Solar panel system

“AI + Energy” Ushers PV O&M into a Systems-Level Era: Multifit Meets the New Intelligent Maintenance Paradigm with “Waterless Cleaning + Cloud Collaboration” Matrix

If the past decade of photovoltaics was defined by competition over “module efficiency,” then the watershed moment of 2026 lies in “O&M intelligence.” A policy-level development is reshaping the logic of solar panel maintenance: In July 2026, four central authorities—the National Development and Reform Commission, the National Energy Administration, the Ministry of Industry and Information Technology, and the National Data Administration—jointly issued the Action Plan for Promoting Mutual Empowerment of Artificial Intelligence and Energy. The plan explicitly accelerates R&D and deployment of robots, drones, and intelligent sensing systems to advance smart O&M at remote renewable energy stations. “AI + Energy” has thus moved from conceptual exploration into large-scale, scenario-driven deployment.
A Clear Signal: O&M Is Shifting from "Personnel Dispatched Onsite" to "Machines Executing Onsite Tasks"

For years, renewable energy O&M has relied heavily on manual labor—technicians trekking across rugged terrain, climbing arrays, visually inspecting modules, then dispatching crews for onsite repairs. The process is inefficient, carries significant fall risks, and lacks standardization. The new direction is unequivocal: fixed-schedule manual washing and reactive repairs are being replaced by "drone inspection + AI recognition + robotic cleaning + platform closed-loop."

An industry paradigm is already taking shape: drones autonomously capture visible-light and infrared imagery; AI identifies soiling, hot spots, and micro-cracks to generate contamination distribution maps; the platform dispatches cleaning tasks "on demand"; robots execute panel washing; and pre- and post-cleaning data comparisons verify results—forming a full-chain closed loop of "inspect → identify → dispatch → clean → re-inspect."​ This shifts the old model of "scheduled full-array cleaning" to "clean only where needed," drastically reducing wasted effort.

For solar panel cleaning, the implication is direct: a cleaning robot is no longer just a "dust-sweeping brush"—it is an execution terminal within an intelligent O&M system. It must be locatable, self-correcting, cloud-connected, traceable, and collaborative.​ This is precisely the next stage that policy and real-world scenarios jointly point toward.

Multifit: 17-Year Source Manufacturer Embeds "Cleaning Hardware" into the "Intelligent O&M Closed Loop"

Amid this paradigm shift, Beijing Multifit electrical Technology Co., Ltd —a National High-Tech Enterprise established in 2009, recognized as a Guangdong Provincial "SRDI" (Specialized, Refined, Unique, Innovative) SME and a Shantou "Gazelle" enterprise—holds 60+ patents​ and CE/FCC/ISO certifications, with products deployed in 100+ countries​ across Europe, the Americas, the Middle East, and Asia-Pacific. Multifit is meeting the systems-level requirements of "AI + Energy" with its "source manufacturer + full-scenario robotics" approach:

 
MR-G3 Hanging-Type Waterless Robot: Self-powered, dry-brush technology, dust removal rate ≥99%, cleans GW-scale arrays daily. It executes the high-frequency "unattended dry cleaning" action required by AI on-demand O&M in arid regions, perfectly matching the "no-pressure-washer" policies in the Middle East and Australia.
 
 
 
MR-T1 AI Path-Following Robot: Cloud-based swarm scheduling, 50mm obstacle-crossing, automatic posture correction. It can directly receive "inspection dispatch orders" from an O&M platform, turning individual units into a centrally managed fleet.
 
Cloud Data Closed Loop: Multifit's equipment cloud platform records "traceable cleaning logs and auditable generation data per megawatt," directly aligning with the policy's mandate for "automated fault diagnosis and O&M data retention." AI determines soiling levels pre-cleaning; post-cleaning transmittance is verified—producing auditable O&M records for plant owners.

The transition from "selling a robot" to "becoming a node in an intelligent O&M closed loop" mirrors the policy direction exactly: future PV cleaning will no longer be judged by brush width or travel speed, but by "whether it can be AI-dispatched, whether it can collaborate with drone/inspection data, and whether it can prove—with data—that generation has recovered."


Post time: Aug-26-2026

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