oemof / demandlib / 4

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Build # 4

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Pull #29

travis-ci

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web-flow

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<a href="https://github.com/oemof/demandlib/commit/<a class=hub.com/oemof/demandlib/commit/7d74da48acbd4b086a8bc2e76438e813e49e7282">7d74da48a<a href="https://github.com/oemof/demandlib/commit/7d74da48acbd4b086a8bc2e76438e813e49e7282">">Merge </a><a class="double-link" href="https://github.com/oemof/demandlib/commit/<a class="double-link" href="https://github.com/oemof/demandlib/commit/761231b725420ab2254b2f8787daf9bb5a0041c8">761231b72</a>">761231b72</a><a href="https://github.com/oemof/demandlib/commit/7d74da48acbd4b086a8bc2e76438e813e49e7282"> into d10b09b9c">d10b09b9c</a>

Pull Request Pull Request #29: Add tests and test-ci

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/demandlib/examples/power_demand_example.py

# -*- coding: utf-8 -*-
"""
Creating power demand profiles using bdew profiles.

Installation requirements
-------------------------
This example requires at least version v0.1.4 of the oemof demandlib. Install
by:
    pip install 'demandlib>=0.1.4,<0.2'
Optional:
    pip install matplotlib

SPDX-FileCopyrightText: Birgit Schachler
SPDX-FileCopyrightText: Uwe Krien <krien@uni-bremen.de>
SPDX-FileCopyrightText: Stephen Bosch

SPDX-License-Identifier: MIT

"""

import datetime
import demandlib.bdew as bdew
import demandlib.particular_profiles as profiles
from datetime import time as settime
try:
    import matplotlib.pyplot as plt
except ImportError:
    print("Install the matplotlib to see the plots.")
    plt = None

# The following dictionary is create by "workalendar"
# pip3 install workalendar
# >>> from workalendar.europe import Germany
# >>> cal = Germany()
# >>> holidays = dict(cal.holidays(2010))

holidays = {
    datetime.date(2010, 5, 24): 'Whit Monday',
    datetime.date(2010, 4, 5): 'Easter Monday',
    datetime.date(2010, 5, 13): 'Ascension Thursday',
    datetime.date(2010, 1, 1): 'New year',
    datetime.date(2010, 10, 3): 'Day of German Unity',
    datetime.date(2010, 12, 25): 'Christmas Day',
    datetime.date(2010, 5, 1): 'Labour Day',
    datetime.date(2010, 4, 2): 'Good Friday',
    datetime.date(2010, 12, 26): 'Second Christmas Day'}


def power_example(testmode=False):
    year = 2010

    ann_el_demand_per_sector = {
        'g0': 3000,
        'h0': 3000,
        'i0': 3000,
        'i1': 5000,
        'i2': 6000,
        'g6': 5000}

    # read standard load profiles
    e_slp = bdew.ElecSlp(year, holidays=holidays)

    # multiply given annual demand with timeseries
    elec_demand = e_slp.get_profile(ann_el_demand_per_sector,
                                    dyn_function_h0=True)

    # Add the slp for the industrial group
    ilp = profiles.IndustrialLoadProfile(e_slp.date_time_index,
                                         holidays=holidays)

    # Beginning and end of workday, weekdays and weekend days, and scaling
    # factors by default
    elec_demand['i0'] = ilp.simple_profile(ann_el_demand_per_sector['i0'])

    # Set beginning of workday to 9 am
    elec_demand['i1'] = ilp.simple_profile(ann_el_demand_per_sector['i1'],
                                           am=settime(9, 0, 0))

    # Change scaling factors
    elec_demand['i2'] = ilp.simple_profile(
        ann_el_demand_per_sector['i2'],
        profile_factors={'week': {'day': 1.0, 'night': 0.8},
                         'weekend': {'day': 0.8, 'night': 0.6}})

    print("Be aware that the values in the DataFrame are 15minute values with "
          "a power unit. If you sum up a table with 15min values the result "
          "will be of the unit 'kW15minutes'.")
    print(elec_demand.sum())

    print("You will have to divide the result by 4 to get kWh.")
    print(elec_demand.sum() / 4)

    print("Or resample the DataFrame to hourly values using the mean() "
          "method.")

    # Resample 15-minute values to hourly values.
    elec_demand = elec_demand.resample('H').mean()
    print(elec_demand.sum())

    if plt is not None and not testmode:
        # Plot demand
        ax = elec_demand.plot()
        ax.set_xlabel("Date")
        ax.set_ylabel("Power demand")
        plt.show()

    return elec_demand


if __name__ == '__main__':
    print(power_example())

1	# -- coding: utf-8 --
2	"""	2✔
3	Creating power demand profiles using bdew profiles.
4
5	Installation requirements
6	-------------------------
7	This example requires at least version v0.1.4 of the oemof demandlib. Install
8	by:
9	pip install 'demandlib>=0.1.4,<0.2'
10	Optional:
11	pip install matplotlib
12
13	SPDX-FileCopyrightText: Birgit Schachler
14	SPDX-FileCopyrightText: Uwe Krien <krien@uni-bremen.de>
15	SPDX-FileCopyrightText: Stephen Bosch
16
17	SPDX-License-Identifier: MIT
18
19	"""
20
21	import datetime	2✔
22	import demandlib.bdew as bdew	2✔
23	import demandlib.particular_profiles as profiles	2✔
24	from datetime import time as settime	2✔
25	try:	2✔
26	import matplotlib.pyplot as plt	2✔
27	except ImportError:	2✔
28	print("Install the matplotlib to see the plots.")	2✔
29	plt = None	2✔
30
31	# The following dictionary is create by "workalendar"
32	# pip3 install workalendar
33	# >>> from workalendar.europe import Germany
34	# >>> cal = Germany()
35	# >>> holidays = dict(cal.holidays(2010))
36
37	holidays = {	2✔
38	datetime.date(2010, 5, 24): 'Whit Monday',
39	datetime.date(2010, 4, 5): 'Easter Monday',
40	datetime.date(2010, 5, 13): 'Ascension Thursday',
41	datetime.date(2010, 1, 1): 'New year',
42	datetime.date(2010, 10, 3): 'Day of German Unity',
43	datetime.date(2010, 12, 25): 'Christmas Day',
44	datetime.date(2010, 5, 1): 'Labour Day',
45	datetime.date(2010, 4, 2): 'Good Friday',
46	datetime.date(2010, 12, 26): 'Second Christmas Day'}
47
48
49	def power_example(testmode=False):	2✔
50	year = 2010	2✔
51
52	ann_el_demand_per_sector = {	2✔
53	'g0': 3000,
54	'h0': 3000,
55	'i0': 3000,
56	'i1': 5000,
57	'i2': 6000,
58	'g6': 5000}
59
60	# read standard load profiles
61	e_slp = bdew.ElecSlp(year, holidays=holidays)	2✔
62
63	# multiply given annual demand with timeseries
64	elec_demand = e_slp.get_profile(ann_el_demand_per_sector,	2✔
65	dyn_function_h0=True)
66
67	# Add the slp for the industrial group
68	ilp = profiles.IndustrialLoadProfile(e_slp.date_time_index,	2✔
69	holidays=holidays)
70
71	# Beginning and end of workday, weekdays and weekend days, and scaling
72	# factors by default
73	elec_demand['i0'] = ilp.simple_profile(ann_el_demand_per_sector['i0'])	2✔
74
75	# Set beginning of workday to 9 am
76	elec_demand['i1'] = ilp.simple_profile(ann_el_demand_per_sector['i1'],	2✔
77	am=settime(9, 0, 0))
78
79	# Change scaling factors
80	elec_demand['i2'] = ilp.simple_profile(	2✔
81	ann_el_demand_per_sector['i2'],
82	profile_factors={'week': {'day': 1.0, 'night': 0.8},
83	'weekend': {'day': 0.8, 'night': 0.6}})
84
85	print("Be aware that the values in the DataFrame are 15minute values with "	2✔
86	"a power unit. If you sum up a table with 15min values the result "
87	"will be of the unit 'kW15minutes'.")
88	print(elec_demand.sum())	2✔
89
90	print("You will have to divide the result by 4 to get kWh.")	2✔
91	print(elec_demand.sum() / 4)	2✔
92
93	print("Or resample the DataFrame to hourly values using the mean() "	2✔
94	"method.")
95
96	# Resample 15-minute values to hourly values.
97	elec_demand = elec_demand.resample('H').mean()	2✔
98	print(elec_demand.sum())	2✔
99
100	if plt is not None and not testmode:	2✔
101	# Plot demand
102	ax = elec_demand.plot()	×
103	ax.set_xlabel("Date")	×
104	ax.set_ylabel("Power demand")	×
105	plt.show()	×
106
107	return elec_demand	2✔
108
109
110	if __name__ == '__main__':	2✔
111	print(power_example())	×

oemof / demandlib / 4

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