To run the system and apply the optimal heating decision for the current hour:

python main.py

This will:

  1. Fetch current weather and price data
  2. Determine the optimal heating decision
  3. Control the smart plug accordingly

Customizing Heating Parameters

You can modify the heating parameters in the find_heating_decision function call in main.py:

user_input = bool(utils.find_heating_decision(
    temp_price_df,
    decision='discrete',
    heat_loss=0.1,             # Adjust heat loss rate
    heating_power=2,           # Adjust heating power
    min_temperature=18         # Adjust minimum temperature
)[0][0])

Analysis and Visualization

To analyze and visualize the performance of different heating strategies:

# In a Python script or notebook
from strom import utils

# Get temperature and price data
temp_price_df = utils.get_temp_price_df()

# Compare optimal vs baseline strategies
compare_df = utils.compare_decision_costs(
    temp_price_df,
    heat_loss=0.1,
    heating_power=2,
    min_temperature=18
)

# Generate visualization
utils.plot_costs_and_temps(compare_df)

Technical Details

Data Collection

Weather Data

Weather data is fetched from OpenWeatherMap for Barcelona. The system:

  • Gets 3-hour forecast data
  • Interpolates to create hourly temperature forecasts
  • Converts temperatures from Kelvin to Celsius

Electricity Price Data

Price data is fetched from ENTSO-E for Spain. The system:

  • Gets day-ahead market prices
  • Converts prices to €/kWh
  • Aligns timestamps with weather data

Optimization Logic

The optimization uses convex programming (via CVXPY) to determine when to heat based on:

  1. Objective Function: Minimize electricity cost while maintaining comfort
  2. Constraints:
    • Indoor temperature must stay above minimum threshold
    • Thermal dynamics model (heating power, heat loss rate)
    • Initial temperature conditions

The optimization can run in two decision modes:

  • Relaxed: Allows continuous values between 0-1 (partial heating)
  • Discrete: Binary decisions (on/off)

And two optimization types:

  • Optimal: Minimize monetary cost
  • Baseline: Minimize temperature deviation from target

Thermal Model

The system models building thermal dynamics with these parameters:

  • heat_loss: Rate at which the building loses heat to the outdoors (°C/hr per °C difference)
  • heating_power: Rate at which the heater can increase indoor temperature (°C/hr)
  • min_temperature: Minimum acceptable indoor temperature (°C)

Advanced Topics

Customizing Location

To change the location for weather data:

  1. Modify the city parameter in the API call in get_weather_data(): 
    call_str = "https://api.openweathermap.org/data/2.5/forecast?q=YourCity&appid=" + API_KEY

Customizing Country for Electricity Prices

To change the country for electricity prices:

  1. Modify the country code in get_prices(): 
    country_code = 'XX'  # Replace with your country code

Scheduling

For automated operation, consider setting up a cron job (Linux/Mac) or Task Scheduler (Windows) to run main.py at regular intervals.

Troubleshooting

API Connection Issues

If you encounter API connection issues:

  1. Verify your API keys are correct
  2. Check your internet connection
  3. Ensure you haven’t exceeded API rate limits

Smart Plug Connection Issues

If you have trouble connecting to your smart plug:

  1. Verify the IP address is correct
  2. Ensure the smart plug is connected to your network
  3. Check that your TP-Link credentials are correct