Smart Energy Anomaly Detection: Baseline & Logic¶

This document defines the technical approach for detecting unusual energy consumption patterns using Home Assistant, Shelly sensors, and local AI reasoning.

What problem it solves¶

Energy anomalies (appliances left on, failing hardware, or unexpected power spikes) can lead to high costs and safety risks. Real-time detection provides proactive alerts and insights into household operations.

Hardware & Sensors¶

1. Primary Sensors¶

Shelly EM: Ideal for monitoring entire circuits or the whole house via split-core current clamps (50A/120A).
Shelly Plug S / Shelly Plus Plug: Best for monitoring individual high-load appliances like washing machines, fridges, or space heaters.

2. Home Assistant Helper Sensors¶

Utility Meter: Resets periodically (daily/weekly) to track consumption over time. Essential for "Energy Dashboard" integration.
Derivative Sensor: Measures the rate of change (W/min or W/h). High positive values indicate a sudden "spike" in consumption.

Baseline vs. Anomaly Logic¶

To distinguish between normal operation and a true anomaly, we use a combination of statistical thresholds and state-aware filtering.

1. Statistical Baseline¶

Calculation: Average power ($P_{avg}$) and Standard Deviation ($\sigma$) for specific time buckets (e.g., Weekday Morning 07:00-09:00).
Threshold: An anomaly is flagged if $P_{current} > P_{avg} + (2 \times \sigma)$.

2. Rate of Change (Spike Detection)¶

Calculation: Using the Derivative sensor.
Threshold: If the rate of change exceeds $X$ Watts/minute where no high-load appliance (e.g., EV Charger, Oven) has changed state to 'on'.

3. State-Aware Filtering (Context)¶

To reduce false positives, the detection engine must check the state of known high-consumption devices: - EV Charging: If sensor.ev_status == 'charging', ignore spikes up to 7kW. - Washing Machine: If binary_sensor.washer_running == 'on', ignore oscillating patterns typical of motor cycles.

Reference Implementation Pattern¶

Home Assistant Template (Spike Detection)¶

template:
  - binary_sensor:
      - name: "Energy Anomaly Detected"
        state: >
          {% set power = states('sensor.house_total_power') | float %}
          {% set derivative = states('sensor.power_derivative') | float %}
          {% set ev_charging = is_state('sensor.ev_status', 'charging') %}

          {# Trigger if spike is > 2000W and EV is NOT charging #}
          {{ derivative > 2000 and not ev_charging }}
        attributes:
          severity: "high"

n8n Workflow Integration¶

The binary_sensor above can trigger an n8n workflow that uses a local LLM (via Ollama) to classify the anomaly. - Input: Current Power, Previous Hour Average, Active Appliances. - Prompt: "The current power draw is {{ $json.power }}W. The usual average for this time is {{ $json.avg }}W. The following high-load appliances are ON: {{ $json.appliances }}. Is this an anomaly? Respond with 'Normal' or 'Anomaly' and a brief reason."

Sources / References¶

Contribution Metadata¶

Last reviewed: 2026-04-18
Confidence: high