# -*- coding: utf-8 -*-
"""
Implementation of the bdew standard load profiles for electric power.
"""
import logging
from datetime import time as settime
import pandas as pd
from .tools import add_weekdays2df
[docs]class IndustrialLoadProfile:
"""Generate an industrial heat or electric load profile."""
def __init__(self, dt_index, holidays=None):
self.dataframe = pd.DataFrame(index=dt_index)
self.dataframe = add_weekdays2df(
self.dataframe, holiday_is_sunday=True, holidays=holidays
)
[docs] def simple_profile(self, annual_demand, **kwargs):
"""
Create industrial load profile
Parameters
----------
annual_demand : float
Total demand.
Other Parameters
----------------
am : datetime.time
beginning of workday
pm : datetime.time
end of workday
week : list
list of weekdays
weekend : list
list of weekend days
profile_factors : dictionary
dictionary with scaling factors for night and day of weekdays and
weekend days
"""
# Day(am to pm), night (pm to am), week day (week),
# weekend day (weekend)
am = kwargs.get("am", settime(7, 00, 0))
pm = kwargs.get("pm", settime(23, 30, 0))
week = kwargs.get("week", [1, 2, 3, 4, 5])
weekend = kwargs.get("weekend", [0, 6, 7])
default_factors = {
"week": {"day": 0.8, "night": 0.6},
"weekend": {"day": 0.9, "night": 0.7},
}
profile_factors = kwargs.get("profile_factors", default_factors)
self.dataframe["ind"] = 0
self.dataframe["ind"].mask(
cond=self.dataframe["weekday"].between_time(am, pm).isin(week),
other=profile_factors["week"]["day"],
inplace=True,
)
self.dataframe["ind"].mask(
cond=self.dataframe["weekday"].between_time(pm, am).isin(week),
other=profile_factors["week"]["night"],
inplace=True,
)
self.dataframe["ind"].mask(
cond=self.dataframe["weekday"].between_time(am, pm).isin(weekend),
other=profile_factors["weekend"]["day"],
inplace=True,
)
self.dataframe["ind"].mask(
cond=self.dataframe["weekday"].between_time(pm, am).isin(weekend),
other=profile_factors["weekend"]["night"],
inplace=True,
)
if self.dataframe["ind"].isnull().any(axis=0):
logging.error("NAN value found in industrial load profile")
time_interval = self.dataframe.index.freq.nanos / 3.6e12
return (
self.dataframe["ind"]
/ self.dataframe["ind"].sum()
* annual_demand
/ time_interval
)