In October a US congressional subcommittee heard testimony about the role artificial intelligence could play across the energy industry – from the discovery of new generation sources to better predictions for electricity demand. The tone was generally optimistic. “Utilities will be able to utilise the benefits of AI for maintaining or improving safety, affordability, efficiency and environmentally-friendly energy production,” said Jeremy Renshaw, senior technical executive for AI, quantum and nuclear innovation at the Electric Power Research Institute.
Here are some of the considerations for companies evaluating AI projects as part of their corporate sustainability efforts.
Automation isn’t a panacea
Companies seeking to deploy AI for building management applications should focus first on ensuring that existing lighting and heating, ventilation and air conditioning (HVAC) systems are operating efficiently. If you have stuck valves or dampers in your building, you have uncalibrated sensors, AI is not going to fix that for you,” said Andrew Knueppel, workplace engineering manager for real estate services firm Cushman & Wakefield. “It’s going to basically take in that bad data and give you bad outputs. If you have congested networks that the AI can’t read, if you have isolated systems or proprietary protocols, you’ll basically be stuck, and you won’t be able to get that data out in order to use it for any application.”
Quality trumps quantity when it comes to data
Charles Tripp, senior scientist for AI at the National Renewable Energy Laboratory, underscored the importance of choosing the data used for AI applications carefully. It’s not only a matter of screening for potential bias, but also of choosing the most robust metrics for the models,” he said. “If you need more data, how much does it cost to collect that data? Or do you get it from a third party that might have generated it? If you’re generating it synthetically, are there privacy issues involved, or contractual issues involved with getting that data and using it for certain purposes?”
Using AI can increase energy consumption
According to Jennifer Huffstetler, chief product sustainability officer at Intel, close to 70% of corporate chief information officers surveyed by Intel are concerned about the energy consumption associated with deploying AI within their organisations. “That’s particularly true of training generative AI applications such as ChatGPT, which can require thousands of megawatt-hours of computing time,” she said.
This makes it imperative for sustainability teams to collaborate closely with their corporate IT departments in the planning phase. Underscoring Tripp’s remarks, Huffstetler said there are three pillars to consider for deploying AI sustainably:
• Optimising the efficiency of the software algorithms and the hardware they run on.
• Running the computing workloads on grids powered by renewable energy.
• Using only data that is necessary for the model.
Leaving humans out of decision making is a mistake
“Companies should ensure that facilities teams and building occupants understand the algorithms machine learning systems used in order to intervene as appropriate,” said Knueppel. “This means starting small, and monitoring results closely to verify their accuracy.”
He offered the example of an AI application that falsely predicts the need for maintenance on a fan or piece of equipment. “If you’re using the AI for building control, so everything in the building is being actively manipulated by the AI, and you’re getting complaints in the building, which will always happen, you’ll never be able to get rid of those if you put operators in the position where they don’t understand why the building is doing what it’s doing,” he advised.
Transparency, in other words, is key.
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