AI in the utilities sector
One of the most exciting tech trends emerging in the utilities sector is artificial intelligence (AI) and the optimisation potential that it brings.
The reason for AI’s increased prevalence lies in the increasingly large volumes of data that companies are able to gather each day - some estimate that global data generation will reach 463 EB by 2025.
Assuming that a utility company’s systems are integrated via cloud and connected via a digital ecosystem of IoT sensors, streams of data - from equipment status to energy output - can be relayed back and create a valuable resource of information.
It is by leveraging this data with advanced AI analytics programmes and machine learning software that companies can unlock latent potential, optimise energy output and streamline systems.
A survey by Gartner found that the number of enterprises implementing AI had grown by 270% between 2015 and 2019:
“Four years ago, AI implementation was rare, only 10% of survey respondents reported that their enterprises had deployed AI or would do so shortly. For 2019, that number has leapt to 37%,” said Chris Howard, VP, Gartner.
Furthermore, the International Data Corporation anticipates that global spending on AI systems will reach almost US$98bn by 2023.
Benefits of AI for utilities
So, if the trend is for AI to merge with business generally, what are some of the core benefits specific to its deployment within the utilities sector?
Predictive maintenance: With IoT equipment monitoring vital machinery and equipment, the collected data can be relayed to AI analytical software, which will then assess the maintenance status of core components and anticipate faults before they occur.
This not only saves money by effecting repairs before damages become more costly, but also mitigates the need for protracted and sometimes dangerous physical inspections by an engineer.
Optimising energy output: A constant flow of data means that minute changes in energy output can be recognised early and adjusted accordingly. This not only streamlines the business but also has cost-saving implications for both operators and consumers.
For instance, GE Renewable Energy’s Digital Wind Farm makes use of software which monitors its turbines in real-time, allowing engineers to optimise energy output significantly - GE claims to have increased production 20% and made over $100mn in extra revenue.
Improved forecasting: More data and the ability to analyse it quickly means that utility companies can make energy usage predictions with greater accuracy than ever before.
Leading AI-insights providers like Autogrid help their customers to understand utility usage trends by collecting, storing and managing data via a network of optimisation algorithms which form aggregations and forecasts.
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