fleet.

Weather variations also impact both demand and generation patterns. Extreme temperatures can drive up heating or cooling needs, while changes in wind or sunlight levels influence renewable generation output.

Renewables have added new variables to energy sourcing

The increasing share of renewables sources into the energy mix has also introduced more variability into the supply side of the electricity market.

Market mechanisms, such as real-time pricing and incentive programmes for demand response, which encourage consumers to adjust their electricity usage in response to price signals, can also contribute to intra-day price changes by affecting demand patterns and prices.

Traditionally, production schedules were often planned based on static parameters. But, in today’s dynamic market conditions, this approach is no longer sufficient.

With energy prices constantly fluctuating businesses need to adopt a more agile and data-driven approach to operations.

At the heart of AI-powered forecasting is the ability to predict future trends based on a variety of data inputs.

For manufacturers, AI-powered forecasting can predict energy demand, price fluctuations, and even the availability of renewable energy sources like wind and solar.

By leveraging these insights, manufacturers can better manage their energy consumption and avoid energy wastage.

AI’s ability to optimize energy management through forecasting has direct implications for reducing carbon emissions.

By improving energy efficiency, enabling greater use of renewable energy, and minimising waste, AI-powered forecasting can play a critical role in helping manufacturers meet their sustainability goals.

Lisa Balk has more than 20 years’ experience across multiple sectors including construction, renewable energy, and property industries. Prior to joining GridBeyond Australia as Director of Sales, Lisa worked for AGL Energy in the Sustainable Business Energy Solutions division as the national commercial sales manager.

Picture: Lisa Balk