Predictive Maintenance · Real-time Analytics
Matrix Renewables needed to onboard and monitor a rapidly growing portfolio of solar assets without the delays, data gaps, and scaling limits caused by decentralized monitoring across multiple SCADA systems.
Reported impact
Matrix Renewables reduced reporting effort and downtime, improved production efficiency, increased transparency across operational and financial KPIs, and established a scalable monitoring platform for continued renewable-asset growth.
Matrix Renewables standardized asset monitoring on IBM Maximo Renewables to create a centralized operating layer across its global solar portfolio. The platform ingests operational data from major SCADA providers such as GPM, AlsoEnergy, and Inaccess, plus seven tracker OEMs, using provider APIs rather than direct SCADA integrations. IBM optimized those API-based connections around query-frequency limits, concurrent-call restrictions, and timeout behavior so the company could maintain data quality and availability at scale. Within Maximo Renewables, AI and machine-learning analytics detect underperformance, uncover hidden inefficiencies, and support decisions that recover energy losses and improve ROI. The resulting data warehouse then feeds in-house BI reporting, real-time dashboards, alerts, and onboarding checks, giving asset managers a unified, scalable monitoring and reporting environment as Matrix continues international expansion.
Operational data moves from SCADA provider APIs and tracker OEM systems into IBM Maximo Renewables, where analytics identify underperformance and energy-loss issues; curated portfolio data is then exposed through the Maximo-powered data warehouse to BI tools, dashboards, reports, and alerting workflows.
10% reduction in downtime
75% reduction in reporting time
IBM’s advanced analytics capability, which combines AI and machine-learning models to detect underperformance, reveal hidden inefficiencies, and support data-driven decisions
This solution unifies data from major SCADA providers—including GPM, AlsoEnergy and Inaccess—as well as seven tracker original equipment manufacturers (OEMs), which resulted in near real-time visibility across the portfolio.
the team worked through SCADA-provider APIs, but this introduced new challenges such as query-frequency limits, concurrent-call restrictions, and time-out scenarios.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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