Machine-Learning-Modelle lernen kontinuierlich Ihre Energiemuster und warnen Sie in Echtzeit vor Unregelmäßigkeiten – von Geräteausfällen bis zu Abrechnungsfehlern.
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 →Let machine learning protect your energy infrastructure and catch costly anomalies before they impact operations.
Try Live Dashboard →