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 means of the thermal_P_var attribute.

# importing functions
from ramp import User,calc_peak_time_range,yearly_pattern
from ramp import load_data
import pandas as pd

Creating a user category and appliances

household = User()

when the power is variant during the day, the “power” parameter needs to 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

peak_time_range = calc_peak_time_range(
    user_list = [household]
)
year_behaviour = yearly_pattern()

# generating the yearly profiles for different thermal_P_var

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
        household.thermal_P_var = value

        # creating a list to sotre profiles for all the years of the year
        profiles = []
        for day in range(365):

            profile = household.generate_single_load_profile(
                    prof_i = day,
                    peak_time_range = peak_time_range,
                    Year_behaviour = year_behaviour
                )

            profiles.extend(profile)

        # assiging 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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