Skip to content

Industry Use Cases

Solar Energy

  • Irradiance sensors: Detect shading, soiling, or sensor drift
  • Inverter power output: Identify curtailment, derating, or inverter faults
  • String-level monitoring: Compare current/voltage across parallel strings

Wind Energy

  • Wind speed/direction: Detect icing on anemometers
  • Power curve validation: Compare actual vs. expected power output
  • Vibration monitoring: Flag abnormal turbine vibration patterns

Battery Storage

  • State of charge (SOC): Detect drift or recalibration events
  • Temperature monitoring: Flag thermal runaway precursors
  • Cycle counting: Validate charge/discharge cycles

Manufacturing

  • Process sensors: Detect stuck sensors in continuous processes
  • Quality control: Monitor production line measurements for drift
  • Predictive maintenance: Flag abnormal sensor behavior before failures

Environmental Monitoring

  • Weather stations: Validate temperature, humidity, pressure readings
  • Air quality: Detect sensor degradation over time
  • Water quality: Flag out-of-range pH, turbidity, or conductivity

Utilities

  • Substation monitoring: Validate voltage, current, frequency measurements
  • Meter data: Detect anomalous consumption patterns
  • Transformer health: Flag abnormal temperature or load patterns

Oil & Gas

  • Pipeline monitoring: Detect pressure anomalies and flow irregularities
  • Wellhead sensors: Validate temperature, pressure, and flow rate measurements
  • Tank level monitoring: Flag abnormal fill/draw patterns

Getting Started

Regardless of industry, getting started with timeseries-qc follows the same pattern:

import pandas as pd
import tsqc

df = pd.read_csv("sensor_data.csv")
result = tsqc.check(df, assume_tz="UTC")
result.plot().show()

See the Quickstart guide for a complete example.

Next Steps