Machine learning models continuously learn your energy patterns and alert you to irregularities in real-time – from equipment failures to billing errors.
Start ML Monitoring →Unsere ML-Modelle lernen kontinuierlich aus Ihren Energiedaten und erkennen Abweichungen in Echtzeit. Von Gerätedefekten über ungewöhnliche Verbrauchsmuster bis zu Abrechnungsfehlern – Sie werden sofort informiert, bevor kleine Probleme zu teuren Ausfällen werden.
From consumption anomalies to power quality issues – AI that understands your energy fingerprint.
Detects unusual consumption patterns that deviate from learned baselines. Identifies phantom loads, equipment left running, HVAC malfunctions, and billing meter errors. Average detection time: 3-7 minutes.
See Live Detection →ML models trained on vibration, temperature, current draw, and acoustic signatures predict equipment failures 1-4 weeks before they occur. Integrates with maintenance systems for automated work orders.
Equipment AI →Detects voltage sags, swells, harmonics, flicker, and frequency deviations that fall outside IEC 61000 standards. Correlates power quality events with production issues and equipment damage.
Quality Monitoring →Compares actual meter readings vs. ML-predicted consumption to identify billing discrepancies. Has detected €50,000+ in meter errors and incorrect tariff applications for enterprise customers.
Validate Bills →Learns optimal HVAC performance curves and detects inefficiencies like dirty filters, refrigerant leaks, damper issues, or incorrect setpoints. Typical energy savings: 15-25% after optimization.
HVAC Insights →Detects unusual SCADA/IoT communication patterns that may indicate cyberattacks, unauthorized access, or malware. Monitors Modbus, OPC-UA, MQTT traffic for behavioral anomalies.
Security AI →A food manufacturer deployed our ML monitoring across 8 cold storage units. One Friday evening, the AI detected a 4% increase in compressor current draw – within normal operating range but outside the learned pattern. Investigation revealed a refrigerant leak. Early detection prevented product spoilage worth €180,000.
A commercial office building noticed their ML monitoring dashboard flagged a billing anomaly: actual consumption was 22% higher than predicted for 3 consecutive months. Investigation revealed the utility had applied the wrong tariff rate. Refund: €6,800. Corrected billing saved €24,000/year going forward.
An industrial plant replaced threshold-based alerts with our ML monitoring. The previous system generated 200+ alarms/month, mostly false positives. Our context-aware AI reduced alerts to 28/month (all actionable) while catching 3 critical anomalies the old system missed. Operations team satisfaction: 9.2/10.
Let machine learning protect your energy infrastructure and catch costly anomalies before they impact operations.
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