Strategic Intelligence: Predictive Maintenance in Power (2026)

Strategic Intelligence: Predictive Maintenance in Power (2026)

Summary

Predictive maintenance (PdM) is becoming a central driver of operational optimization since it uses the Internet of Things (IoT) sensor data to detect equipment issues early and schedule repairs before failures occur.

With expansion of wind and solar capacity, predictive maintenance is becoming increasingly critical by helping to sustain availability, reduce curtailment, and enable smoother renewable integration.

Artificial intelligence (AI) and machine learning (ML) are powering predictive maintenance continuously through sensor data to identify anomalies and anticipate equipment failures before they occur.

Scope

• The report focuses on predictive maintenance in the power sector as the theme, including key market drivers, technology enablers, and adoption trends.
• It encompasses a thematic overview of predictive maintenance within the power sector, articulating its strategic relevance, principal adoption drivers, and evolving role in modern asset-management paradigms across generation, storage, and utility operations.
• It provides industry insights on how predictive maintenance supports a proactive maintenance strategy, improves asset reliability, and enables more efficient power generation.
• It examines the role of artificial intelligence (AI) in predictive maintenance, covering how AI/ML techniques enhance condition monitoring, fault prediction, and maintenance decision-making.
• The report highlights the growing application of predictive maintenance in wind and solar PV, including common failure modes, monitoring approaches, and representative use cases across generation assets.
• It discusses predictive maintenance adoption and use cases within power utilities.
• The report delivers an overview of how predictive maintenance can optimize energy storage performance, safety, uptime, lifecycle management, and cost of ownership (e.g., battery health forecasting and thermal risk detection).
• It provides a competitive overview of the positioning of power utility companies adopting predictive maintenance.

Reasons to Buy

• Gain a comprehensive assessment of the accelerating market momentum for predictive maintenance in the power industry, including key growth catalysts, adoption patterns, and emerging opportunity areas.
• Identify the leading players shaping the predictive maintenance landscape and understand where they sit across the value chain.
• Understand how power-sector dynamics, technology evolution, and macroeconomic forces (e.g., cost pressures, reliability mandates, energy-transition investment) are redefining the role and urgency of predictive maintenance.
• Access a clear technology briefing on maintenance paradigms such as reactive, preventive, condition-based, and predictive, enabling informed benchmarking of operational maturity and roadmap planning.
• Leverage a detailed mapping and analysis of the predictive maintenance value chain.
• Review in-depth company profiles of leading power utilities deploying predictive maintenance.
• Benchmark the ecosystem through an overview of predictive maintenance service and solution providers, supporting vendor shortlisting.
• Explore how AI and machine learning are augmenting predictive maintenance and assess their transformative impact on solar PV and wind, including improved fault detection, performance forecasting, and maintenance optimization.
• Use a power-sector scorecard snapshot to anticipate and compare the relative positioning of leading power utilities in the predictive maintenance theme, aiding competitive intelligence and investment prioritization.