Thermal loads

This example input file represents a single household user whose only load is the “shower”. The example showcases how to model thermal loads by: 1) using a time-varying average power attribute, pre-calculated as a function of the average daily groundwater temperature; and 2) using the thermal_p_var attribute to add further variability to the actual power absorbed by the appliance in each usage event, which reflects the randomness of user behaviour in preferring a slightly warmer or colder shower temperature.

# importing functions
from ramp import User, UseCase, load_data, get_day_type
import pandas as pd

Creating a user category and appliances

household = User()

When the power varies across days of the year, for instance, as a function of the average daily groundwater temperature, the “power” parameter can be passed as a pd.DataFrame or np.array with a daily profile (365 rows of data). For this exercise, data can be loaded from the default examples in ramp:

shower_power = load_data(example="shower")

# shower power distribution
shower_power.plot()

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shower = household.add_appliance(
    name="Shower",
    number=1,
    power=shower_power,
    num_windows=2,
    func_time=15,
    time_fraction_random_variability=0.1,
    func_cycle=3,
    window_1=[390, 540],
    window_2=[1080, 1200],
    random_var_w=0.2,
)

Generating profiles for increasing degrees of thermal_p_var

usecase = UseCase(users=[household], date_start="2020-01-01")
usecase.initialize(num_days=365)

You will simulate 365 day(s) from 2020-01-01 00:00:00 until 2020-12-31 00:00:00

def thermal_p_var_sensitivity(values):
    # buidling a pd.DataFrame for saving sensitivity results
    results = pd.DataFrame(
        index=pd.date_range(start="2020-01-01", periods=1440 * 365, freq="T"),
        columns=[f"p_var = {value}" for value in values],
    )

    for value in values:
        # changing the thermal_P_var
        shower.thermal_p_var = value

        profiles = usecase.generate_daily_load_profiles(flat=True)

        # assigning the yearly profile for a given sensitivity case
        results[f"p_var = {value}"] = profiles

    return results

sensitivity_results = thermal_p_var_sensitivity([0, 0.25, 0.5, 0.75, 1])

sensitivity_results

	p_var = 0	p_var = 0.25	p_var = 0.5	p_var = 0.75	p_var = 1
2020-01-01 00:00:00	0.0	0.0	0.0	0.0	0.0
2020-01-01 00:01:00	0.0	0.0	0.0	0.0	0.0
2020-01-01 00:02:00	0.0	0.0	0.0	0.0	0.0
2020-01-01 00:03:00	0.0	0.0	0.0	0.0	0.0
2020-01-01 00:04:00	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...
2020-12-30 23:55:00	0.0	0.0	0.0	0.0	0.0
2020-12-30 23:56:00	0.0	0.0	0.0	0.0	0.0
2020-12-30 23:57:00	0.0	0.0	0.0	0.0	0.0
2020-12-30 23:58:00	0.0	0.0	0.0	0.0	0.0
2020-12-30 23:59:00	0.0	0.0	0.0	0.0	0.0

525600 rows × 5 columns

# showing the daily average of the load profiles
average_daily_profiles = sensitivity_results.resample("1d").mean()

average_daily_profiles.plot()

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first_day = pd.date_range(
    start="2020-01-01 00:00:00", freq="1min", periods=24 * 60  # a full day
)
sensitivity_results.loc[first_day].plot()

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