Python version of Richardson tool.
The Richardson tool is able to generate stochastic occupancy and electric load profiles for residential apartments.
Original version published under GNU GENERAL PUBLIC LICENSE by Ian Richardson, Murray Thomson and David Infield CREST (Centre for Renewable Energy Systems Technology), Department of Electronic and Electrical Engineering, Loughborough University, Leicestershire LE11 3TU, UK and Department of Electronic & Electrical Engineering, University of Strathclyde, UK Tel. +44 1509 635326. Email address: [email protected]
see:
https://dspace.lboro.ac.uk/dspace-jspui/handle/2134/3112
and
https://dspace.lboro.ac.uk/dspace-jspui/handle/2134/5786
Python version provided by: Institute for Energy Efficient Buildings and Indoor Climate, E.ON Energy Research Center, RWTH Aachen University
Installation is possible via pip:
'pip install richardsonpy' (for static installation into your current Python distribution)
or
clone development version via git and install via pip (egglink): 'pip install -e <your_path_to_richardsonpy>'
richardsonpy requires the following Python packages:
- numpy
- matplotlib
- xlrd
Example code on how to generate a stochastic user profile (profile of active occupancy; 600 seconds resolution)
import numpy as np
import richardsonpy.classes.occupancy as occ
# Total number of occupants within apartment
number_occupants = 3
# Generate occupancy object instance
occupancy_object = occ.Occupancy(number_occupants=number_occupants)
# Pointer to occupancy profile
occupancy_profile = occupancy_object.occupancyExample code on how to generate stochastic electric load profile (60 seconds resolution)
import richardsonpy.classes.occupancy as occ
import richardsonpy.functions.change_resolution as cr
import richardsonpy.functions.load_radiation as loadrad
import richardsonpy.classes.electric_load as eload
def example_stoch_el_load():
# Total number of occupants in apartment
nb_occ = 3
timestep = 60 # in seconds
# Generate occupancy object (necessary as input for electric load gen.)
occ_obj = occ.Occupancy(number_occupants=nb_occ)
# Get radiation (necessary for lighting usage calculation)
(q_direct, q_diffuse) = loadrad.get_rad_from_try_path()
# Convert 3600 s timestep to given timestep
q_direct = cr.change_resolution(q_direct, old_res=3600, new_res=timestep)
q_diffuse = cr.change_resolution(q_diffuse, old_res=3600, new_res=timestep)
# Generate stochastic electric load object instance
el_load_obj = eload.ElectricLoad(occ_profile=occ_obj.occupancy,
total_nb_occ=nb_occ,
q_direct=q_direct,
q_diffuse=q_diffuse,
timestep=timestep)
# Calculate el. energy in kWh by accessing loadcurve attribute
energy_el_kwh = sum(el_load_obj.loadcurve) * timestep / (3600 * 1000)
print('Electric energy demand in kWh: ')
print(energy_el_kwh)
if __name__ == '__main__':
example_stoch_el_load()The appliance data, light bulb configurations, transition probability matrices, activity statistics, and weather data can be found in ...\richardsonpy\inputs...
In case you want to use own customized data sets, for instance for appliances: Copy and modify Appliances.csv and provide the new path for the ElectricLoad object, e.g.:
# Generate stochastic electric load object instance
el_load_obj = eload.ElectricLoad(occ_profile=occ_obj.occupancy,
total_nb_occ=nb_occ,
q_direct=q_direct,
q_diffuse=q_diffuse,
path_app=your_path_to_app_csv)Furter input parameters for the constructor of the ElectricLoad object instance are:
def __init__(self, occ_profile, total_nb_occ, q_direct, q_diffuse,
annual_demand=None, is_sfh=True,
path_app=None, path_light=None, randomize_appliances=True,
prev_heat_dev=False, light_config=0, timestep=60,
initial_day=1,
season_light_mod=False,
light_mod_fac=0.25, do_normalization=False, calc_profile=True,
save_app_light=False):
"""
Constructor of ElectricLoad class
Parameters
----------
occ_profile : array-like
Occupancy profile given at 10-minute intervals for a full year
total_nb_occ : int
Maximum possible number of occupants (does not necessarily need to
be equal to max(occ_profile), as there is no guarantee, that
maximum number of persons is reached
q_direct : array-like
Direct radiation in kW/m2
q_diffuse : array-like
Diffuse radiation in kW/m2
annual_demand : float, optional
Annual electric energy demand in kWh
is_sfh : bool, optional
Defines, if building type is of type single family house
(default: True). If False, assumes multi-family house.
path_app : str, optional
Path to appliance input data set (default: None). If None, uses
...\richardsonpy\richardsonpy\inputs\Appliances.csv
path_light : str, optional
Path to lighting input data set (default: None). If None, uses
...\richardsonpy\richardsonpy\inputs\LightBulbs.csv
randomize_appliances : bool, optional
Defines, if random set of appliance should be selected
(default: True). If False, always uses defined appliances in
...\richardsonpy\richardsonpy\inputs\Appliances.csv
prev_heat_dev : bool, optional
Enables prevention of electric heating devices and hot water
devices (default: False). If True, devices for space heating and
hot water are not allowed to be installed.
light_config : int, optional
Number of lighting configuration (default: 0)
timestep : int, optional
Timestep for profile rescaling (default: 60). Profile is
originally generated with 60 seconds timestep. If another
timestep is given, profile resolution is changed to given
timestep.
initial_day : int, optional
Defines number for initial weekday (default: 1).
1-5 correspond to Monday-Friday, 6-7 to Saturday and
Sunday
season_light_mod : bool, optional
Defines, if sinus-wave should be used to modify electric load
profile to account for seasonal influence, mainly lighting
differences in summer and winter month (default: False)
light_mod_fac : float optional
Modification factor for season_light_mod == True (default: 0.25)
do_normalization : bool optional
Defines, if profile should be normalized to given annual electric
reference demand value in kWh (default: False)
calc_profile : bool, optional
Defines, if profile should be generated (default: True).
save_app_light : bool, optional
Defines, if separate electric profiles for appliance and lighting
should be saved (default: False). If False, only saves summed up
electric load profiles.
Returns
-------
loadcurve : array-like
Electric power load curve in W
"""[1] I. Richardson, M. Thomson, D. Infield, A high-resolution domestic building occupancy model for energy demand simulations, Energy and Buildings 40 (8) (2008) 1560 1566.
[2] I. Richardson, M. Thomson, D. Infield, A. Delahunty, Domestic lighting: A high-resolution energy demand model, Energy and Buildings 41 (7) (2009) 781 789.
[3] I. Richardson, M. Thomson, D. Infield, C. Clifford, Domestic electricity use: A high-resolution energy demand model, Energy and Buildings 42 (10) (2010) 1878 1887.
richardsonpy is released by RWTH Aachen University's Institute for Energy Efficient Buildings and Indoor Climate (EBC) under the GNU GENERAL PUBLIC LICENSE
Grateful acknowledgement is made for financial support by Federal Ministry for Economic Affairs and Energy (BMWi), promotional references 03ET1138D.
)
Moreover, we would like to thank Ian Richardson, Murray Thomson and David Infield for providing the basic tool version as open-source tool.