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Multi year ramp 2024 #158

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Multi year ramp 2024 #158

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NilsL22 Feb 7, 2025
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121 changes: 106 additions & 15 deletions ramp/core/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,6 +112,7 @@ def __init__(
if self.random_seed:
random.seed(self.random_seed)


@property
def date_start(self):
"""Start date of the daily profiles generated by the UseCase instance"""
Expand Down Expand Up @@ -386,8 +387,9 @@ def calc_peak_time_range(self, peak_enlarge=None):
# The peak_time is randomly enlarged based on the calibration parameter peak_enlarge
return np.arange(peak_time - rand_peak_enlarge, peak_time + rand_peak_enlarge)

"""Added a new input here: num_years"""
def generate_daily_load_profiles(
self, days=None, flat=True, cases=None, verbose=False
self, days=None, flat=True, cases=None, verbose=False, num_years = 1, num_user_lim = None, num_app_lim = None, promotion = 'no'
):
"""
Iterate over the days and generate a daily profile for each of the days
Expand All @@ -407,14 +409,87 @@ def generate_daily_load_profiles(
-------
daily_profiles: numpy array
"""
if cases is not None:
# =============================================================================
# if cases is not None:
# results = {}
# for case in cases:
# profiles = self.generate_daily_load_profiles(days=days, flat=True)
# results[f"case {case}"] = pd.Series(
# index=self.datetimeindex, data=profiles
# )
# answer = Plot(pd.concat(results, axis=1))
# =============================================================================
if num_years > 1:
if promotion == 'yes': #add new appliances if the promotion year has come
app_prom_df = pd.read_excel("Appliance_promotion_example.xlsx")
results = {}
for case in cases:
profiles = self.generate_daily_load_profiles(days=days, flat=True)
results[f"case {case}"] = pd.Series(
original_user_info = {}
new_user_info = {}
for y in range(num_years):
#print("Year ",y)
#print("Number of days: ", self.days)
for user in self.users: #for each user category
#increase the number of users in the user category based on the uniform distribution for all users
if user.num_users < num_user_lim[user.user_name]:
#print("The old number of users: ",user.user_name, user.num_users)
user_increase = int(np.heaviside(y,0)*round(random.uniform(0,3))) # heaviside function is used to make sure that no increases are made in year 0
if user.num_users + user_increase > num_user_lim[user.user_name]:
user_increase = num_user_lim[user.user_name] - user.num_users
user.num_users = random.choice([user.num_users,user.num_users+user_increase])
#print("The new number of users:", user.num_users,"\nUser Increase:", user_increase)
#if user.num_users == num_user_lim[user.user_name]:
# print("Maximum number of users reached")

for app in user.App_list:
if app.number < num_app_lim[app.name]:
#print("The old number of apps: ",app.name, app.number)
app_increase = int(np.heaviside(y,0)*round(random.uniform(0,3)))
if app.number + app_increase > num_app_lim[app.name]:
app_increase = num_app_lim[app.name] - app.number
app.number = random.choice([app.number,app.number+app_increase])
#print("The new number of apps: ",app.number,"\nApp increase: ",app_increase)
#if app.number == num_app_lim[app.name]:
#print("Max number of appliances reached")
for user in self.users:
for n_prom in range(app_prom_df.shape[0]):
if app_prom_df.loc[n_prom,'promotee_name'] == user.user_name and app_prom_df.loc[n_prom,'promotion_year'] == y: #check which appliances have to be promoted
user_new = User(user.user_name + "_promotion", user.num_users, user.user_preference)
self.add_user(user_new)
user_new.add_appliance(
number=int(app_prom_df.loc[n_prom,'number']),
power=float(app_prom_df.loc[n_prom,'power']),
num_windows=int(app_prom_df.loc[n_prom,'num_windows']),
func_time=int(app_prom_df.loc[n_prom,'func_time']),
time_fraction_random_variability=float(app_prom_df.loc[n_prom,'time_fraction_random_variability']),
func_cycle=int(app_prom_df.loc[n_prom,'func_cycle']),
#fixed=app_prom_df.loc[n_prom,'fixed'],
#flat=app_prom_df.loc[n_prom,'flat'],
name=app_prom_df.loc[n_prom,'name'],
occasional_use=float(app_prom_df.loc[n_prom,'occasional_use']),
window_1 = np.array([app_prom_df.loc[n_prom,'window_1_start'],app_prom_df.loc[n_prom,'window_1_end']],dtype=np.intc),
window_2 = np.array([app_prom_df.loc[n_prom,'window_2_start'],app_prom_df.loc[n_prom,'window_2_end']],dtype=np.intc),
window_3 = np.array([app_prom_df.loc[n_prom,'window_3_start'],app_prom_df.loc[n_prom,'window_3_end']],dtype=np.intc),
random_var_w = float(app_prom_df.loc[n_prom,'random_var_w'])
)
#temp_inst.windows([20*60, 24*60], random_var_w=0.3)
# =============================================================================
# if y == 0:
# for user in self.users:
# original_user_info[user.user_name] = user.num_users
# elif y == num_years-1:
# for user in self.users:
# new_user_info[user.user_name] = user.num_users
# print(user.App_list)
# print(original_user_info)
# print(new_user_info)
# =============================================================================
self.save("multiyear output year " + str(y)) # just for checking the use_case. Can be commented out
profiles = self.generate_daily_load_profiles(days=days, flat=True, num_years = 1)
results[f"y {y}"] = pd.Series(
index=self.datetimeindex, data=profiles
)
answer = Plot(pd.concat(results, axis=1))

answer = pd.concat(results, axis=1)
else:
if self.days is None:
if days is not None:
Expand Down Expand Up @@ -613,30 +688,30 @@ def load(self, filename: str) -> None:

# assign windows arguments
for k in WINDOWS_PARAMETERS:
if "window" in k:
w_start = row.get(k + "_start", np.NaN)
w_end = row.get(k + "_end", np.NaN)
if "window" in k:
w_start = row.get(k + "_start", np.nan)
w_end = row.get(k + "_end", np.nan)
if not np.isnan(w_start) and not np.isnan(w_end):
appliance_parameters[k] = np.array(
[w_start, w_end], dtype=np.intc
)
else:
val = row.get(k, np.NaN)
val = row.get(k, np.nan)
if not np.isnan(val):
appliance_parameters[k] = val

# assign duty cycles arguments
for duty_cycle_params in DUTY_CYCLE_PARAMETERS:
for k in duty_cycle_params:
if "cw" in k:
cw_start = row.get(k + "_start", np.NaN)
cw_end = row.get(k + "_end", np.NaN)
cw_start = row.get(k + "_start", np.nan)
cw_end = row.get(k + "_end", np.nan)
if not np.isnan(cw_start) and not np.isnan(cw_end):
appliance_parameters[k] = np.array(
[cw_start, cw_end], dtype=np.intc
)
else:
val = row.get(k, np.NaN)
val = row.get(k, np.nan)
if not np.isnan(val):
appliance_parameters[k] = val

Expand Down Expand Up @@ -667,7 +742,8 @@ def __init__(
# TODO check type of Usecase
self.usecase = usecase
self.user_name = user_name
self.num_users = num_users
"""Changed self.num_users = num_users to self._num_users = num_users"""
self._num_users = num_users
self.user_preference = user_preference
self.rand_daily_pref = 0
self.load = None
Expand Down Expand Up @@ -822,6 +898,16 @@ def num_days(self):
if self.usecase.is_initialized is True:
answer = self.usecase.num_days
return answer

"""Added a new propery with a setter: num_users"""
@property
def num_users(self):
return self._num_users

@num_users.setter
def num_users(self, new_num_users):
self._num_users = new_num_users


def save(self, filename: str = None) -> Union[pd.DataFrame, None]:
"""Saves/returns the model database including all appliances as a single pd.DataFrame or excel file.
Expand Down Expand Up @@ -1046,7 +1132,7 @@ def generate_aggregated_load_profile(
"""

self.load = np.zeros(1440) # initialise empty load for User instance
for _ in range(self.num_users):
for _ in range(self._num_users):
# iterates for every single user within a User class.
self.load = self.load + self.generate_single_load_profile(
prof_i, peak_time_range, day_type
Expand Down Expand Up @@ -1317,6 +1403,11 @@ def __eq__(self, other_appliance) -> bool:
answer = np.array([])
for attribute in APPLIANCE_ATTRIBUTES:
if hasattr(self, attribute) and hasattr(other_appliance, attribute):
#nice_attr = getattr(self, attribute)
#print(nice_attr)
#nice_other_attr = getattr(other_appliance, attribute)
#print(nice_other_attr)
#print("attribute is:", attribute)
answer = np.append(
answer,
[getattr(self, attribute) == getattr(other_appliance, attribute)],
Expand Down