Cooking Appliances

In this example, appliances with multiple preferences index and attributes are modeled.

To have a better understanding of RAMP features for modelling these category of appliances, two households are considered:

1. First household with a fixed lunch habit of eating soup everyday.

2. Second household with two lunch preferences: cooking soup or rice.

The number of user preferences can be specified through “user_preference” parameter when initializing a User instance.

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

Creating a user category

user_1 = User(
    user_name = "Household with single lunch habit",
    num_users = 1,
    user_preference = 1, # user_1 has only one lunch preference
)

user_2 = User(
    user_name = "Household with different lunch habit",
    num_users = 1,
    user_preference = 2, # user_2 has two lunch preferences
)

Defining the cycles for cooking soup and rice

for cooking soup it is assumed that the user needs 25 minutes divided into two parts:

cycle	power	time
Boiling Water	1200	5
Cooking soup	750	20

for cooking rice it is assumed that the user needs 15 minutes divided into two parts:

cycle	power	time
Boiling Water	1200	5
Cooking rice	600	10

# soup for lunch
soup_1 = user_1.add_appliance(
    name = "soup for lunch",
    power = 1200,
    func_time = 25,
    func_cycle = 25,
    thermal_p_var = 0.2,
    fixed_cycle = 1,
    window_1 = [12*60,15*60],
    p_11 = 1200,   # power of the first cycle
    t_11 = 5,      # time needed for the first cycle
    p_12 = 750,    # power of the second cycle
    t_12 = 20,     # time needed for the second cycle
    cw11 = [12*60,15*60]
)

As second user has two different preferences for lunch, we need to model this preferences and their characterisitics as two different appliances.

Each preference needs to be specified with the cooking energy needs like the power, functioning time and the duty cycles of the cooking process.

More importantly, for each preference, the user needs to specify the index of preference by using pref_index parameter. In this example, soup is the first prefernce of the user (pref_index = 1), and rice is the second one (pref_index = 2).

# soup for lunch
soup_2 = user_2.add_appliance(
    name = "soup for lunch",
    power = 1200,
    func_time = 25,
    func_cycle = 25,
    thermal_p_var = 0.2,
    fixed_cycle = 1,
    pref_index = 1, # the first preference
    window_1 = [12*60,15*60],
    p_11 = 1200,   # power of the first cycle
    t_11 = 5,      # time needed for the first cycle
    p_12 = 750,    # power of the second cycle
    t_12 = 20,     # time needed for the second cycle
    cw11 = [12*60,15*60]
)

# rice for lunch
rice_2 = user_2.add_appliance(
    name = "rice for lunch",
    power = 1200,
    func_time = 15,
    func_cycle = 15,
    thermal_p_var = 0.2,
    pref_index = 2,  # the second preference
    fixed_cycle = 1,
    window_1 = [12*60,15*60],
    p_11 = 1200,   # power of the first cycle
    t_11 = 5,      # time needed for the first cycle
    p_12 = 600,    # power of the second cycle
    t_12 = 10,     # time needed for the second cycle
    cw11 = [12*60,15*60]

)

# you can have an overview of data inputs by usering User.export_to_dataframe method
user_lunch = UseCase(users=[user_1,user_2])
user_lunch.export_to_dataframe().T

	0	1	2
user_name	Household with single lunch habit	Household with different lunch habit	Household with different lunch habit
num_users	1	1	1
user_preference	1	2	2
name	soup for lunch	soup for lunch	rice for lunch
number	1	1	1
power	1200.0	1200.0	1200.0
num_windows	1	1	1
func_time	25	25	15
time_fraction_random_variability	0	0	0
func_cycle	25	25	15
fixed	no	no	no
fixed_cycle	1	1	1
occasional_use	1	1	1
flat	no	no	no
thermal_p_var	0.2	0.2	0.2
pref_index	0	1	2
wd_we_type	2	2	2
p_11	1200	1200	1200
t_11	5	5	5
cw11_start	720	720	720
cw11_end	900	900	900
p_12	750	750	600
t_12	20	20	10
cw12_start	0	0	0
cw12_end	0	0	0
r_c1	0	0	0
p_21	0	0	0
t_21	0	0	0
cw21_start	0	0	0
cw21_end	0	0	0
p_22	0	0	0
t_22	0	0	0
cw22_start	0	0	0
cw22_end	0	0	0
r_c2	0	0	0
p_31	0	0	0
t_31	0	0	0
cw31_start	0	0	0
cw31_end	0	0	0
p_32	0	0	0
t_32	0	0	0
cw32_start	0	0	0
cw32_end	0	0	0
r_c3	0	0	0
window_1_start	720	720	720
window_1_end	900	900	900
window_2_start	0	0	0
window_2_end	0	0	0
window_3_start	0	0	0
window_3_end	0	0	0
random_var_w	0	0	0

Generating a profile for some months

peak_time_range = calc_peak_time_range(
    user_list = user_lunch.users
)
year_behaviour = yearly_pattern()

# number of days
n_days = 90

# storing all the profiles for all the users
profiles = pd.DataFrame(index = pd.date_range(start = "2020-01-01",periods = 1440*n_days,freq="T"))

for user in user_lunch.users:

    # storing daily profiles for a user
    user_profiles = []
    for day in range(n_days):
        single_profile = user.generate_single_load_profile(
            prof_i = day, # the day to generate the profile
            peak_time_range = peak_time_range,
            Year_behaviour = year_behaviour
        )

        user_profiles.extend(single_profile)

    profiles[user.user_name] = user_profiles

As the second user has the probability of cooking rice for lunch, and lunch has a less energy intensive cooking cycle, it is expected to see that the user with fixed lunch meal has higher energy consumption in most of the cases.

# daily energy consumption
profiles.resample("1d").sum().plot(title = "daily energy consumption")

<AxesSubplot:title={'center':'daily energy consumption'}>