Commit 072a65a3 authored by Sebastian Schwarz's avatar Sebastian Schwarz
Browse files

Edited example_24_mpi_scheduling_city_district.py.

parent 645e7e3a
Pipeline #512754 passed with stages
in 13 minutes
......@@ -33,7 +33,7 @@ from pycity_scheduling.util import mpi_interface
# In this example, the power schedule for a city district scenario is determined using the parallel MPI Exchange
# ADMM. The scenario is built upon the district setup as defined in example 'example_13_district_generator.py'.
# ADMM. The scenario is built upon the district setup code as defined in example 'example_13_district_generator.py'.
def main(do_plot=False):
......@@ -46,8 +46,8 @@ def main(do_plot=False):
# automatically encapsulates time, weather, and price data/information.
env = factory.generate_standard_environment(initial_date=(2018, 12, 6), step_size=900, op_horizon=96)
# Create 20 single-family houses:
num_sfh = 10
# Create 5 single-family houses:
num_sfh = 5
# 50% SFH.2002, 30% SFH.2010, 20% SFH.2016 (based on TABULA):
sfh_distribution = {
......@@ -140,7 +140,8 @@ def main(do_plot=False):
# Conclusions:
# The parallel and distributed power scheduling for a city district scenario can be done easily using pycity_scheduling
# with MPI support and the Python module mpi4py installed.
# with MPI support and the Python module mpi4py installed. It is already recommended to make use of MPI for distributed
# city district optimizations in which one considers more than 20 buildings.
if __name__ == '__main__':
......
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