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Quick Start Guide

Get your heating optimization running in 15 minutes!

Prerequisites Checklist

Before starting, ensure you have:

  • [x] Home Assistant 2023.1+ installed
  • [x] HACS installed
  • [x] Basic knowledge of your building (area, construction quality)
  • [x] Electricity price sensor (or know your fixed price)
  • [x] Power consumption sensor

Step-by-Step Setup

1. Install the Integration

  1. Open HACS in Home Assistant
  2. Go to Integrations
  3. Click ⋮ (menu) → Custom repositories
  4. Add https://github.com/bvweerd/heating_curve_optimizer
  5. Category: Integration
  6. Click Download
  7. Restart Home Assistant
  1. Download from GitHub Releases
  2. Extract to custom_components/heating_curve_optimizer/
  3. Restart Home Assistant

2. Add the Integration

  1. Navigate to Settings → Devices & Services
  2. Click + ADD INTEGRATION
  3. Search for "Heating Curve Optimizer"
  4. Click to start configuration wizard

3. Configure Building Parameters

Basic Settings

Fill in your building characteristics:

Parameter Example Value How to Find
Area 150 m² Floor plan or property documents
Energy Label C Energy Performance Certificate (EPC)
Glass East 4 m² Measure windows facing ±45° of east
Glass West 4 m² Measure windows facing ±45° of west
Glass South 10 m² Measure windows facing ±45° of south
Glass U-value 1.2 W/m²K Window specifications (1.2 = standard double glazing)

Energy Label

In Netherlands: Check ep-online.nl

No label? Use these estimates:

  • New home (< 10 years): A or B
  • Renovated (insulation upgraded): B or C
  • Average home (20-40 years): C or D
  • Older home (> 40 years, no upgrades): E or F

Heat Pump Settings

Parameter Typical Value Notes
Base COP 3.5 - 4.5 Check datasheet at A7/W35 condition
K-Factor 0.025 - 0.035 Use 0.03 if unsure
COP Compensation 0.85 - 0.95 Use 0.90 if unsure

Where to Find COP

Your heat pump datasheet will list COP at various conditions:

  • Look for A7/W35 rating (7°C air, 35°C water)
  • Example: "COP 4.2 at A7/W35" → Base COP = 4.2

4. Select Sensors

Consumption Sensor

Select your electricity consumption sensor:

  • Type: Power or Energy sensor
  • Units: W or kW
  • Example: sensor.power_consumption

Common sources:

  • P1 smart meter integration
  • Shelly EM
  • Energy monitor device
  • Utility integration (e.g., HomeWizard)

Production Sensor (Optional)

If you have solar panels:

  • Select: Solar production sensor
  • Units: W or kW
  • Example: sensor.solar_power

If no solar, leave empty.

5. Configure Prices

Consumption Price

Select your dynamic price sensor:

  • Nordpool integration: sensor.nordpool_kwh_nl_eur_3_10_0
  • ENTSO-E: sensor.entsoe_price
  • Energy tariffs: Your configured price sensor

Leave sensor empty, the integration will use current price as fixed value.

Production Price (Optional)

If you have solar and receive feed-in compensation:

  • Select: Production price sensor (if dynamic)
  • Or leave empty: Integration will use consumption price - typical margin

6. Finish Configuration

Click Submit to complete setup.

You should see:

✅ "Integration configured successfully"

Verify Installation

Check Sensors

Navigate to Developer Tools → States

Search for: sensor.heating_curve_optimizer

You should see 17 sensors:

Key Sensors

Sensor Expected State Notes
outdoor_temperature Numeric (°C) Should update every 5 min
heat_loss Numeric (kW) Current heat loss
net_heat_loss Numeric (kW) After solar gain
heating_curve_offset -4 to +4 Main optimization output
current_electricity_price Numeric (€/kWh) Current price

Sensor Unavailable?

If sensors show "unavailable":

  1. Wait 5 minutes for first update
  2. Check integration logs: Settings → System → Logs
  3. See Troubleshooting Guide

Check Number Entities

You should see 5 number entities for manual control:

  • number.heating_curve_optimizer_offset
  • number.heating_curve_optimizer_min_supply_temp
  • number.heating_curve_optimizer_max_supply_temp
  • number.heating_curve_optimizer_min_outdoor_temp
  • number.heating_curve_optimizer_max_outdoor_temp

Connect to Your Heating System

The integration outputs the optimal offset, but you need to apply it to your heating system.

Create an automation to apply the offset:

automation:
  - alias: "Apply Heating Curve Offset"
    trigger:
      - platform: state
        entity_id: sensor.heating_curve_optimizer_heating_curve_offset
    action:
      - service: climate.set_temperature
        target:
          entity_id: climate.your_heat_pump
        data:
          temperature: >
            {{ states('sensor.calculated_supply_temperature') }}

Or if your heat pump supports offset directly:

automation:
  - alias: "Apply Heating Curve Offset"
    trigger:
      - platform: state
        entity_id: sensor.heating_curve_optimizer_heating_curve_offset
    action:
      - service: number.set_value
        target:
          entity_id: number.heat_pump_offset
        data:
          value: >
            {{ states('sensor.heating_curve_optimizer_heating_curve_offset') }}

Option 2: Node-RED

Use Node-RED for more complex logic:

  1. Trigger: State change of heating_curve_offset sensor
  2. Function: Process offset value
  3. Action: Call heat pump service

Option 3: Manual Monitoring

View the recommended offset in Lovelace:

type: entities
entities:
  - entity: sensor.heating_curve_optimizer_heating_curve_offset
  - entity: sensor.heating_curve_optimizer_optimized_supply_temperature
  - entity: sensor.heating_curve_optimizer_heat_buffer

Manually adjust your heat pump offset to match the recommendation.

Dashboard Card (Optional)

Create a nice dashboard view:

type: vertical-stack
cards:
  - type: entities
    title: Heating Curve Optimizer
    entities:
      - entity: sensor.heating_curve_optimizer_heating_curve_offset
        name: Optimal Offset
      - entity: sensor.heating_curve_optimizer_heat_buffer
        name: Thermal Buffer
      - entity: sensor.heating_curve_optimizer_current_electricity_price
        name: Current Price

  - type: history-graph
    title: Offset History
    hours_to_show: 24
    entities:
      - entity: sensor.heating_curve_optimizer_heating_curve_offset

  - type: entities
    title: Manual Controls
    entities:
      - entity: number.heating_curve_optimizer_offset
      - entity: number.heating_curve_optimizer_min_supply_temp
      - entity: number.heating_curve_optimizer_max_supply_temp

Initial Monitoring (First 24 Hours)

What to Watch

  1. Offset values: Should range from -4 to +4, changing gradually
  2. Buffer accumulation: Should increase during sunny/warm periods
  3. Price correlation: Offset should increase during low-price periods
  4. No errors: Check logs for warnings/errors

Expected Behavior

Morning (low prices):

  • Offset: +1 to +3 (pre-heating)
  • Buffer: Accumulating

Midday (high prices):

  • Offset: -2 to 0 (reduced heating)
  • Buffer: Being used

Evening:

  • Offset: 0 to +2 (normal heating)
  • Buffer: Depleting

Fine-Tuning

After a few days, consider adjusting:

1. COP Parameters

Monitor your heat pump's actual efficiency:

  • If optimizer seems too conservative: Decrease k-factor
  • If optimizer is aggressive: Increase k-factor

2. Temperature Limits

Adjust comfort bounds:

# Via number entities or configuration
Min Supply Temperature: 30°C  # Lower = more optimization flexibility
Max Supply Temperature: 50°C  # Higher = more heat capacity

3. Planning Window

  • 6 hours (default): Good balance
  • 12 hours: More pre-heating opportunities
  • 24 hours: Maximum optimization (higher computation)

Common Questions

Offset not changing?

Possible causes:

  • Price sensor not providing forecast → Check price sensor attributes
  • All prices similar → Optimization limited with flat pricing
  • Very cold weather → Limited flexibility at max capacity

Check: sensor.heating_curve_optimizer_diagnostics for details

Buffer always zero?

Possible causes:

  • No solar gain configured → Add window areas
  • Winter period → Lower solar radiation
  • Very cold → Heat loss exceeds solar gain

Expected: Buffer only accumulates when solar gain exceeds heat loss

Optimizing Performance

Factors affecting optimization effectiveness:

  • Price volatility: Higher volatility provides more optimization opportunities
  • Weather: Shoulder seasons have best optimization potential
  • Building thermal mass: Better insulation = better buffering

Expected outcomes:

  • Dynamic pricing: Higher cost reduction potential
  • Fixed pricing: Lower cost reduction potential (COP optimization only)

Next Steps

Now that you're running:

Congratulations! Your heating optimization is now active. 🎉

Monitor your energy costs over the next few weeks to observe the optimization in action!