Electricity and Control April 2025

Industry 4.0 + IIoT

With the widely increasing reliance on data, AI can assist utilities to manage their networks more efficiently.

recruitment to retirement, which drive operational and cost eiciency. By leveraging a cloud-native AI platform built on Kubernetes architecture, utilities can integrate and analyse data from diverse sources, enabling a unified approach to addressing strategic and operational challenges. Such a holistic platform empowers utilities to embrace a new era of smart, predictive, and eicient solutions, driving transformative outcomes across their organisations. The integration of AI in the utilities sector marks a shiœ towards smarter, more predictive, and eicient operations. As the industry evolves, driven by advances in technology and changing market dynamics, leveraging AI will become essential in navigating the complexities of modern energy and water management. The transformative potential of AI enhances core functions such as resource management, predictive maintenance, and revenue protection, and supports non-core activities like procurement integrity and HR analytics. By adopting a comprehensive AI transformation roadmap, utilities can align their AI initiatives with strategic and operational goals, supporting a unified approach that maximises value across all areas. With these innovations utilities can adapt to economic fluctuations, climate change, and evolving customer expectations, in turn developing a more resilient and sustainable energy infrastructure. With thoughtful implementation and strategic planning, the utilities sector can unlock new levels of eiciency and eectiveness, driving progress in the transition to a smarter, more connected future.

with the aid of AI and drones. Advanced machine learning techniques can be deployed for non-intrusive load disaggregation (NILD) based energy management and/or demand response management using granular meter or data logger data. Non-core initiatives, while not directly tied to primary operations, are important for overall organisational eiciency and support. These initiatives include continuous monitoring for spare parts contracting and procurement, and advanced human resources analytics, which help streamline processes and improve cost eiciency. Although they may not impact the core functions of energy and utilities directly, non-core AI applications enhance broader organisational capabilities and support the comprehensive integration of AI. A well-rounded AI strategy would include both core and non-core elements in the AI transformation roadmap, maximising AI’s value across all areas of the organisation. The power of a data-to-decisioning AI platform Having a data-to-decisioning platform that runs on modern and scalable architecture is key to maximising the strategic capabilities of AI, resulting in the realisation of substantial value across core and non-core areas of utility operations. In core areas, a robust platform can provide AI solutions that deliver accurate large-scale forecasting automation, real-time process or energy optimisation, and intelligent decisioning workflows for department strategies built from AI models. Additionally, it can provide insights from unstructured data, and enable optimised decisions based on scenario planning. In non-core areas, a robust AI platform supports continuous monitoring for processes such as procurement to payment and

Reference [1] FDS Future Data Stats

https://www.futuredatastats.com/artificial-intelligence-in-energy-and-utilities-mar ket?srsltid=AfmBOop8ZE1pA6ePrkO_5oyln74YDRJDHx03womYmdIwRJR8DLknyH_z

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APRIL 2025 Electricity + Control

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