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Merge pull request #245 from RWTH-EBC/issue242_release
Issue242 release
2 parents 7d682cc + f2e14a0 commit a9ad117

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‎README.md‎

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@@ -73,7 +73,190 @@ by adding new .py file and trying to import uesgraphs and pyCity.
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Import should be possible without errors.
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## Example usage
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```Python
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import shapely.geometry.point as point
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import matplotlib.pyplot as plt
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import uesgraphs.visuals as uesvis
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import pycity_base.classes.Timer as time
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import pycity_base.classes.Weather as weath
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import pycity_base.classes.Prices as price
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import pycity_base.classes.Environment as env
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import pycity_base.classes.demand.Apartment as apart
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import pycity_base.classes.demand.Occupancy as occ
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import pycity_base.classes.demand.DomesticHotWater as dhw
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import pycity_base.classes.demand.ElectricalDemand as eldem
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import pycity_base.classes.demand.SpaceHeating as spaceheat
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import pycity_base.classes.Building as build
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import pycity_base.classes.CityDistrict as citydist
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import pycity_base.classes.supply.BES as besys
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import pycity_base.classes.supply.Boiler as boil
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import pycity_base.classes.supply.PV as pvsys
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def main():
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# Define the time discretization for the timer object
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timestep = 3600 # in seconds
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# Define the total number of timesteps (in this case for one year)
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nb_timesteps = int(365 * 24 * 3600 / timestep)
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# Generate environment with timer, weather, and prices objects
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# ######################################################################
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timer = time.Timer(timeDiscretization=timestep,
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timestepsTotal=nb_timesteps)
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weather = weath.Weather(timer=timer)
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prices = price.Prices()
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environment = env.Environment(timer=timer, weather=weather, prices=prices)
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# Generate city district object
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# ######################################################################
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city_district = citydist.CityDistrict(environment=environment)
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# Annotations: To prevent some methods of subclasses uesgraph / nx.Graph
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# from failing (e.g. '.subgraph()) environment is set as optional input
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# parameter. However, it is necessary to use an environment object as
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# input parameter to initialize a working cityDistrict object!
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# Empty dictionary for building positions
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dict_pos = {}
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# Generate shapely point positions
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dict_pos[0] = point.Point(0, 0) # (x, y)
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dict_pos[1] = point.Point(20, 0)
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# Use for loop to generate two identical building objects for city
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# district
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# ######################################################################
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for i in range(2):
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living_area = 200 # in m2
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spec_sh_dem = 160 # Specific space heating demand in kWh/m2
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number_occupants = 3 # Total number of occupants
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# Generate space heating demand object (holding loadcurve attribute
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# with space heating power)
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heat_demand = spaceheat.SpaceHeating(
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environment=environment,
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method=1, # Standard load profile
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livingArea=living_area, # in m2
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specificDemand=spec_sh_dem) # in kWh/m2
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# Generate occupancy object with stochastic user profile
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occupancy = occ.Occupancy(environment=environment,
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number_occupants=number_occupants)
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# Generate electrical demand object
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el_dem_stochastic = eldem.ElectricalDemand(
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environment=environment,
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method=2, # stochastic Richardson profile (richardsonpy)
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total_nb_occupants=number_occupants, # Number of occupants
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randomizeAppliances=True, # Random choice of installed appliances
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lightConfiguration=10, # Light bulb configuration nb.
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occupancy=occupancy.occupancy, # Occupancy profile (600 s resol.)
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prev_heat_dev=True, # Prevent space heating and hot water devices
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annualDemand=None, # Annual el. demand in kWh could be used for
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do_normalization=False) # rescaling (if do_normalization is True)
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# Annotation: The calculation of stochastic electric load profiles
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# is time consuming. If you prefer a faster method, you can either
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# hand over an own array-like load curve (method=0) or generate a
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# standardized load profile (SLP) (method=1)
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# Generate domestic hot water demand object
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dhw_obj = dhw.DomesticHotWater(
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environment=environment,
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tFlow=60, # DHW output temperature in degree Celsius
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method=2, # Stochastic dhw profile
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supplyTemperature=25, # DHW inlet flow temperature in degree C.
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occupancy=occupancy.occupancy) # Occupancy profile (600 s resol.)
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# Generate apartment and add demand durves
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apartment = apart.Apartment(environment)
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apartment.addMultipleEntities([heat_demand,
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el_dem_stochastic,
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dhw_obj])
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# Generate building and add apartment
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building = build.Building(environment)
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building.addEntity(apartment)
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# Add buildings to city district
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city_district.addEntity(entity=building,
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position=dict_pos[i])
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# Access information on city district object instance
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# ######################################################################
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print('Get number of building entities:')
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print(city_district.get_nb_of_building_entities())
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print()
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print('Get list with node ids of building entities:')
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print(city_district.get_list_build_entity_node_ids())
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print()
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print('Get city district overall space heating power load curve:')
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print(city_district.get_aggr_space_h_power_curve())
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print()
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# We can use the Visuals class of uesgraphs to plot the city district
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# Generate uesgraphs visuals object instance
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uesvisuals = uesvis.Visuals(uesgraph=city_district)
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fig = plt.figure()
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ax = fig.gca()
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ax = uesvisuals.create_plot_simple(ax=ax)
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plt.show()
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plt.close()
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# Access buildings
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# ######################################################################
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# As city_district is a networkx graph object, we can access the building
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# entities with the corresponding building node,
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# Pointer to building object with id 1001:
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building_1001 = city_district.nodes[1001]['entity']
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print('Get building 1001 electric load curve:')
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print(building_1001.get_electric_power_curve())
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print()
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# Add energy systems to buildings
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# ######################################################################
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# We can also add building energy systems (BES) to each building object
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# Generate boiler object
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boiler = boil.Boiler(environment=environment,
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qNominal=10000, # Boiler thermal power in Watt
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eta=0.85) # Boiler efficiency
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# Generate PV module object
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pv = pvsys.PV(environment=environment,
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area=30, # Area in m2
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eta=0.15) # Electrical efficiency at NOCT conditions
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# Instantiate BES (container object for all energy systems)
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bes = besys.BES(environment)
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# Add energy systems to bes
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bes.addMultipleDevices([boiler, pv])
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# Add bes to building 1001
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building_1001.addEntity(entity=bes)
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print('Does building 1001 has a building energy system (BES)?')
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print(building_1001.hasBes)
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# Access boiler nominal thermal power
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print('Nominal thermal power of boiler in kW:')
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print(building_1001.bes.boiler.qNominal / 1000)
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if __name__ == '__main__':
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# Run program
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main()
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```
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## Tutorial
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‎pycity_base/classes/demand/ElectricalDemand.py‎

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@@ -187,8 +187,8 @@ def __init__(self,
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q_diffuse=q_diffuse,
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annual_demand=annualDemand,
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is_sfh=singleFamilyHouse,
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path_app=None,
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path_light=None,
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path_app=app_filename,
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path_light=light_filename,
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randomize_appliances=randomizeAppliances,
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prev_heat_dev=prev_heat_dev,
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light_config=lightConfiguration,

‎pycity_base/examples/example_cityDistrict_stoch.py‎

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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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Example script on how to generate a city district with space heating,
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user, stochastic electric, and hot water profiles plus energy systems.
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"""
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from __future__ import division
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import shapely.geometry.point as point
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import matplotlib.pyplot as plt
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import uesgraphs.visuals as uesvis
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import pycity_base.classes.Timer as time
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import pycity_base.classes.Weather as weath
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import pycity_base.classes.Prices as price
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import pycity_base.classes.Environment as env
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import pycity_base.classes.demand.Apartment as apart
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import pycity_base.classes.demand.Occupancy as occ
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import pycity_base.classes.demand.DomesticHotWater as dhw
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import pycity_base.classes.demand.ElectricalDemand as eldem
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import pycity_base.classes.demand.SpaceHeating as spaceheat
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import pycity_base.classes.Building as build
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import pycity_base.classes.CityDistrict as citydist
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import pycity_base.classes.supply.BES as besys
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import pycity_base.classes.supply.Boiler as boil
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import pycity_base.classes.supply.PV as pvsys
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def run_test():
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# Define the time discretization for the timer object
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timestep = 3600 # in seconds
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# Define the total number of timesteps (in this case for one year)
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nb_timesteps = int(365 * 24 * 3600 / timestep)
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# Generate environment with timer, weather, and prices objects
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# ######################################################################
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timer = time.Timer(timeDiscretization=timestep,
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timestepsTotal=nb_timesteps)
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weather = weath.Weather(timer=timer)
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prices = price.Prices()
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environment = env.Environment(timer=timer, weather=weather, prices=prices)
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# Generate city district object
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# ######################################################################
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city_district = citydist.CityDistrict(environment=environment)
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# Annotations: To prevent some methods of subclasses uesgraph / nx.Graph
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# from failing (e.g. '.subgraph()) environment is set as optional input
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# parameter. However, it is necessary to use an environment object as
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# input parameter to initialize a working cityDistrict object!
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# Empty dictionary for building positions
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dict_pos = {}
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# Generate shapely point positions
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dict_pos[0] = point.Point(0, 0) # (x, y)
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dict_pos[1] = point.Point(20, 0)
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# Use for loop to generate two identical building objects for city
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# district
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# ######################################################################
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for i in range(2):
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living_area = 200 # in m2
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spec_sh_dem = 160 # Specific space heating demand in kWh/m2
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number_occupants = 3 # Total number of occupants
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# Generate space heating demand object (holding loadcurve attribute
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# with space heating power)
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heat_demand = spaceheat.SpaceHeating(
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environment=environment,
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method=1, # Standard load profile
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livingArea=living_area, # in m2
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specificDemand=spec_sh_dem) # in kWh/m2
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# Generate occupancy object with stochastic user profile
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occupancy = occ.Occupancy(environment=environment,
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number_occupants=number_occupants)
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# Generate electrical demand object
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el_dem_stochastic = eldem.ElectricalDemand(
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environment=environment,
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method=2, # stochastic Richardson profile (richardsonpy)
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total_nb_occupants=number_occupants, # Number of occupants
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randomizeAppliances=True, # Random choice of installed appliances
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lightConfiguration=10, # Light bulb configuration nb.
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occupancy=occupancy.occupancy, # Occupancy profile (600 s resol.)
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prev_heat_dev=True, # Prevent space heating and hot water devices
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annualDemand=None, # Annual el. demand in kWh could be used for
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do_normalization=False) # rescaling (if do_normalization is True)
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# Annotation: The calculation of stochastic electric load profiles
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# is time consuming. If you prefer a faster method, you can either
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# hand over an own array-like load curve (method=0) or generate a
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# standardized load profile (SLP) (method=1)
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# Generate domestic hot water demand object
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dhw_obj = dhw.DomesticHotWater(
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environment=environment,
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tFlow=60, # DHW output temperature in degree Celsius
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method=2, # Stochastic dhw profile
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supplyTemperature=25, # DHW inlet flow temperature in degree C.
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occupancy=occupancy.occupancy) # Occupancy profile (600 s resol.)
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# Generate apartment and add demand durves
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apartment = apart.Apartment(environment)
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apartment.addMultipleEntities([heat_demand,
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el_dem_stochastic,
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dhw_obj])
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# Generate building and add apartment
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building = build.Building(environment)
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building.addEntity(apartment)
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# Add buildings to city district
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city_district.addEntity(entity=building,
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position=dict_pos[i])
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# Access information on city district object instance
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# ######################################################################
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print('Get number of building entities:')
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print(city_district.get_nb_of_building_entities())
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print()
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print('Get list with node ids of building entities:')
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print(city_district.get_list_build_entity_node_ids())
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print()
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print('Get city district overall space heating power load curve:')
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print(city_district.get_aggr_space_h_power_curve())
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print()
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# We can use the Visuals class of uesgraphs to plot the city district
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# Generate uesgraphs visuals object instance
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uesvisuals = uesvis.Visuals(uesgraph=city_district)
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fig = plt.figure()
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ax = fig.gca()
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ax = uesvisuals.create_plot_simple(ax=ax)
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plt.show()
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plt.close()
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# Access buildings
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# ######################################################################
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# As city_district is a networkx graph object, we can access the building
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# entities with the corresponding building node,
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# Pointer to building object with id 1001:
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building_1001 = city_district.nodes[1001]['entity']
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print('Get building 1001 electric load curve:')
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print(building_1001.get_electric_power_curve())
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print()
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# Add energy systems to buildings
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# ######################################################################
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# We can also add building energy systems (BES) to each building object
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# Generate boiler object
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boiler = boil.Boiler(environment=environment,
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qNominal=10000, # Boiler thermal power in Watt
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eta=0.85) # Boiler efficiency
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# Generate PV module object
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pv = pvsys.PV(environment=environment,
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area=30, # Area in m2
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eta=0.15) # Electrical efficiency at NOCT conditions
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# Instantiate BES (container object for all energy systems)
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bes = besys.BES(environment)
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# Add energy systems to bes
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bes.addMultipleDevices([boiler, pv])
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# Add bes to building 1001
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building_1001.addEntity(entity=bes)
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print('Does building 1001 has a building energy system (BES)?')
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print(building_1001.hasBes)
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# Access boiler nominal thermal power
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print('Nominal thermal power of boiler in kW:')
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print(building_1001.bes.boiler.qNominal / 1000)
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if __name__ == '__main__':
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# Run program
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run_test()

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