The latest report from Itron indicates a significant uptake of artificial intelligence and machine learning among global utility companies, highlighting both the benefits and challenges of these technologies.
The latest Resourcefulness Insight Report by Itron has unveiled that a significant majority of utility companies worldwide are adopting artificial intelligence (AI) and machine learning (ML) technologies. The findings, drawn from a survey of 600 utility executives spanning the United States, Canada, France, the United Kingdom, India, and Australia, indicate that over 80% of these companies are engaging in AI and ML projects. Notably, nearly a quarter of these executives have integrated these technologies fully into their operations, while more than half have invested substantially in mature AI/ML projects.
This widespread integration underscores the pivotal role of AI and ML in managing the complexities of a rapidly evolving energy sector. As utilities face decentralisation challenges and the need for real-time decision-making and action, digital technologies, particularly AI and ML, have become indispensable tools.
Conducted annually, Itron’s Resourcefulness study provides insights into various facets of the energy system, with the current edition focusing on the uptake of AI and ML among global utilities. Safety emerges as the primary application for AI and ML, addressing key utility challenges including detecting and managing hazardous situations. Furthermore, the technologies facilitate cyber threat detection, optimise asset utilisation, help achieve sustainability objectives, and enhance consumer engagement and satisfaction.
Despite these advantages, implementing AI comes with its challenges. AI, particularly generative models, is known for its energy-intensive nature, contributing to a growing energy demand prompted by expansive data centre operations, construction, and sustainability efforts like renewable energy and electric vehicles. Approximately one-third of surveyed executives identified this burgeoning energy demand as their foremost challenge.
A lack of expertise is highlighted by almost half of the respondents as a significant barrier to AI and ML implementation. Additional concerns include the perceived high cost of implementation, challenges related to data governance, standardisation, scalability, and the risks linked with untested technology in critical infrastructures. Nearly two-thirds of executives stressed the importance of deploying well-proven technology and also emphasised the need for continuous support, regulatory compliance, and comprehensive employee training.
Marina Donovan, Vice President of Global Marketing, ESG, and Public Affairs at Itron, remarked on the survey’s implications, noting the simultaneous increase in electricity demand and the emphasis on safety. She stated, “As utilities advance towards a smarter and more connected grid, the integration of AI and ML becomes essential and strategically beneficial to address contemporary challenges.”
To maximise the potential of AI and ML, the availability of high-quality, reliable data for model training is imperative. In this regard, edge intelligence, derived from devices such as sensors and smart meters, is crucial. More than a third of survey participants believe AI and ML could enhance edge intelligence through real-time anomaly detection and improve distribution system efficiency. Moreover, these technologies are expected to assist in better managing demand response, load shedding, and shifting.
In conclusion, Itron references Gartner’s five-step approach to implementing AI, beginning with small, swiftly resolvable use cases. Subsequent steps involve skill assembly, appropriate data gathering, selecting suitable AI techniques, and structuring expertise and accumulated knowledge.
Marina Donovan concluded that the findings clearly demonstrate the emergence of AI within utility operations, asserting that strategic deployment of AI and ML is critical for boosting safety, strengthening consumer relations, and achieving sustainable long-term goals. The report underscores the readiness of utilities to leverage AI and ML as decisive factors in the evolution of smart utility management.
Source: Noah Wire Services
