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How AI-driven cable management can boost Southeast Asian power networks

Adoption of AI-driven cable management can help accelerate the region’s energy transition while delivering benefits to consumers.


In brief

  • Reliable power distribution networks are needed to address rapidly growing energy demands and the energy transition. 
  • Predictive analytics, artificial intelligence (AI) and machine learning could be game-changing for power distribution as these help enhance grid resilience.

  • Developing countries that quickly adopt AI-driven cable management can modernize their grids while avoiding inefficiencies associated with legacy systems.


With up to 65%1 of Southeast Asia’s population — more than 500 million people2 — expected to live in urban areas and electricity demand set to triple3 by 2050, the reliability of power distribution networks is essential to support economic growth in the region. Accelerating urbanization is driving the need for greater reliance on underground power cables, especially where more robust, space-efficient power distribution is needed and overhead lines are impractical.

Increased cable failures during heavy rain or wet seasons are common in Southeast Asia, raising the risk of outages during peak demand periods. While underground power cables offer advantages, such as reduced exposure to weather-related disruptions and aesthetic benefits, they are not immune to failure. Aging infrastructure, insulation degradation, defects due to improper handling and environmental stressors are other factors contributing to unplanned cable outages that disrupt services, increase operational costs and frustrate consumers.

To manage these challenges, predictive analytics, artificial intelligence (AI) and machine learning (ML) could be game-changing for power distribution. Such technologies can be used to help utility companies anticipate underground cable failures before they occur, optimize maintenance schedules and enhance grid resilience.

Use of predictive analytics in underground cable management 

Predictive analytics is fundamentally transforming how utilities manage underground cable networks. By leveraging historical data and real-time information collected from the Internet of Things (IoT) sensors embedded in cables, utilities can identify patterns that signal potential failures. This shift from reactive to proactive maintenance has demonstrated measurable success in various parts of the world.

For instance, a utility company in France has been using AI for several years to help optimize network renewal decisions. Initially applied to low-voltage underground networks, their machine learning models are now also used in medium-voltage networks. By analyzing historical fault data and features like cable age and insulation type, the company can successfully identify high-risk feeders for replacement or maintenance. This approach allows it to prioritize investments effectively across its extensive underground cable networks for resilience during extreme weather events like heat waves.

Similarly, a utility company in the US has implemented targeted undergrounding programs using advanced data analytics. These initiatives strategically identify outage-prone overhead lines and convert them into underground systems, resulting in reduced outage durations for residential and commercial customers during hurricanes and other extreme weather events.

Real-world applications of AI and ML in cable maintenance 

AI and ML take predictive analytics a step further by automating the analysis of complex data sets and uncovering insights that would be impossible for humans to discern manually. These technologies analyze parameters like temperature fluctuations, vibration patterns, soil conditions and electrical loads to help predict when and where a cable might fail.

A utility company in Australia has a predictive maintenance program incorporating AI tools that simulate cable aging under various environmental conditions. These simulations allow the company to test different scenarios virtually before implementing solutions on the ground — a cost-effective approach that helps reduce trial-and-error efforts.

In the UK, a utility company has gone a step further by integrating predictive analytics into its broader digital grid strategy. The company combines AI insights with self-healing technologies that automatically reroute power during outages, reducing downtime for customers while maintaining service continuity across its network.

Grid reliability and customer satisfaction 

By helping to prevent outages, predictive analytics offers significant advantages for residential and commercial customers who rely on an uninterrupted power supply for their daily lives and operations. Moreover, proactive maintenance greatly reduces the need for emergency repairs that often result in prolonged service interruptions. Scheduled maintenance activities are less disruptive and more predictable, allowing customers to plan accordingly. This shift aligns with growing consumer expectations for consistent service quality in an increasingly digital world.

By optimizing maintenance schedules and reducing unplanned outages, utilities can lower their operational expenses. These savings can then be passed on to consumers through stable or even reduced electricity tariffs over time.

Enhancing sustainability efforts 

Predictive analytics and AI-driven cable management could contribute significantly to sustainability goals — a priority for utility companies worldwide as they navigate the global energy transition. Preventing underground cable outages reduces reliance on backup generators that often run on fossil fuels like diesel or natural gas during emergencies. By greatly reducing the use of these carbon-intensive solutions, utility companies can cut greenhouse gas emissions associated with outage management.

In addition, predictive maintenance reduces the need for emergency repair operations involving heavy machinery and transportation fleets, which are also sources of emissions. Over time, these reductions can help lower the organization’s overall carbon footprint.

Importantly, reliable grids are essential for integrating renewable energy sources like solar and wind into the energy mix. Intermittent renewables require stable distribution networks capable of handling variable loads without frequent disruptions. By driving grid reliability through predictive analytics and AI, utility companies can accelerate their adoption of clean energy technologies — a critical step toward achieving net-zero emissions targets.



Reliable grids play a key role in integrating renewable energy sources into the energy mix and helping to meet net-zero emissions targets.



Supporting energy transition in developing countries 

For developing countries in Southeast Asia and beyond, the stakes are particularly high. Many nations face challenges like aging infrastructure, limited resources for large-scale upgrades and rapidly growing energy demands driven by urbanization and industrialization. Predictive analytics offers an opportunity to leapfrog traditional power distribution approaches by enabling smarter resource allocation without extensive physical overhauls.

In Malaysia for example, utility companies have begun implementing predictive tools to help optimize grid performance during peak demand periods. By leveraging predictive analytics and AI for proactive cable repairs and reducing unplanned outages, one aims to enhance its underground cable maintenance strategies. This approach aligns with Malaysia’s broader goals of modernizing its infrastructure while maintaining a reliable energy supply for its growing economy and empowering customers.

 

Similar opportunities abound in the rest of Southeast Asia. By adopting AI-driven cable management early in their development journey, developing countries in the region can modernize their grids while avoiding inefficiencies associated with legacy systems.

Starting on the implementation roadmap 

When implementing predictive analytics, broader considerations are necessary for alignment with company goals and customer needs and measuring the impact of such initiatives.

 

Utility companies looking to adopt AI-driven grid solutions can consider the following steps.

 

1. Assess maintenance processes and data maturity level 

Evaluate the organization’s current maintenance processes and data maturity level with frameworks like the EY Power & Utilities Maturity Model and EY.ai Maturity Model. These frameworks help identify gaps and areas for improvement in moving toward predictive or prescriptive maintenance.

 

2. Align with strategic vision and customer expectations 

Predictive maintenance goals must align with overall business objectives and customer satisfaction metrics. There needs to be a robust business case aligned to the company’s strategic vision with clear objectives, such as reducing maintenance costs, improving asset reliability or enhancing operational efficiency.

 

3. Invest in data collection 

The foundation of any predictive model is high-quality data. Utility companies must keep track of their historical data records and strategically deploy IoT sensors to monitor parameters like temperature changes, vibration levels, soil moisture content and electrical load variations.

 

4. Develop custom models 

ML algorithms must be tailored to local conditions to help achieve accurate predictions.

 

5. Adjust maintenance processes and regimes

Based on predictive insights, refine maintenance schedules to help optimize resource allocation and greatly reduce downtime. This involves moving from time-based maintenance to condition-based maintenance so that repairs are performed only when necessary.

 

6. Integrate systems seamlessly 

Predictive tools should be integrated into existing grid and asset management platforms for monitoring and decision-making.

 

7. Start small, scale up quickly 

Pilot programs allow utility companies to validate their models on a smaller scale before scaling up quickly across larger networks.

 

8. Track value generation 

Establish KPIs like mean time between failures, mean time to repair, asset uptime and maintenance costs to measure the effectiveness of predictive maintenance initiatives. Regularly monitor the actual metrics and monetary value and compare them against the targets set in the business case to adjust strategies and drive continuous improvements.

 

9. Train personnel 

Companies must invest in training programs to equip their workforce with the skills needed to interpret AI-generated insights effectively. 

 

10. Collaborate regionally 

Neighboring countries that share best practices with one another can help accelerate adoption and reduce costs through shared learning.

Underground cables contribute significantly to annual maintenance expenditure and account for a significant number of network incidents across assets operated by power distribution networks. Predictive analytics is a vital tool for utility companies to directly address these challenges in navigating rising demands for reliability, sustainability and efficiency. The technology is no longer confined to research labs or pilot projects; it is deployed today by forward-thinking utility companies around the world. 

By helping to prevent underground cable outages through advanced AI-driven insights, utility companies across Southeast Asia could deliver tangible benefits for themselves and their customers. For instance, a resulting drop in downtime helps enhance consumer satisfaction, optimize operations, lower costs, reduce reliance on fossil fuels, support climate goals and drive reliable grids that enable integration of renewable energy into them. Training ML models with more scenario-specific data on electrical failure and third-party damage could contribute to reduced maintenance expenditure for underground cables.

For developing countries eager to modernize their infrastructure without incurring prohibitive costs or delays, predictive analytics also offers a pathway to leapfrog traditional methods and help achieve faster sustainable growth.

The future of power distribution lies within data systems and maintenance regimes, and those who embrace this shift could lead the way toward a brighter energy future.

This article was authored with contributions from Pershanta Kumar, Senior Manager, Business Consulting — Power & Utilities, Ernst & Young Consulting Sdn. Bhd.; Professional Engineer, Board of Engineers Malaysia; and Technologist, Malaysia Board of Technologists.


Summary

Utility companies around the world are leveraging predictive analytics, AI and ML to help anticipate failures before they occur, optimize maintenance schedules and enhance grid resilience. 

Developing countries in Southeast Asia that quickly adopt an AI-driven cable management approach can modernize their grids while avoiding inefficiencies associated with legacy systems.

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