Power generation facilities – including coal-fired, biomass, and waste-to-energy plants – are undergoing a digital transformation with AI and smart technologies at the core. These conventional plants remain critical to energy supply for years to come, and leveraging AI-driven solutions is key to improving efficiency, flexibility, and emissions performance. AI is revolutionizing power plant operations by helping operators optimize processes, reduce emissions, and prevent costly failures. By analyzing vast real-time data streams, AI models can detect anomalies, fine-tune fuel combustion, and enhance overall performance. Industry estimates indicate that AI-driven analytics can cut maintenance costs by up to 30% and boost equipment availability by around 20%, greatly improving plant economics and reliability m.sohu.com. In this report, we explore how AI and intelligent devices are being applied in core power plant equipment, the development of smart sensors and predictive maintenance systems, the landscape of key vendors and their latest products, forecasts for the next five years of technological trends and their impact on efficiency, as well as procurement recommendations and potential risks.

Generators and rotating machinery are also benefitting from AI-driven monitoring. A network of smart sensors (measuring vibration, temperature, pressure, etc.) streams health data from turbines and generators. AI anomaly detection models continuously analyze these signals to catch subtle deviations that human operators might miss, such as an incipient bearing vibration issue or slight overheating of a generator winding. Early warning of such anomalies enables predictive maintenance actions that prevent unplanned outages. In fact, AI-based condition monitoring has been shown to reduce equipment downtime by an estimated 35–45% in some deployments. Meanwhile, in flue gas clean-up systems (FGD and SCR units), AI optimizes reagent usage and emission control. Because flue gas treatment involves complex time lags and coupling – for example, sulfur content in coal can vary and SO₂ removal has a delayed response – conventional control often cannot precisely meter limestone or ammonia injection. AI solutions address this by combining physical process models with real-time sensor data. The AI predicts SO₂ production based on boiler conditions and dynamically allocates sorbent feed rates both in-furnace and in downstream reactors. These adjustments occur on the order of seconds, far faster than human operators, continually driving the system toward optimal emissions removal. At one power plant, an AI-guided desulfurization control system achieved over 96% autonomous operation of the scrubbers, keeping SO₂ levels within 20 ± 10 mg/Nm³ (well below regulatory limits) while cutting sorbent consumption by ~8%, saving about 40,000 tons of limestone (approximately ¥2 million) per unit annually. This demonstrates how AI can meet strict environmental targets with improved cost-effectiveness and minimal manual intervention.

Emerging trends: As computational power grows and industrial control systems evolve, AI is moving from an advisory role to closed-loop control in plant operations. Cutting-edge facilities have begun deploying AI algorithms directly into their distributed control system (DCS) loops, enabling millisecond-level data feedback and autonomous control actions. This development means that boiler combustion, turbine governing, and emission control can be fine-tuned continuously with minimal human input to stay at optimal settings. Additionally, the rise of digital twin technology allows high-fidelity virtual models of boilers, turbines, and other equipment to be used for AI training, scenario simulation, and real-time calibration of control strategies. These twins serve as a sandbox for AI to learn and adapt without risking actual equipment. In the coming years, we may see the advent of AI “autopilots” for power plants – systems that leverage knowledge graphs to map out millions of interconnected components and signals in a plant, and then train multiple AI agents to collaboratively control different subsystems. Industry visionaries, such as those at Siemens Energy, view this approach as key to realizing fully autonomous power stations. While a completely self-driving power plant is still on the horizon, the current trajectory shows AI steadily taking on more decision-making in operations. The achievements to date – from efficiency gains and emissions cuts to reduced downtime – underscore AI’s central role in shaping a smarter, more responsive mode of plant operation.

The rapid proliferation of IoT and smart devices in power plants has unlocked unprecedented capabilities for real-time monitoring and diagnostics. Today’s plants are instrumented with thousands of sensors measuring everything from boiler flame intensity, steam temperatures and turbine vibrations to generator winding temperatures, feedwater chemistry, coal conveyor speeds, and emissions composition. These sensors feed a continuous stream of data through industrial networks or wireless (even private 5G) networks into both edge and cloud analytics systems. Edge computing devices – essentially on-site industrial computers loaded with AI models – play a crucial role by processing sensor data near the source. They can run inference locally to detect anomalies within milliseconds and even execute control actions without waiting for cloud commands. For example, if a vibration sensor on a turbine bearing shows an abnormal frequency spike, an edge AI system can instantly flag it and initiate protective actions (like adjusting load or alerting maintenance) to prevent damage. By analyzing data directly at the equipment site, such edge AI reduces latency and ensures critical responses even if connectivity to the cloud is slow or intermittent. At the same time, aggregated data from across the plant is sent to central platforms or the cloud where more computationally intensive AI models can perform deeper analysis and optimization.

Powered by ubiquitous sensing, predictive maintenance (PdM) has emerged as a new paradigm for power plant asset management. Traditional maintenance strategies relied on fixed schedules or simple alarms and often led to either excessive maintenance or unexpected breakdowns. In contrast, AI-driven predictive maintenance employs historical and real-time data to train models for anomaly detection and failure prediction, enabling a shift to condition-based maintenance. These AI models (e.g. neural networks, isolation forest, etc.) analyze multivariate sensor inputs together, learning the normal patterns of equipment behavior. When subtle deviations from normal arise – even those imperceptible to humans – the AI flags an incipient issue and can often diagnose the likely cause. This allows maintenance teams to fix issues proactively before a failure occurs, dramatically reducing unplanned outages and secondary damage. For instance, an LSTM-based multivariate model monitoring a turbine might track the tight correlation between temperatures, pressures, and vibrations under healthy conditions; if that correlation degrades, the model outputs an aggregated anomaly signal indicating potential trouble. Similarly, probabilistic failure models can predict the remaining useful life of components (like generator insulation) and schedule their replacement just in time. To tackle the challenge of limited failure examples in training data, techniques like transfer learning and federated learning are used to leverage knowledge from similar equipment across multiple plants, improving the AI’s robustness. In practice, implementing AI-driven PdM has delivered impressive results: studies show maintenance costs can be reduced by ~30% and equipment availability increased by up to 20%. One large US utility, for example, deployed over 400 AI models across 67 coal and gas units, achieving heat rate improvements of 1–3% and significantly cutting forced outages. This translated into roughly $60 million in annual savings and 1.6 million tons CO₂ reduction– equivalent to removing 300,000 cars’ emissions – underscoring the transformative impact of AI on plant reliability and performance.

In addition to safeguarding major equipment, a new generation of intelligent inspection devices – such as robots and drones – is enhancing plant maintenance practices. Ground-based inspection robots are now used in some facilities, equipped with infrared cameras, optical zoom lenses, and gas leak detectors. Guided by AI algorithms, these robots autonomously patrol around boilers, pipelines, valve racks, and electrical rooms to perform routine checks. They can read analog gauge dials via computer vision, listen for acoustic anomalies, sniff for gas leaks, and report irregularities to operators in real time. Drones, on the other hand, are deployed to inspect tall structures like stacks and cooling towers, or even to hover inside boiler furnaces for a close look at tube conditions. Using high-resolution imagery and AI-based image analysis, drones can detect cracks, hotspots, or ash buildup in places that are hard or dangerous for humans to reach. The data from these smart inspection devices, when integrated into a plant’s digital twin or asset management platform, provides maintenance teams with an interactive 3D view of equipment health. Engineers can virtually inspect and diagnose issues from the control room, reducing the need for hazardous on-site manual inspections. This fusion of sensors, edge AI, and intelligent inspection gadgets effectively forms a nervous system for the power plant, moving it closer to a “minimal manpower” maintenance model. The result is not only improved safety – by keeping staff out of high-risk areas – but also higher inspection frequency and consistency, which further improves reliability.

Across the globe, leading industry players and tech companies are actively developing smart power plant solutions and cutting-edge products. General Electric (GE) was an early mover, launching its “Digital Power Plant for Steam” suite in 2016 atop the Predix industrial IoT platform. This suite included applications for boiler optimization (using AI to improve combustion tuning and sootblowing schedules), coal analyzer (feeding real-time coal quality data like moisture into the combustion control), and plant optimization (tracking key KPIs to close the gap between actual and ideal performance). GE claimed that its digital solutions could boost plant efficiency by about 1.5% and cut CO₂ emissions by 3%, translating to roughly 67,000 tons of coal saved per gigawatt-hour over time. GE also offers Asset Performance Management (APM) software that leverages AI for equipment health monitoring and life prediction – credited with successes like restoring the idle Chivasso power plant in Italy through predictive insights. Siemens Energy, on the other hand, is charting a vision of autonomous power plants, highlighting the use of knowledge graphs and digital twins to enable AI controllers for complex power station management. Siemens’ “Power 4.0” digital initiative integrates 3D visualization with advanced analytics to improve plant reliability, availability, and efficiency. Part of Siemens’ offering is setting up remote monitoring and diagnostic centers that employ AI algorithms to evaluate unit performance, detect nascent issues, and optimize fuel and maintenance scheduling via big data – delivering cost savings and efficiency gains for clients. Moreover, Siemens provides its open IoT operating system MindSphere and related AI platforms to ingest the vast data from plants and deploy custom AI applications. It also recently introduced AI-powered assistants and digital twin solutions (in partnership with platforms like NVIDIA Omniverse) for real-time operational guidance and training simulations. Notably, Mitsubishi Power (formerly MHPS) has developed the “TOMONI” digital platform, offering a suite of AI solutions for real-time monitoring, diagnostics, and optimization. Mitsubishi’s tools such as the Component Condition Monitoring Tool (CCMT) combine decades of failure data on boiler pressure parts with live plant data to implement risk-based maintenance programsm. The company reports that such AI-driven preventive maintenance has dramatically reduced pressure-part failure rates across dozens of boilers over two decades.

Beyond the OEMs, industrial automation and software companies have also introduced solutions for smart power plant operations. ABB’s Ability™ platform, for example, includes predictive maintenance tools for motors and generators that use AI algorithms to analyze real-time operating data, identify anomalies, and forecast failures. ABB’s recently launched Genix APM suite even features an “APM Copilot” AI assistant to help optimize asset performance and energy efficiency. Schneider Electric and Honeywell have similarly integrated AI into their control systems; Honeywell’s Experion and Forge platforms enable AI-driven health monitoring of critical plant equipment. Emerson has augmented its Ovation control system with machine learning models to finetune boiler-turbine coordination and thermal performance. Numerous startups are entering this niche as well – firms like SparkCognition, Uptake, and others offer specialized AI analytics services to help power plants reduce fuel consumption and predict failures. In China, power equipment manufacturers and ICT companies are jointly driving the development of smart power plants. Traditional equipment giants such as Shanghai Electric not only supply turbines and boilers but have also established remote monitoring and diagnostics centers, developing their own digital plant systems. Shanghai Electric, for instance, built a full lifecycle data model and 3D design delivery system for its gas turbines, and in 2017 launched a remote monitoring & simulation center to leverage big data for control logic improvements – laying a solid foundation for “intelligent plant” capabilities. Other major state-owned firms (Harbin Electric, Dongfang Electric, etc.) are likewise working on proprietary smart control systems. On the tech side, Huawei offers an Intelligent Power Generation solution that emphasizes a digital infrastructure of “one network, one AI center, one platform”. Huawei’s solution leverages 5G connectivity, cloud computing, and AI models to provide smart safety, operations, and maintenance for both conventional and renewable power stations – aiming to create an unmanned, efficient, low-carbon power plant ecosystem. In collaboration with industry partners, Huawei has developed six major intelligent applications (covering smart construction, security, operations, maintenance, etc.) with the goal of enabling unattended operation and automated inspection in plants. Baidu, as noted earlier, has applied its cloud AI platform to real-world power plant optimization projects (e.g. smart cooling and FGD control), and is packaging these as “AI + Power” solutions for the sector. Alibaba Cloud and Tencent Cloud have also launched energy AI platforms that combine IoT data, big data analytics, and machine learning for power generation optimization and management. Overall, the “digital + intelligent” trend is a focal point for both global and Chinese vendors. New products are emerging rapidly – for example, at MWC 2024 Huawei unveiled an Intelligent Distribution Solution (IDS) for power systems– illustrating the accelerating wave of innovation in energy sector AI.

In the next five years, we expect AI and smart equipment adoption in power plants to deepen and fundamentally reshape operations. Autonomy in plant control will increase markedly. With more robust knowledge models and federated learning sharing insights across fleets, multiple AI controllers will coordinate different subsystems, moving plants toward minimal staffing and even “dark” (lights-out) operation. New plants (and retrofits) will likely feature “AI operators” handling routine adjustments, with human staff transitioning to supervisory and strategic decision-making roles. Secondly, digital twins and simulation will become standard tools. Virtually every critical asset will have a digital twin used for both real-time optimization and training/emergency drills. By simulating myriad scenarios (load swings, equipment failures, extreme transients), AI agents can learn optimal responses in advance, thus improving the plant’s resilience to grid fluctuations and unexpected events. Third, the ubiquity of edge computing and 5G will make data acquisition and control actions even more instantaneous and reliable. Edge AI devices will be everywhere on site, bringing reaction times down to milliseconds and giving the plant near-instant reflexes. This is especially crucial as grids demand more flexible operation; AI will enable thermal units to cycle on/off more rapidly and ramp loads faster to complement renewables, all while keeping equipment within safe stress limits. Fourth, predictive maintenance will be fully mainstream. With growing experience and aggregated data, plant operators will refine their equipment health databases, and AI failure prediction will become more accurate and further ahead of time. Not only major units but also auxiliary systems (feed pumps, fans, etc.) will be covered by PdM programs, thereby drastically cutting unplanned outage rates and boosting overall availability. By around 2025–2030, it’s plausible that many plants will halve their forced outage rates – going from a handful of incidents per year to near-zero in some cases. Fifth, advances in AI technology itself (more powerful deep learning models, AutoML for easier model building, hybrid AI with expert rules, etc.) will unlock new applications. For example, large language models (natural language AI) could be harnessed for plant knowledge management and intelligent decision support; operators might query a conversational AI assistant to troubleshoot complex scenarios or get optimization tips, thus speeding up knowledge transfer and decision-making across the organization.

Collectively, these trends will yield substantial improvements in operational efficiency and economics. Thermal efficiency of units is expected to increase a further 1–3 percentage points beyond current optimized levels, thanks to more precise AI control (for instance, minimizing excess air, optimizing startup/shutdown sequences to cut losses, etc.). Fewer unplanned outages translate to higher equivalent availability, lower maintenance expenses, and consequently greater energy output and revenue. AI optimizations will also drive down emissions intensity – pollutants emitted per kWh generated could drop another 10–20%, easing compliance with tightening environmental regulations. With AI enabling faster and more flexible dispatch, plants can participate more effectively in grid load-balancing and ancillary service markets, unlocking new revenue streams (e.g. compensation for rapid ramping or frequency support). On the safety front, AI’s ability to foresee issues and take automatic corrective action will reduce the risk of accidents and protect personnel, elevating the inherent safety of operations. In summary, the coming years will likely witness a qualitative leap in the performance of power stations empowered by AI – across efficiency, reliability, environmental and safety metrics – reinvigorating the conventional power generation sector with new vitality.

In light of the AI and smart equipment boom, power plant engineers and procurement teams must carefully balance benefits and risks when planning purchases. Key recommendations include:

a. Define Needs and Feasibility: Before procuring any AI solution, identify the primary pain points of your plant. Is the goal to improve boiler heat rate and reduce startup fuel, or to minimize unplanned outages and maintenance costs? Different problems call for different AI modules. Assess the technical feasibility of proposed solutions at your site – understand their requirements for instrumentation, IT infrastructure, and compatibility with your existing systems.

b. Start with Pilot Projects: It is wise to begin with a small-scale pilot on a critical piece of equipment or a single unit to validate the solution’s effectiveness and ROI. For example, deploy an AI combustion optimizer and predictive maintenance system on one boiler-turbine unit and measure performance improvements (fuel savings, reduction in alarms or downtime) over a trial period. Use these learnings to refine the approach before scaling up plant-wide. This phased adoption mitigates upfront investment and implementation risks.Choose Reputable Vendors and Open Architectures: Opt for solution providers with proven industry experience and reference projects in similar power plants. Scrutinize whether their AI models have been validated on real plant data and how accurate and stable they are. Whenever possible, request on-site demonstrations or speak with reference clients to gauge results. Technically, favor systems built on open interfaces and standard protocols to avoid vendor lock-in. The solution should integrate smoothly with your existing DCS, SIS, and data historians to ensure interoperability and data flow. Avoid proprietary black boxes that might impede flexibility or future upgrades.

c. Invest in Training and Change Management: Introducing AI will require new skill sets. Plan to train or hire personnel who understand both power engineering and data science.

d. Provide comprehensive training for operators and maintenance staff on the new tools so they understand how to interpret AI outputs and respond appropriately. There may be initial skepticism – management should promote a culture that embraces data-driven decision-making, while still maintaining human oversight for critical decisions. Encourage your team to treat AI as a decision-support partner rather than a “black magic” box.Consider Total Cost and ROI: Smart upgrades often involve substantial upfront costs, including sensors, networking, servers, software licenses, and integration services.

e. Take a holistic view of total lifecycle cost in procurement decisions, factoring in ongoing subscription fees, software updates, and support contracts. At the same time, quantify the expected benefits – fuel cost reduction, avoided outage costs, manpower savings, emissions compliance benefits, etc. – to estimate the payback period. Ensure that the investment aligns with your plant’s financial and operational goals, and prioritize use-cases with the strongest business case.Address Cybersecurity and Data Privacy: Connecting more devices and granting AI influence over control loops inevitably expands the cyber-attack surface. Verify that any system you purchase adheres to strict cybersecurity standards – encrypted communications, role-based access control, continuous vulnerability patching, and intrusion detection features are a must.

f. It’s advisable to include cybersecurity requirements from the design phase and as part of vendor evaluation. Additionally, clarify data ownership and privacy policies: your plant’s operational data is highly sensitive, so ensure it won’t be exposed or misused. If using cloud-based AI, consider where data is stored and how it’s protected.Mitigate Operational Risks: When deploying AI in closed-loop control, implement robust fallback and redundancy mechanisms to maintain safety. For example, ensure there is a manual override or fail-safe that can take over if the AI outputs aberrant commands or if sensors feed incorrect data.

g. Run extensive testing in simulation before enabling any autonomous control, and perhaps start with advisory mode (AI gives recommendations) before moving to automatic mode. In procurement contracts, define clear performance KPIs and service level agreements – for instance, expected efficiency gains or reliability improvements – and delineate responsibilities. This holds vendors accountable to support and fine-tune the system if outcomes are below expectations.

By following these guidelines, plant teams can embrace AI and smart devices while keeping risks under control and maximizing value. Building the smart power plant of the future is a system-wide endeavor – it requires forward-looking planning, prudent execution, and continuous refinement. With careful selection of solutions and partners, strong focus on workforce readiness, and an unwavering priority on safety and reliability, AI can truly empower power plant operations without falling into its potential pitfalls. A balanced approach of caution and agility during planning and procurement will allow plants to navigate the intelligent transformation smoothly, positioning them to thrive in the evolving energy landscape. Author: HK Evergreen Energy Ltd, All Rights Reserved. 30th Jun, 2025