{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "We start by importing the necessary python libraries that we are going to use." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\lib\\site-packages\\deap\\tools\\_hypervolume\\pyhv.py:33: ImportWarning:\n", "\n", "Falling back to the python version of hypervolume module. Expect this to be very slow.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "cea.api: loading cea_script: \n", "DEBUG:matplotlib.backends:backend module://ipykernel.pylab.backend_inline version unknown\n" ] } ], "source": [ "# import python libraries\n", "from __future__ import division, print_function\n", "import numpy as np\n", "import pandas as pd\n", "import os\n", "import shutil\n", "from matplotlib import pyplot as plt\n", "\n", "# import cea-specific libraries\n", "import cea.api # this is the API to call scripts in CEA, such as the data-helper, radiation engine, demand,...\n", "import cea.inputlocator # this module provides paths to input and output files according to the CEA folder structure\n", "import cea.config # this module lets us interact with the CEA configuration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a next step, we are going to set up our configuration to the desired case study. I am going to use the \"reference-case-cooling\" that ships with the CEA source code. Because CEA provides a script to extract the different reference cases, we can call this script via the API directly in jupyter notebooks. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "City Energy Analyst version 2.19\n", "Running `cea extract-reference-case` with the following parameters:\n", "- extract-reference-case:destination = C:\\Users\\Gabriel\\Documents\\GitHub\\blog\n", " (default: C:\\Users\\Gabriel\\Documents\\GitHub)\n", "- extract-reference-case:case = cooling\n", " (default: open)\n", "\n", "---------------------------------------\n", "Script completed. Execution time: 0.06s\n" ] } ], "source": [ "# create the path to the destination folder\n", "path_to_folder_for_blog = os.path.expandvars(r'${userprofile}/Documents/GitHub/blog')\n", "# extract the reference-case-cooling to the destination folder\n", "cea.api.extract_reference_case(destination=path_to_folder_for_blog, case='cooling')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After I have extracted my case study, I have to make sure that the configuration for the other scripts points to the right folder. Therefore I am setting the project path and scenario name to the correct values. Setting the configuration can be done by manipulating the cea.config file with a text editor or also directly in jupyter via the `config.Configuration` object.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C:\\Users\\Gabriel\\Documents\\GitHub\\blog\\reference-case-cooling\\baseline\n" ] } ], "source": [ "path_to_project = os.path.join(path_to_folder_for_blog, 'reference-case-cooling') # construct the path to the project\n", "\n", "config = cea.config.Configuration() # load the configuration\n", "config.project = path_to_project # set the path to the project\n", "config.scenario_name = 'baseline' # set the scenario name\n", "print(config.scenario) # print the scenario to check the changes\n", "config.save() # save the changes to the cea.config file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we are ready for a run-through of the usual simulation steps for the district energy demand. They are:\n", "- Running the data-helper to set up our building model parameters\n", "- Running the radiation engine to quantify the solar heat gains of the buildings\n", "- Running the energy demand simulation\n", "\n", "The input into all scripts called via the CEA API is the configuration object. On my laptop, it took around 10 minutes to run the scripts." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "City Energy Analyst version 2.19\n", "Running `cea data-helper` with the following parameters:\n", "- general:scenario = C:\\Users\\Gabriel\\Documents\\GitHub\\blog\\reference-case-cooling\\baseline\n", " (default: {general:project}\\{general:scenario-name})\n", "- data-helper:region = SG\n", " (default: CH)\n", "- data-helper:databases = ['comfort', 'architecture', 'HVAC', 'internal-loads', 'supply', 'restrictions', 'technology']\n", " (default: ['comfort', 'architecture', 'HVAC', 'internal-loads', 'supply', 'restrictions', 'technology'])\n", "Running data-helper with scenario = C:\\Users\\Gabriel\\Documents\\GitHub\\blog\\reference-case-cooling\\baseline\n", "Running data-helper with archetypes = ['comfort', 'architecture', 'HVAC', 'internal-loads', 'supply', 'restrictions', 'technology']\n", "\n", "---------------------------------------\n", "Script completed. Execution time: 4.37s\n", "City Energy Analyst version 2.19\n", "Running `cea radiation-daysim` with the following parameters:\n", "- general:scenario = C:\\Users\\Gabriel\\Documents\\GitHub\\blog\\reference-case-cooling\\baseline\n", " (default: {general:project}\\{general:scenario-name})\n", "- general:multiprocessing = True\n", " (default: True)\n", "- general:debug = False\n", " (default: False)\n", "- general:weather = c:\\users\\gabriel\\documents\\cityenergyanalyst\\cityenergyanalyst\\cea\\databases\\weather\\Singapore.epw\n", " (default: c:\\users\\gabriel\\documents\\cityenergyanalyst\\cityenergyanalyst\\cea\\databases\\weather\\Zug.epw)\n", "- radiation-daysim:buildings = []\n", " (default: [])\n", "- radiation-daysim:n-buildings-in-chunk = 100\n", " (default: 100)\n", "- radiation-daysim:roof-grid = 10\n", " (default: 10)\n", "- radiation-daysim:walls-grid = 200\n", " (default: 200)\n", "- radiation-daysim:zone-geometry = 2\n", " (default: 2)\n", "- radiation-daysim:surrounding-geometry = 5\n", " (default: 5)\n", "- radiation-daysim:consider-floors = True\n", " (default: True)\n", "- radiation-daysim:rad-ab = 4\n", " (default: 4)\n", "- radiation-daysim:rad-ad = 512\n", " (default: 512)\n", "- radiation-daysim:rad-as = 32\n", " (default: 32)\n", "- radiation-daysim:rad-ar = 20\n", " (default: 20)\n", "- radiation-daysim:rad-aa = 0.15\n", " (default: 0.15)\n", "- radiation-daysim:rad-lr = 8\n", " (default: 8)\n", "- radiation-daysim:rad-st = 0.5\n", " (default: 0.5)\n", "- radiation-daysim:rad-sj = 0.7\n", " (default: 0.7)\n", "- radiation-daysim:rad-lw = 0.05\n", " (default: 0.05)\n", "- radiation-daysim:rad-dj = 0.7\n", " (default: 0.7)\n", "- radiation-daysim:rad-ds = 0.0\n", " (default: 0.0)\n", "- radiation-daysim:rad-dr = 0\n", " (default: 0)\n", "- radiation-daysim:rad-dp = 32\n", " (default: 32)\n", "- radiation-daysim:albedo = 0.2\n", " (default: 0.2)\n", "- radiation-daysim:daysim-bin-directory = C:\\Daysim\\bin\n", " (default: C:\\Daysim\\bin)\n", "verifying geometry files\n", "C:\\Users\\Gabriel\\Documents\\GitHub\\blog\\reference-case-cooling\\baseline\\inputs\\building-geometry\\zone.shp\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Projection not found (cogr_crs was NULL)\n", "DEBUG:Fiona:Projection not found (cogr_crs was NULL)\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "creating 3D geometry and surfaces\n", "Reading terrain geometry\n", "Creating 3D building surfaces\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Projection not found (cogr_crs was NULL)\n", "DEBUG:Fiona:Projection not found (cogr_crs was NULL)\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "Generating geometry for building B011\n", "burning building B011\n", "Generating geometry for building B012\n", "burning building B012\n", "Generating geometry for building B013\n", "burning building B013\n", "Generating geometry for building B014\n", "burning building B014\n", "Generating geometry for building B015\n", "burning building B015\n", "Generating geometry for building B016\n", "burning building B016\n", "Generating geometry for building B017\n", "burning building B017\n", "Generating geometry for building B018\n", "burning building B018\n", "Generating geometry for building B019\n", "burning building B019\n", "Generating geometry for building B001\n", "burning building B001\n", "Generating geometry for building B002\n", "burning building B002\n", "Generating geometry for building B003\n", "burning building B003\n", "Generating geometry for building B004\n", "burning building B004\n", "Generating geometry for building B005\n", "burning building B005\n", "Generating geometry for building B006\n", "burning building B006\n", "Generating geometry for building B007\n", "burning building B007\n", "Generating geometry for building B008\n", "burning building B008\n", "Generating geometry for building B009\n", "burning building B009\n", "Generating geometry for building B010\n", "burning building B010\n", "Sending the scene: geometry and materials to daysim\n", "\tradiation_main: rad.base_file_path: c:\\users\\gabriel\\appdata\\local\\temp\\default_materials.rad\n", "\tradiation_main: rad.data_folder_path: c:\\users\\gabriel\\appdata\\local\\temp\n", "\tradiation_main: rad.command_file: c:\\users\\gabriel\\appdata\\local\\temp\\command.txt\n", "\tradiation_main: rad.rad_file_path: c:\\users\\gabriel\\appdata\\local\\temp\\geometry.rad\n", "isolation_daysim: daysim_dir=c:\\users\\gabriel\\appdata\\local\\temp\\temp0\n", "\tisolation_daysim: rad.hea_file: c:\\users\\gabriel\\appdata\\local\\temp\\temp0\\temp0.hea\n", "\tisolation_daysim: rad.hea_filename: None\n", "\tisolation_daysim: rad.daysimdir_ies: c:\\users\\gabriel\\appdata\\local\\temp\\temp0\\ies\n", "\tisolation_daysim: rad.daysimdir_pts: c:\\users\\gabriel\\appdata\\local\\temp\\temp0\\pts\n", "\tisolation_daysim: rad.daysimdir_rad: c:\\users\\gabriel\\appdata\\local\\temp\\temp0\\rad\n", "\tisolation_daysim: rad.daysimdir_res: c:\\users\\gabriel\\appdata\\local\\temp\\temp0\\res\n", "\tisolation_daysim: rad.daysimdir_tmp: c:\\users\\gabriel\\appdata\\local\\temp\\temp0\\tmp\n", "\tisolation_daysim: rad.daysimdir_wea: c:\\users\\gabriel\\appdata\\local\\temp\\temp0\\wea\n", "Calculating and sending sensor points\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tisolation_daysim: rad.sensor_file_path: c:\\users\\gabriel\\appdata\\local\\temp\\points_0.pts\n", "Starting Daysim simulation starts for buildings [u'B001', u'B002', u'B003', u'B004', u'B005', u'B006', u'B007', u'B008', u'B009', u'B010']\n", "Total number of sensors: 8073\n", "Executing epw2wea\n", "Executing radfiles2daysim\n", "Writing radiance parameters\n", "Executing gen_dc\n", "Executing ds_illum\n", "Evaluating illumination per sensor\n", "Removing results folder\n", "Writing results to disk\n", "Daysim simulation finished in 9.53 mins\n", "\n", "-----------------------------------------\n", "Script completed. Execution time: 580.83s\n", "City Energy Analyst version 2.19\n", "Running `cea demand` with the following parameters:\n", "- general:scenario = C:\\Users\\Gabriel\\Documents\\GitHub\\blog\\reference-case-cooling\\baseline\n", " (default: {general:project}\\{general:scenario-name})\n", "- general:weather = c:\\users\\gabriel\\documents\\cityenergyanalyst\\cityenergyanalyst\\cea\\databases\\weather\\Singapore.epw\n", " (default: c:\\users\\gabriel\\documents\\cityenergyanalyst\\cityenergyanalyst\\cea\\databases\\weather\\Zug.epw)\n", "- general:multiprocessing = True\n", " (default: True)\n", "- general:number-of-cpus-to-keep-free = 1\n", " (default: 1)\n", "- demand:buildings = []\n", " (default: [])\n", "- demand:loads-output = []\n", " (default: [])\n", "- demand:massflows-output = []\n", " (default: [])\n", "- demand:temperatures-output = []\n", " (default: [])\n", "- demand:resolution-output = hourly\n", " (default: hourly)\n", "- demand:format-output = csv\n", " (default: csv)\n", "- demand:use-dynamic-infiltration-calculation = False\n", " (default: False)\n", "- demand:use-stochastic-occupancy = False\n", " (default: False)\n", "- demand:override-variables = False\n", " (default: False)\n", "- demand:write-detailed-output = False\n", " (default: False)\n", "- demand:predefined-hourly-setpoints = False\n", " (default: False)\n", "- general:debug = False\n", " (default: False)\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "Running demand calculation for scenario C:\\Users\\Gabriel\\Documents\\GitHub\\blog\\reference-case-cooling\\baseline\n", "Running demand calculation with weather file c:\\users\\gabriel\\documents\\cityenergyanalyst\\cityenergyanalyst\\cea\\databases\\weather\\Singapore.epw\n", "Running demand calculation with dynamic infiltration=False\n", "Running demand calculation with multiprocessing=True\n", "Running demand calculation with stochastic occupancy=False\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "read input files\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "done\n", "Running demand calculation for all buildings in the zone\n", "Using 3 CPU's\n", "Building No. 1 completed out of 10\n", "Building No. 2 completed out of 10\n", "Building No. 3 completed out of 10\n", "Building No. 4 completed out of 10\n", "Building No. 5 completed out of 10\n", "Building No. 6 completed out of 10\n", "Building No. 7 completed out of 10\n", "Building No. 8 completed out of 10\n", "Building No. 9 completed out of 10\n", "Building No. 10 completed out of 10\n", "done - time elapsed: 88.2f seconds\n", "\n", "----------------------------------------\n", "Script completed. Execution time: 88.53s\n" ] } ], "source": [ "cea.api.data_helper(region='SG') # run the data-helper script\n", "cea.api.radiation_daysim(weather='Singapore') # run the radiation engine\n", "cea.api.demand(weather='Singapore') # run the building energy demand simulation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we will use some python functionalities to create a new scenario by copying the files of the baseline scenario, modify some building occupancy schedules, and comparing the simulated energy demand results." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "new_scenario_name = 'modified-occupancy-schedules' # the new scenario name\n", "path_to_new_scenario = os.path.join(path_to_project, new_scenario_name) # create the destination path for copying the baseline scenario\n", "\n", "path_to_baseline = os.path.join(path_to_project, 'baseline')\n", "\n", "shutil.copytree(path_to_baseline, path_to_new_scenario) # copy the all files from the baseline to the new scenario" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Part of the CEA building-properties inputs are the various schedules that determine the occupant presence, ventilation rate, electrical appliance use, etc.\n", "These schedules are created based on the region-specific archetypes database during a demand simulation and saved to a CSV file if they are not provided by the user. \n", "\n", "With the help of the CEA `inputlocator` we are going to read these building schedule files (they have been created during the baseline simulation and copied with the above code) and visualize them.\n", "\n", "The various columns contain information about:\n", "\n", "- `Ea` : Electrical appliance use\n", "- `Ed` : Electrical energy demand of data centers\n", "- `El` : Electrical lighting use\n", "- `Epro` : Electrical process energy use\n", "- `Qhpro` : Thermal process energy use\n", "- `Qs` : Sensible heat gains from occupants\n", "- `Vw` : Freshwater use\n", "- `Vww` : Hot water use\n", "- `X` : Latent heat gains (humidity gains) from occupants\n", "- `people` : Occupant presence\n", "- `ve` : Required ventilation rate due to occupant presence" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n" ] }, { "data": { "text/html": [ "
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921.0 921.0 \n", "8 71.04620 409.0 409.0 409.0 \n", "9 56.83696 256.0 256.0 256.0 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inputlocator = cea.inputlocator.InputLocator(scenario=path_to_new_scenario) # the input for the inputlocator is the path to the scenario\n", "buildings = inputlocator.get_zone_building_names() # get all building names in scenario\n", "\n", "building_schedule_path = inputlocator.get_building_schedules(buildings[0]) # the path to the first building schedules file\n", "df_schedules = pd.read_csv(building_schedule_path) # use pandas to read the CSV file into a DataFrame\n", "\n", "df_schedules.head(10) # print the first ten rows of DataFrame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can quickly visualize the data by plotting the first week of some schedules." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ 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\n", 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p5yuBz7gE9wMjIrLK4/6qqEoYryuUwoaIGEJua1qtSNtz1kCj1Qq20Kp74M3wugB68jZbLUTCyRsieqxVAxsG5egxpLwa8FkJHgVeJSLLRWSQJEW0FljpnNsLkH7N4qXVQGd1czQ9tihQ0/YSU0XqCbTY1hS9GDDyupTsQqvlaLmwHi1oOgnJHx5MF6q68tZb5YoeE3nDOjXa60LI56uB3DUG59zjInITcDfwLPAQcLzS/XxP4pinLSI3kKSaWLduXV7xukZFOa/cTBeDUP//lQBeV28t4Msl0n4mvminZgJ5XdoeuEX0CJoRQ7g0HWTRo6buhoseIXNqyqELWvBaCZxztznnLnbOvQo4ADwJPJWliNKvT6enj5JEFBnWAHN7zJJr3uKc2+Sc27RihV5754mQKZZWuqPRcvRURa3z5Gio52lNUknK4XiwnH3W5aO00GbRY+gIR0kf6s1WMFkhya9rpkEbzbCppJqi7mZ/d6joUQte0onImenXdcCvA7cDdwFb0lO2AHemn+8CrpUEm4HxLOW0GJBFDFqLbSN6XXNQrYga91BwDzx9K/TkTQxZOHlTp0Yt2nVUQ+bsRS+6gUQfQqaSKqKou82wuqAF3zmGL4jIcqAOvCdtS/1j4HMicj2wC7g6PfdLJHWI7STtqu/wvLcqZiMGvVxiWK9Lt/jcCNyVpOp1Ba/f6EYMjbaXWJKupOA5e92IIXTxWVN3Q+uCFrwMg3Pu0nmO7Qcum+e4A97jc7+Q0M8rtwJ7XcqGIfDLVVFMfdWDe+DJV+2CY2naa5uho0fdiCF040RFM9pt68IpnEo6lRDG6wr5cul7XSGVtaZYLM/+7tA1hqZSG9XsHEOY5xtiwC1sGlR3Cr4Z+F3TjRjC1se0EA1Diqpyy1/idYUMx/W9rp6StCi2W/4CpZJmPXCd67WHHQNFZOozOE0XNNWhP+AWvnFCfdhxkaeSomFIEWLALbTXpR8xlKNYHnqSeNYD144YyjGQZ6ILitxDJu2qWgNuJZljiIYhhXYqqdkK63VVK3o5ZTDyurTpRoIXc3Wu1059hY5wtAbcmq2g7ZRJ9Kh3vaRxInCxXJESA8KlFbWwuKUzREU7TxuQywdmIwanprCBvS7FYnloD3y2EUHHMtQDtyhm19XstQ8fPWrSo4SmxNCcKk91IaaSygF1SoxW2BpDJq+W5xX+5dIzDNl1QhXLg0UMgYvlmjM4oaNHRbtgYMj00rahdUEL0TCkqCh7XaGnMWe9RKU8uAG7qmaqAwJSYoSqMQSTN72PItFb6OhRM2KotwI3TigO5LV1N6aSyoG2B16adtVMXp3rhW5X1aYuhrBelyZJYXgaa+WIIXgaNKkxaKRBWwY01rUQEUNMJZUDWV5ZM08bNBwX3Yih3moFLpZrRgzhC3gVVX6c0DTWyVfNrq/QugA6XnjdgMa6olhjCF0f00I0DCnUqYsNaKxBJ2KY9boCDwlpD7gF7rXXalEMT2OdRgyqrdZho8fsPr6wiR4rqinb7JqLGYtbOkNozzGEb1fVixhm+VsCE5EpFvYh7GJQFd3UV9B2SvVoN/ywI+ikvmbrN2GjR61hx6ZBhKOBaBhSqPeCG3ldGotXaPZPSF4EzcI+hPW6qopbp9aDcw+Vq9Vak7DSYmCsVtHbS6QeB9zKBf3p0fIsBhZ5T1XqYoPNTjTnLoLTWAfgSgodPYJO6svCqUm20dW5VtNAdzUQDUOKEF5XaQyDkdelTURWlmJ5eBprfXbV0NEj6EYMobmdtFuXT+mNek4ltBk1VYeEwnq0oBUxZNOY5eDgt/C6asoUHjZOgt7iZdNqrVd8Dpq2VUwrlmWjnmgYUlSUW/5CFxyDeF0l4eCvB96oB9KCo2bEENijBcUBt9DDmYrF8mxgLGj0GILOJRafy4Gacstf6IKjZp7WwgPXJSIzKJYrD7gF1QXFLh+wa1fVWGzNokfFNF1yzcW99Pru+fw7IvJ9EXlURG4XkX4ROVtEviUiT4rIHSLSm57bl36/Pf35eo0/QAv6LX8l6uwwyHtqRgwmxXLVAbfy6AKk7aolKZZb6YIa7fapXnwWkdXAfwI2OecuAKrA24CbgI855zYAB4Hr01+5HjjonHsB8LH0vEWDrGtEL2IIO/msyQZrkffUpBUIvVEPpJvJlITGWjN6BBtKDFBqtTZoXVaNGNpO2ClqGFLUgAERqQGDwF7gtcDn059vBd6Sfr4y/Z7055eJyKJ5OiGGhEJ3+ST3KYnXFSBPG7pF8bkaMVgVn3UbJwJHDCVqnNBAbsPgnPsp8L+AXSQGYRx4ABhzzjXS00aB1enn1cDu9Hcb6fnL895fG5rTmGBDYw3KXldIDzxAZ0fQFsWq7txFUN4s7W1pW63g0RjoOjU9oSMG5Wg3pLwa8EklLSOJAs4GzgKGgCvmOTV7ovO9xcc8bRG5QUS2ici2ffv25RWva7QpJjQ7O0rmdcWIYRZVxYjBgsYadGs4Fo0TmjM4ZYkeGymhYuVUjRiAXwZ+4pzb55yrA/8HeAUwkqaWANYAe9LPo8BagPTnw8CBoy/qnLvFObfJObdpxYoVHuJ1h+z/SasX3ILGOruPL6xI6bSIyCy2R9TdirQ8OfuMULEsrdZmhIolMbpa8Pnf3wVsFpHBtFZwGfAYcC/w1vScLcCd6ee70u9Jf/6vTmtfSgWIiOqeAcFprBW9rrqF16XIwd9ohqWxBv3JZ5MuH4Vo14TGWlN3LQgV1YcdF3caCfxqDN8iKSJ/F3gkvdYtwIeAD4jIdpIawm3pr9wGLE+PfwC40UPuIKgqtSia0Fgr5pWza4TsnNHOK4f2urQNQ9DoUfQiBisa6857+aBpQaj4HIwYaic+ZWE45z4CfOSowz8GXjbPuVPA1T73C42qEtGbFY016HpdVukOL6UjPPcQJPLWlZjTQtNYVyqCSJmix+Sran0sNG+WJgX7Im9VhTj5PAdaRG8mNNZBvK6SFMsDF/YhmdTWpAkP3Z6olQcvW8Rg0WpdrQjO6cyJ1Jtho0ctLH4JDVFValG0mcacey8fzHYllSWV1ArudWkOuIWmsQa9rq82ZUPQKfiStVoryht6qlwL0TB0QKtF0YbGWm8DeAtiL9UWxcA5e1BuUQxMYw16EYOVBw46HYBWlBigF+0u9uE2iIZhDqoVne0nbWiss3uVJH2gWSw3SCVpRwyh5dXidppt/7SIHv2vZUWomNyrHI0TGoiGoQPVis6G9e0un8Bspcm9/N+uukFnh3axPHQqqao8d2GR+tIpPtsMO4Ky7hpEu1qGN6TR1cLil9AQWt0HFtOYs3MM/tey2KBcN09r066qFDCYFBy1dNdkYKyqqbs2JHqgVXwOOwWvhWgYOqDVr2xCY10N4HUZ5Gk1IrJGYOZa0I4Ywhccq0rU0Ca60PbANXTXpl0VNCOGaBhKBTXDYEBjrRsxhI9w2l6XUg3HJGJQ2gDeouBYFd2IIWSEo6kLFvWxqmKjR92gcUIDi19CQ2ht4Ve2zo66YYuiVteXxUKrFTGYTGorsddaUGJoElaaECqqNnrEVFLpoEWJYdvZURavS7mzI3QqSZEm3KLgqLURUul0wYDGut3ooZUGjYahXKgqtSiadHZoeuAGBUdN0j+T4rMiTXjdwEusKFFiWOyIpjng1jSgsc5sukpjSqwxlA9alBhWVMCglLO3XAxK0tmhTagYmttJK2Kw4h4CrdZlF3zTG83WcAueLw0sfgkNUVEacLMgItPtlLChsU7uVY7ODrXo0WChBb0BN5P6mHL0aFFvSu7lf61GbFctH2rKA24WHrjO4mUzFwA6hsyis0M7erSgxChN9Kg4BW8VPYJOe62FIdNANAwd0BoSMunsUJ7GDL9w6bX8haaxBj16FIu5ANCkxCgXxYTVVDmg0r5cb7aCEypqYPFLaAitOQYLGuuMg1+FDbYZPk/bZoNV6uwInj7QrjeVhNtpdjizHBQTFoSKMWJ4jqOqVsALH46DIhtsq9UO70NBmw02tNelxcE/W8y10AWF4qhBfUyTYqLRtJkqBz3dPaW7kkTkRSLyYMe/QyLyfhE5XUTuFpEn06/L0vNFRG4Wke0i8rCIXKz3Z+igqtXyZ1RwVON2MuiU0GaDNSs4ej5fCwp20JvUNqFzUW61togeQZHO5VSOGJxzP3DObXTObQReCkwAXyTZy/ke59wG4B5m93a+AtiQ/rsB+ISP4CGgFTFYpQ+0+HEsOiXatAJaBcfQRlepQGox7Ah63E4WdC4iQkX0Wq0tokfQnGNY/IkaLQkvA37knNsJXAlsTY9vBd6Sfr4S+IxLcD8wIiKrlO6vgmpFd99ci32JtZTVygNXIyIza1H0k9di2BEyXfC/jkW7KujWcKwiBrXdB0/liOEovA24Pf280jm3FyD9emZ6fDWwu+N3RtNjcyAiN4jINhHZtm/fPiXxTg61SkVtGhMInrfXI/1zZnlaLcNrVXD01QerdtVEF/QiBosIpyw01qozOM+VHdxEpBd4M/D3Jzp1nmPHPGnn3C3OuU3OuU0rVqzwFa8rVLRptw06UZ6LXpcVjTX48+NYdPmA3iZTFqR0oNc4YTLsqLwt7XOlXfUK4LvOuafS75/KUkTp16fT46PA2o7fWwPsUbi/GmpqeVqbl6siWi2K4XurNVv+LFJftYpO6mtWF8J3JWnSWJvsH1G6dlWlDsDnQsQAXMNsGgngLmBL+nkLcGfH8WvT7qTNwHiWclosSBZa/+vMktKVYzrXai4AylNwrCjJa9ahVtWlxLBJfSk5NVa6qxUxlMAw1Hx+WUQGgdcBv9Vx+I+Bz4nI9cAu4Or0+JeANwLbSTqY3uFz7xDQixhsCo4VxaEmCyMGOi1/NpPaShGDVTFXK3o0oLGGJIIqi1OjpQvNlFCxDBv1eBkG59wEsPyoY/tJupSOPtcB7/G5X2gkNQb/61h5XWoRgwUttGLEYENjreMlWqUVNXUhNI01JB2AWk5Nf0+MHrWx+E2XIWpKnR2zedqStKuWyOsyo7Gu6nmJYKMLatGjQaqjphUxGAxnakW7VvNNGvCKGE41aO/5HPr/X2vArdly9AX2urTytBabCkEnB7/nHIMBKV12fb0ZkfD+Yl6Swnq9zujoKFNTUwB8YNMg1Yrw+OOPa4vYRqPV4tY3r2JZ9SCPP34493VaznHrm1cxPDChJm9/fz9r1qyhp6dH5XoZomHogGanRE9VEAm/eOmwwToGDbpmQCNnbzQwptSi2DTL2WuxwdoMYOU1ZKOjoyxZsoT169cjIlSfOkxfrcLzlw8FkDLBTKOF+9khVi8bYPlQX+7rNJotmnsPcdbIAGeclv86GZxz7N+/n9HRUc4++2zv63UippI6UCvRJDHoTWqb0FgrUUxYds2AhrwljBgMcuB5U19TU1MsX7687XQpvK4nRNu/87xX9utaT1dEWL58eTt60kQ0DB1QG3BrWoXjSmywFu2qSh64JSkd6BkyE0LFkkyVgx8b7NxI3CFqS+0C92vfyQ+ZEdNMJITKSkTD0IGaYm+1idelxgZbHiIyKxrrmpa8VoZMlPYSMZgqh8yQ+V/HOd2F9vg38/31E8cMn/70p3nve9/rdyMFRMPQgYoILZfk7nxg2dmhVSw3S834UkwY7ogG/hvAm+3NUdWLdm3SoDodgA691MxCyAyP99MNEDGEQjQMHdDacrBplEqqKNUYLAyZ1v4GVjTWs7rgd52m1eSzUsRg5dRoscE6A8uQpaocjh07dvDiF7+YLVu2cOGFF/LWt76ViYkJHnjgAV796lfz0pe+lNe//vXs3ZuQOjz44INs3ryZCy+8kKvfehWHxsYQ4DWveQ3vf//7ecUrXsEFF1zAt7/97WPuu2/fPq666iouueQSLrnkEr7xjW+E/UM7EA1DBzIv0beIVzfiQ9Fjgw1fcMy2IlXL2RsNuPlOwlvt+azVONE02i9AK2IAF7z77+giww9+8ANuuOEGHn74YZYuXcpf/uVf8tu//dt8/vOf54EHHuCd73wnH/7whwG49tpruemmm3j44Yc5/4IL+OTHb2pHDEeOHOGb3/wmf/VXf8U73/nOY277vve9j9/5nd/hO9/5Dl/4whd417veFfbv7EBsV+2AWsRmaPLsAAAgAElEQVTQCk9jDXobwFsVHDWmc2dpoQMvtCXbqKdSmd2K1Gdq2bRd1TNk+P1/+D7bdhygVqnQW9N5vuedtZSP/Or5c44dXXxeu3Ytr3zlKwF4+9vfzh/90R/x6KOP8rrXvQ6AZrPJqlWrGB8fZ2xsjFe/+tUA/Obb/z1X/8ZvtCOQa665BoBXvepVHDp0iLGxsTn3/fKXv8xjjz3W/v7QoUMcPnyYJUuWqPytx0M0DB1QK5Aa5Wn1JrVtCo4abLDWEYPWRj2Wk+W9Hvcya1dV2qPaQfgiQ+e9OLYTaMmSJZx//vncd999c46Pj4/P/f2jas9HX+fo71utFvfddx8DAwNecudBNAwd0OPgb9nUGESJ28nQkJWFxlozeuy8Xiho8fnUDWisIYnIphp+sn7kV8/nkZ+Oc8ZpvawaDrd4ikji5afPdteuXdx33338wi/8ArfffjubN2/m1ltvbR+r1+v88Ic/5Pzzz2fZsmX827/9G5deeim3/93fsunlr2zbsTvuuINf+qVf4utf/zrDw8MMDw/Pue/ll1/OX/zFX/DBD34QSOoVGzduDPZ3diLWGDqguWuXhdelFTFYFRw15kTMaKxLNsegxwBqk0rS2kvEufBzDADIbMRw7rnnsnXrVi688EIOHDjQri986EMf4qKLLmLjxo1885vfBGDr1q188IMf5MILL+Thhx7it97/n9vyLlu2jFe84hW8+93v5rbbbjvmljfffDPbtm3jwgsv5LzzzuOTn/xk+L8zRYwYOqC1GNSbhp0danMXNjWGshSf1QyD0f7feqkvq1Zr/+gxayu3aP8UaFuGSqVyzCK9ceNGvva1rx3zexs3buT+++8H4PBUnZ88c6Qt71VXXcVHP/rROedfd911XHfddQCcccYZ3HHHHZp/xkkjRgwd0Ewf2HV2lKlF0b+Lymqh1Rpwa6Y01lY1Bh3dLYdTo00xcTwICpPPHdda7IgRQwc0C45mxWfPhcuKxhpSbict6mKD9trO++VF3croVnXYYC1orEHJMFgOjKWppPXr1/Poo4/mu0aHZfjKV76iJFgYxIihA5otimY5e8+Ftm64eYjG3IUVjbWmB24ySazGRWXXruqrCydDMaGFzuJzXmTymtREPOFlGERkREQ+LyJPiMjjIvILInK6iNwtIk+mX5el54qI3Cwi20XkYRG5WOdP0MPsUJOC12WVs9eaJDYxZOWhsVbThaYLLitopr4WfyqpTVlTQMTggxARji99z0Lw1dg/A/7ZOfdi4CLgceBG4B7n3AbgnvR7gCuADem/G4BPeN5bHVkIrbGFX1m6fOpGW0+CDreTFY11tjj6z1202pTjIdFOfflGkE2bVuu8hqG/v5/9+/fjnGW8MLf4nBfa8mb7MfT39ytdcRa5awwishR4FXAdgHNuBpgRkSuB16SnbQW+AnwIuBL4TLr38/1ptLHKObc3t/TKyJx834nMhmFnR1n67CHZ0U6rKyn0QJ7exkI2OXvNiMEq9ZVHF9asWcPo6Cj79u2j2XI8NT7FzDM9PN0Xtlz6s/EpDtQqHH6qN/c1JmYaHDhSR8b71HQi28FNGz5P8xxgH/DXInIR8ADwPmBlttg75/aKyJnp+auB3R2/P5oem2MYROQGkoiCdevWeYjXPapqEYNNOF4RhYExIxpr0IkYst+3YoP11gWjnL0mG6xJvSknG2xPT097t7LdByZ409/cy/+6+iLeeq7+4tiJd//Pe3nJ2hE+/rZzc1/js9/exY13PcI3b3wtZ43YTzN3A5/VoAZcDHzCOfcS4AizaaP5MJ+2HaMZzrlbnHObnHObVqxY4SFe92hHDCoFRxsv0TvVYUTyBjrcTnWjdtXMMHhHj0YeuBYbrFW0W1Fgg83oRkyK5SpOmN275guft2sUGHXOfSv9/vMkhuIpEVkFkH59uuP8tR2/vwbY43F/dcxuAO/LqBl+q0zQ2c7RiuQNdCa1zWisFQfcrHiowJ8N1rJxQk93F3exPMMsAeTibwbNLaFz7mfAbhF5UXroMuAx4C5gS3psC3Bn+vku4Nq0O2kzML6Y6gvQ2fLndx2zPG3FfwN4S6+rosDBb0VjrUmPYhkx+DKkWDZO+Ea7VroASobBKA2qAd+KzW8DfysivcCPgXeQGJvPicj1wC7g6vTcLwFvBLYDE+m5iwrt9EFJvC7diKEc3E5WEY4eV1Ir+LapoKe7VptMqUYMi7iLqhPZ71tEkL7wMgzOuQeBTfP86LJ5znXAe3zuFxqzLYp+17EcEvLl4C+b12VHY600SWy4VSZosKsa7VeuOexo5dQoNKVAOSKGxZ/sMoRuntZw2tVDYWe7fAy8LoWCo5XXlb27ZRl21CqW26VB9SjNLQYIVZiBjYYzNbD4JTRETcnrsursqCpQeFjRWGf3KEueVkSSGo5CKskqGgOdORGbxomkddlncrdhOpypQxkvgtcOe1aIhqEDul6XjQee3S8vLFvoNFoUrdhVQalF0TiV5Bs9OmcXPQL4PF5Lp0ZnZsjGYdRANAwdUBtqMtoqc7bg6O91WRUcy0JjDTpdX1b7f6vognH0COVxampV/+ixaTQFr4FySGkEjZer1XK0nN3Cld0zL0y9ror/BvBWNNags2G91VaZbQ9cxUmwm7vwMgyGTo1GxFA3akrRQDQMHdDI085y+djx4/h5ibZ5Wg0P3Mrr0okY7IYdoVy6AL6pL0vKeCXdLUGrKkTDMAc6OXubdsrkHv7cTpadEjqUGHZeVzIn4tmhZlRj0GCDzSZzLZwaDTZY61Zr7+ixaRM9aqAcUhpBM2KwUda598yDpqEh0+B2aracSd86ZHMXftewbl3204WyRQx28mpFjzGVVEJoGIamqReTRgwqqa/yEJGZpZJEZ1LbajIXPKNHQ12otFNf+Z+vZdpWg2XAyknQQDQMHdDI2c9OY9rsoQw6XUlWXldZtp4EncXAKvWl0Wo9qwuG+0copL6s0rYardYxYighKgpe1+w0pl3EUJZiuRYRmZXXpTHgZrlVJvilZjLv3Sp6BKW0rYm8SnvBl4BZFaJhmIOaqtdVkpfL1OvSGXCz8ro0iN6sCo6a9THTgTwF3bVJ1flHDLFdtaTQzNOaeoklKZbrDLjZdPlAuQqOOgut7cAYlMeQaW2jWwYCPYiGYQ40esHbvdUl8RItN+qpVMR7s3orGmvQaVE0J1TU0AWjgbHOe+aBJY21Rqu1FaGiBsohpRE0FlrL3mqNlj8rGmtQoi42mgsAnYjBKvWlQRNuTWMNvjWRcg1nWhEqaiAahg6oel2WQ0IeLX/R61oYWhsh2ejC7P3ywprGGnTqeRbyJtGj/7Djc8IwiMgOEXlERB4UkW3psdNF5G4ReTL9uiw9LiJys4hsF5GHReRijT9AEzoRg91WmSrFcmuvqyQ01qC0sZCRvFnE4NVqbRw9gm89z47GOoke/a7xXJtj+CXn3EbnXLaT243APc65DcA96fcAVwAb0n83AJ9QuLcqRISKZ1taO5VkuAG8Tzg+00hbFEtCY11vGJLoedKEN5otnDPK2ae38FloM921HXDz0N1my2zTGw16lKQrqRxJmhBSXglsTT9vBd7ScfwzLsH9wIiIrApwfy/UPLccnKo3AejvqWqJtCA0OjumGk16qxUjr8t/Unuq0TR5tuAfMUylRre/x2JgLI0YPKJHU91VYAaerrfoM3i2kM20+F1jqt400QUN+ErpgH8RkQdE5Ib02Ern3F6A9OuZ6fHVwO6O3x1Njy0qVDy3HGy/XLXwL5dGZ4fty5V89fESLV8ub8NguNBmdl3HqbGjCffXBSMnQfwjhql6y0xeX9Q8f/+Vzrk9InImcLeIPHGcc+dzSY/RitTA3ACwbt06T/G6R81zkGXa1EtUMAymHrg/G+x0w+7lqnp2UVnqQrYVqU8jQiZvn4FTo7GXSKILlhTs4JxDJF90Pd1omjiMGvB6qs65PenXp4EvAi8DnspSROnXp9PTR4G1Hb++BtgzzzVvcc5tcs5tWrFihY94ueBbY7D0EjWK5YkXU7KIwejlKlPEAP5ssNMF6G6ZdAHK8675IreUIjIkIkuyz8DlwKPAXcCW9LQtwJ3p57uAa9PupM3AeJZyWkyoVSteIWOWV7ZIz+goq+XLlfbae+XB7V6umueAW2YYLDxw8GeDnarbRThqumtodKE8qS9f+KSSVgJfTMOqGvB3zrl/FpHvAJ8TkeuBXcDV6flfAt4IbAcmgHd43DsYkg3r8/++pdelMSRkm6dNvpZF3or4DTVZLrSQ0Tbk//0Y7S4MX7oc5xzTjRZ9p7phcM79GLhonuP7gcvmOe6A9+S9nxVqnnla0+Jz2V6uatZrn+/5Wr9ctapfe62lkwAp5YhXtNukWhEzpl3w9MAbTU7r8y2Tnhx8Kfkt600aKIeUhvDN007VW1TEphdco/g81WiapTpmWxTz/b71y1WtVLxba8HOMPhSjkzVW/TVrKIb/0aERF7bVFJefbB0GDUQDcNR8O3syFIdeTsXukFFpeXPMGJoy5vv+Vq/XFXxf7ZgZ8gqCsVyu5x98tU3IrNOJeWVd1YXomEoJRKvK//vTzcMvS6FDeCnG02z1ExVKWKwm7vwbV22LT77UkNPN1r0G+muxrBj8q6VI2KY1YVyLLnlkNIQ3nla46Eb8PW6WuYtf6WJGLyHHY0jBk/KkbLprumwo6e8MWIoOXy9rinjASzwzdPah+N55bV+uapa9ChWEUPVj6Rwqm5X2K8qRLtFtKvmXRssp8o1UA4pDVHxJE6bqjfNwkWNDeDL1Atu/XLpRQyWtA2+U/Dl8MAhc8Js3zV/wxAjhlKiVi1TAU8hYogv14LwpUeZHXCzpG0oy7Cj3wxOvdmi2XKlkdeSUFED5ZDSEL552ukChm7yymv+cnmS/lm/XN7RY6NJb82GuRb8tyItYmCsmbM3vAi6EfB3aqyK5b6IhuEo+G7hZ00LDQrKajbgVq6Xyzd6TAr7dq+YBreTdZdPXjtWxFQ5lCfa9UU0DEehosCPUxoPvIAcOJTn5dKoN1kuBL5ssFOWNQbPbWlnnRo7XYD8ujttbMh8UQ4pDaHRC265eQj4jOkbd80o0QrYTef6cfBb6gL4RwzThvsFlE4XqjrvWkwllRTeXpdhxJBx8Ocf07cdGMty7bmHhArgHso4+PPAUhdAodXamKAQFCgmShIxWKe+fFEOKQ3hn6e15Vz3aVG07/LRGhIqT17ZMpXkPeBmGOH4Rwz2HWpQnjSoL6JhOApl8rrAr0XR+uXybvkrqhPFYyDP0knwGXBrtRwzDbsp+EpFEPGJGFInwXhmKH9HnR1zrQbKIaUhfAqORXCu+7QoFvZy5ZXX+OXSWAzKEjHMMtca6m6Jol3/DkDbDjVflEdSI/i0KBbBue4TMVh3dqh44IYvl38qyY7kDfxara2H8cCvnmdPj+If7ZYljQTRMBwDn4ihCM71qkfnjHXOXmPuwtoDB58WRbv2T/CMHo3TipAahtzRrjU9im97rV3Hlwa8n6qIVEXkeyLyj+n3Z4vIt0TkSRG5Q0R60+N96ffb05+v9713CPhsdlIEg6LPxkLmJG8a4bjhs60pDOSVpd5URNeMV8RQwCZIQP53rdE0bV32hYak7wMe7/j+JuBjzrkNwEHg+vT49cBB59wLgI+l5y06VCsV6o18//uzxVzbdEc9p7Za55UzDv788tq+XJmXWM/p1U4b8lBB0jkz4/Fswdap8dLddn3MNpXkI29Zdm8DT8MgImuAXwH+3/R7AV4LfD49ZSvwlvTzlen3pD+/TCy2OesSg71VJlNPulu05wIMFWDAS15bQzbYmzwXn+dr+Wz95bWjmIBEF6Zm/HTX0pAN9taYnMmZmmnY0rm0dSHn87V2anzhK+nHgf8MZP+7y4Ex51wj/X4UWJ1+Xg3sBkh/Pp6ev6gw2FflSO6Xyz5iGOqtMTHdOPGJ88A6HM9eronp/C+X9cIFcCT387WNGIZ6FXTX2PBOzOR8tnXbyedMF/LLazvs6IvcT1VE3gQ87Zx7oPPwPKe6k/hZ53VvEJFtIrJt3759ecXLjaHeGjONFo0cIWMRQyyDXotBMS/XkZK8XEPtxaD752vNXAsw2FfzWrjArkMNEnnz6u50uu+JVdJhqC95Lj7vWlmmnsEvYngl8GYR2QF8liSF9HFgRERq6TlrgD3p51FgLUD682HgwNEXdc7d4pzb5JzbtGLFCg/x8qHt1eZIHxTBuT7UV8sf3tYTWmirl6taEfp7KrnltX65BtPFIM9iW4STMNRbpd5MBtW6hbWTAIm8kz5OguGz7a9VEcnnJMBzqF3VOfdfnHNrnHPrgbcB/+qc+03gXuCt6WlbgDvTz3el35P+/F9dXhKagGiHjDnSHUVwricRg48HbuvFDPXW/OQ1jsYg32JQVM4e8uXBiyg+D/bWOJIzrWjtJFQqwkBP1Stt+5wwDMfBh4APiMh2khrCbenx24Dl6fEPADcGuLc3ZkPGcniJg73V3Dn7InqrB3zkNX65hjxqDIWkZno1dNfSkHnUGApYaAd786e+ypZKqp34lBPDOfcV4Cvp5x8DL5vnnCngao37hYRPxFAE5/qgjwdewMvlFzEYp5I8IoZCPPC+/AXSImZwhjwbPayLuUN9PsVy2w41X5THhBlhyMPrKmIxSJS1mYsa2nIb0gyDqbx5MG38cg315S+WW/NQQYfu5nFqCkol5U3NWM+IgF/qK5E3GobSQsPrsizgDfbWaLZce1itGxQWMXi0f1r2gvfVKlQkZ/TYKCKVVD5DNlHP59QU4YEP5Ux9Zcy1luuCL8ojqRH8Co7FdKJAvoJjEeF4klfuXlZrWmhINkIa7K35FZ8tF9o+P12oVYSaIS30YF8N52afVTeYqts6CZDWx3KlFe3TdL6IhuEo+AxhFcG5PuiZ7rB+uYb68i20Rb1ceQukxTQiZLqQz5DZR49+xfIiol0/XSjPclseSY0w5BmOW7d/+kY41uH4gPdCa2/I8i60YN+hBuTK2081muapjgGfRo8CcvaDfdVcNYYimGt9EQ3DUZgdasqZminAi4F8LZVFFPCGesv1ciXtwOUwZENeEUMRuusZMRQwg+PX8VWe5bY8khqht1qhVpGcvesFeDGeEUMRveCT9WbXVNZFvVx522uLMGQDHhHDdAFpRb9GjwJ0N2d7bRE8VL6IhuEoJAXHfEWmIjjX2y2VOb1a+9RMPsbSol6uvO21U8a00AC9tQq91Ur+iKGALh/I115bxMBYXh61IupNvoiGYR4M5gwZi+Bc96GGnjbu8oH8LJXFFp992lXt50Ty8A8VNRcA3Ue7yd7qRUS7+XjUMt19LtFun5LIGzIWwbk+GzF0/3IVklfOajhdylvEnsSQfwiriJkWyFJf5UgrDuUkKaw3HS1XjC6Aj+7GiKHUyLvHQRHheDuvnPPlsvYSB3rydX0VwT0E+fc4sKaFzpC766sAD3ygXXzucqEtqBEhL49aLD6fIsi7x0ERec/Bnnx52vYOWAXwzUD36YOiXq68exwURbOcu+urbj+Z297voksnrCgnIW/EUATdiC+iYZgHQyVaDGrVCn21StfyFjUXkHdXtKJerrx7HBTFppm3PlaE7g705IsYpguYKof87bWx+HyKIC81dFGc68kQVpcLbZYDLyyvnLMryTzdka9YXpwu5I8YrA1ZpSK55kSK04V8adsi6FF8UR5JDTGUt121MC+xe3mLernybpdZ1Ms1lHNOpKg9frM5kW5hzVybYbC31nWXTxFT5TDb6FGWd80H0TDMg7x7HBT1ciXF8nwtdMVReJQjlZR3CKuI9k/IIoa87arFRDjdRgyzulCQ7uZ916JhKDfy7nEwVdDLNZBje8+ivJjZGkNJvMScQ1hT9aZ5mg6Srq9uPdpmyzHTLMaQDfR03+hRnC7k76jrqQrVim2Hmg9ya4KI9IvIt0XkIRH5voj8fnr8bBH5log8KSJ3iEhverwv/X57+vP1On+CPvLscdCmhS7IS8zf5WP7cvX3VNJN1cvxcuXd46AIehTIdkVrdOXUFNk1k6fRo8gpeMiXBi0THQb4RQzTwGudcxcBG4E3iMhm4CbgY865DcBB4Pr0/OuBg865FwAfS89blMiTVy6q/ROynaW6e7kmCxoYE5F0s57uXq7JotJ0HgN5RWzMMtjb/R4H2f4NxcjbfbG8rbvGTlheHrXJuv3gqy9yS+sSPJt+25P+c8Brgc+nx7cCb0k/X5l+T/rzy8R6+uckkaelcs/YFADPG+4LItPxkKdYvnd8EoDnDfeHEOm4yLPHwd6xKc5cav9sB3O2KO4Zm+R5S+2fbZ4hrL3jqe4WIW+O9tpMd1cusZVXRHJt1rN3fJKVBTxbH3iZMRGpisiDwNPA3cCPgDHnXPY/PQqsTj+vBnYDpD8fB5b73D8UBnMQve06cASA5y8fCiLT8TCYY/Obnfsn6O+pcOaSAgxZHnkPTLC+iGebOgnd7Io2NjHDoakGz18+GEqsBZFH3p37J4CidLf7hXbn/glGBnsYHuwJJNXCyGPIdu0vRnd94GUYnHNN59xGYA3wMuDc+U5Lv84XHRyTCBWRG0Rkm4hs27dvn494uZFnj4Mdz6Qv1+n2i0GevWh37j/C+uVD5pQN0H3E4Jxj5/4jhSy0efY42FHgQptnCGvH/sypKeb55jEMRTxb6J5HrdFssfvgBOsKeLY+UEl8OefGgK8Am4EREamlP1oD7Ek/jwJrAdKfDwMH5rnWLc65Tc65TStWrNAQr2vk2eNg5/4jLOmrcfpQbyixFkS2L3Griz0Odu6fYF0BRgzousbwzLMzTMw0CzG6efY42JkutOuLiBhykCru2j/BGaf1tfv0LTGYo71254EjhegCdM+jtnd8inrTFaILPvDpSlohIiPp5wHgl4HHgXuBt6anbQHuTD/flX5P+vN/dd32gxohzx4HOw8kXkFRHjicfOqr1XJJauaMYryubonesoX2+QXIm2ePgyw1s7aAxSvPnMiO/UcKW7gGe2pMd7HHwUyjxU8PThYnb5c8apkurDv9uZNKWgXcKyIPA98B7nbO/SPwIeADIrKdpIZwW3r+bcDy9PgHgBs97h0UA7kihuLyiG0v8SQXg6cOTzHTaBUXMXQZjrdz4AXJ260h27H/CKuG+wtp/xzMMXex60BxqY5219dJOjU/HZuk5WBdUe9aDl0AWH9GuSKG3LGjc+5h4CXzHP8xSb3h6ONTwNV572eJbgdZGs0WowcneMMFzwsp1oIY6pzIXHLi87N6SGGGrMtwfOf+I1QE1iwrKn3QXUvlroLTdHDyEcNUvcne8alCdQES3V3af+Ji8o4C03SQNnqkjsrJYNeBCXprFfMOKl+Uq7nWCO2upJP0aovOI3a7E9ZsB1VxC203/Dg7D0xw1sgAvQWRkA321Zisd+MlFhk9dhft7j6QFcoLjhhO0pDtylIzRepuN40IzyT1kEqJpp4hGoZ50e0eB0XnEbt9uXbsn6CnKqwqYIYBUq+rCw+8yIUWuosYjkw3eObZ6QIXru4ihiI7qKB7p2bH/iMM9lZZcZp9mzV0z6O268BEYUbXB9EwzINu9zgost0POmkbTjJi2D/BmmWD1KrF/PcP9VaZabZOeo+DXfuPFNru180eB7MzAQXVQ7p2alLdLSz1lcl78hHDutOLafKA7njUkjbridIVniEahgUx2AUxXZZHLGJyFDpZH0/ekBXpxQx0MYQ1Plnn4ES90Ha/bmgbsjRdURFOpSIM9Jx8gXTn/gmW9tcYKWBYDLpv9NiRzt8UhW541PYdnmay3ixd4RmiYVgQg11QWe945gjrCswjdjOE5Zxj1/6JwjxE6G4Ia9ciaPfrZnvPHQXnwKG7rq+dB5JhseI88JNv9Gi2HLsPTBbq1HQz49TWhQLftbywn2gpCYb6qvzLY0/xuj/96gnP3X1wgl98wRkGUs2PrOB40z8/wf/+6o+Oe64DDk83Csspw2x77TW33k/vCdJZWYqhSK9rqLfK6MHJk9KFfc9Oc/pQ70l12ITCYG+Nf3hoD9/5yTHzo8dg54EJLj9vpYFU8yNbaP/gHx/jz7785HHPbbqEHrxI3c2csKs+8U1qJ3AEn810t2R0GBANw4L4D5eew70/ePqkzt2w8jSuedm6wBItjOVDvbzrF89mT0oudiJccNZSLj+/uMVg8zmn8+svWd1mpD0Rfvm0Pl6w4rTAUi2Mq166hsNTDdyxDC7HYMPK09h8TrEUYL/16nP4xvZnTurcF65cwts3Pz+wRAvjrOEBrnvFep4+PHVS51+0ZoTLzj0zsFQL49IXnsGvvWR1m6r8RLhi6UApIwZZpMPHAGzatMlt27ataDEiIiIiSgURecA5tynv78caQ0RERETEHETDEBERERExB9EwRERERETMQTQMERERERFzEA1DRERERMQcRMMQERERETEH0TBERERERMxBNAwREREREXOwqAfcRGQfsNPjEmcAJzcCunhQRpmhnHKXUWYop9xllBnKKfcZwJBzbkXeCyxqw+ALEdnmM/1XBMooM5RT7jLKDOWUu4wyQznl1pA5ppIiIiIiIuYgGoaIiIiIiDk41Q3DLUULkANllBnKKXcZZYZyyl1GmaGccnvLfErXGCIiIiIiusepHjFERERERHSJU9IwiMgbROQHIrJdRG4sWp75ICJrReReEXlcRL4vIu9Lj/83EfmpiDyY/ntj0bIeDRHZISKPpPJtS4+dLiJ3i8iT6ddlRcvZCRF5UcczfVBEDonI+xfb8xaRT4nI0yLyaMexeZ+tJLg51fOHReTiRSb3/xSRJ1LZvigiI+nx9SIy2fHMP7nI5F5QJ0Tkv6TP+wci8vpFJPMdHfLuEJEH0+P5nrVz7pT6B1SBHwHnAL3AQ8B5Rcs1j5yrgIvTz0uAHw1kiaQAAAO0SURBVALnAf8N+N2i5TuB7DuAM4469j+AG9PPNwI3FS3nCXTkZ8DzF9vzBl4FXAw8eqJnC7wR+CdAgM3AtxaZ3JcDtfTzTR1yr+88bxE+73l1In0/HwL6gLPTdaa6GGQ+6ud/Avyez7M+FSOGlwHbnXM/ds7NAJ8FrixYpmPgnNvrnPtu+vkw8DiwulipvHAlsDX9vBV4S4GynAiXAT9yzvkMTwaBc+5rwNGbNS/0bK8EPuMS3A+MiMgqG0nnYj65nXP/4pxrpN/eD6wxF+wEWOB5L4Qrgc8656adcz8BtpOsN6Y4nswiIsBvALf73ONUNAyrgd0d34+yyBdcEVkPvAT4VnrovWn4/anFlpJJ4YB/EZEHROSG9NhK59xeSIweUNzGvCfG25j74iz2573Qsy2Trr+TJLrJcLaIfE9EvioilxYl1HEwn06U4XlfCjzlnHuy41jXz/pUNAwyz7FF23olIqcBXwDe75w7BHwC+DlgI7CXJCxcbHilc+5i4ArgPSLyqqIFOlmISC/wZuDv00NleN4LoRS6LiIfBhrA36aH9gLrnHMvAT4A/J2ILC1KvnmwkE6U4Xlfw1ynJ9ezPhUNwyiwtuP7NcCegmQ5LkSkh8Qo/K1z7v8AOOeecs41nXMt4FYKCFVPBOfcnvTr08AXSWR8KktjpF+fLk7C4+IK4LvOuaegHM+bhZ/totd1EdkCvAn4TZcmvdNUzP708wMkufoXFiflXBxHJxb18xaRGvDrwB3ZsbzP+lQ0DN8BNojI2al3+DbgroJlOgZpLvA24HHn3J92HO/MEf8a8OjRv1skRGRIRJZkn0kKjI+SPOMt6WlbgDuLkfCEmONRLfbnnWKhZ3sXcG3anbQZGM9STosBIvIG4EPAm51zEx3HV4hINf18DrAB+HExUh6L4+jEXcDbRKRPRM4mkfvb1vIdB78MPOGcG80O5H7W1hV1o6r9G0m6fH4EfLhoeRaQ8RdJwtCHgQfTf28E/gZ4JD1+F7CqaFmPkvscks6Mh4DvZ88XWA7cAzyZfj29aFnnkX0Q2A8MdxxbVM+bxGjtBeokHur1Cz1bktTGX6Z6/giwaZHJvZ0kJ5/p9yfTc69Kdech4LvAry4yuRfUCeDD6fP+AXDFYpE5Pf5p4N1HnZvrWcfJ54iIiIiIOTgVU0kRERERER6IhiEiIiIiYg6iYYiIiIiImINoGCIiIiIi5iAahoiIiIiIOYiGISIiIiJiDqJhiIiIiIiYg2gYIiIiIiLm4P8HUIA4obp9Q58AAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_schedules['Ea'][0:168].plot() # visualize 1 week of electrical appliance schedule\n", "plt.legend()\n", "plt.show()\n", "df_schedules['people'][0:168].plot() # visualize 1 week of occupant presence schedule\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, I am going to introduce some randomness (or stochasticity) to the schedules by multiplying each value with a factor sampled from a normal distribution centered around 1 (`mu = 1`) with a standard distribution of 5% (`sigma = 0.05`).\n", "\n", "Such random factors can be obtained with `sigma * np.random.randn(...) + mu`\n", "\n", "https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.randn.html\n", "\n", "We can test and visualize how this looks like for our schedules." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mu = 1.0\n", "sigma = 0.05\n", "\n", "df_schedules['Ea'][0:168].plot(label='default') # visualize 1 week of electrical appliance schedule\n", "df_schedules['Ea'] = df_schedules['Ea'] * (sigma * np.random.randn(8760) + mu) # randomize the schedule\n", "df_schedules['Ea'][0:168].plot(label='randomized')\n", "plt.legend()\n", "plt.show()\n", "\n", "df_schedules['people'][0:168].plot(label='default') # visualize 1 week of occupant presence schedule\n", "df_schedules['people'] = df_schedules['people'] * (sigma * np.random.randn(8760) + mu) # randomize the schedule\n", "df_schedules['people'][0:168].plot(label='randomized')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, I am simply going to randomize all schedules of all buildings in a nested `for` loop by reading, modifying, and saving, the schedules again.\n", "When I save the schedules from DataFrames back to CSV, I set `index=False` to comply with the default file format. However, the files should also work fine with CEA if they have some additional columns, such as an index. They will just be ignored.\n", "\n", "The location for schedule inputs is `{PROJECT}/{SCENARIO}/inputs/building-properties` the file name is `{BUILDING}_schedules.csv`." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n" ] } ], "source": [ "buildings = inputlocator.get_zone_building_names() # get all building names in scenario\n", "input_folder_path = inputlocator.get_building_properties_folder() # get the path to the input/building_properties \n", "\n", "for building in buildings:\n", " \n", " building_schedule_path = inputlocator.get_building_schedules(building) # the path to the building schedules file\n", " df_schedules = pd.read_csv(building_schedule_path) # use pandas to read the CSV file into a DataFrame\n", " \n", " for schedule in df_schedules.keys(): # iterate over all columns\n", " \n", " df_schedules[schedule] = df_schedules[schedule] * (sigma * np.random.randn(8760) + mu) # randomize the schedule\n", " \n", " building_schedule_input_path = os.path.join(input_folder_path,'{}_schedules.csv'.format(building))\n", " df_schedules.to_csv(building_schedule_input_path, index=False) # save the schedules after modification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because we do not want to change other building properties and because the schedules do not have an impact on the solar heat gains, I can now already run the energy demand simulation for my modified case study.\n", "\n", "\n", "The tool will display a message like `Schedules for building B002 detected. Using these schedules.` in the console when you run the demand script." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "City Energy Analyst version 2.19\n", "Running `cea demand` with the following parameters:\n", "- general:scenario = C:\\Users\\Gabriel\\Documents\\GitHub\\blog\\reference-case-cooling\\modified-occupancy-schedules\n", " (default: {general:project}\\{general:scenario-name})\n", "- general:weather = c:\\users\\gabriel\\documents\\cityenergyanalyst\\cityenergyanalyst\\cea\\databases\\weather\\Singapore.epw\n", " (default: c:\\users\\gabriel\\documents\\cityenergyanalyst\\cityenergyanalyst\\cea\\databases\\weather\\Zug.epw)\n", "- general:multiprocessing = True\n", " (default: True)\n", "- general:number-of-cpus-to-keep-free = 1\n", " (default: 1)\n", "- demand:buildings = []\n", " (default: [])\n", "- demand:loads-output = []\n", " (default: [])\n", "- demand:massflows-output = []\n", " (default: [])\n", "- demand:temperatures-output = []\n", " (default: [])\n", "- demand:resolution-output = hourly\n", " (default: hourly)\n", "- demand:format-output = csv\n", " (default: csv)\n", "- demand:use-dynamic-infiltration-calculation = False\n", " (default: False)\n", "- demand:use-stochastic-occupancy = False\n", " (default: False)\n", "- demand:override-variables = False\n", " (default: False)\n", "- demand:write-detailed-output = False\n", " (default: False)\n", "- demand:predefined-hourly-setpoints = False\n", " (default: False)\n", "- general:debug = False\n", " (default: False)\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "Running demand calculation for scenario C:\\Users\\Gabriel\\Documents\\GitHub\\blog\\reference-case-cooling\\modified-occupancy-schedules\n", "Running demand calculation with weather file c:\\users\\gabriel\\documents\\cityenergyanalyst\\cityenergyanalyst\\cea\\databases\\weather\\Singapore.epw\n", "Running demand calculation with dynamic infiltration=False\n", "Running demand calculation with multiprocessing=True\n", "Running demand calculation with stochastic occupancy=False\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "read input files\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "DEBUG:fiona:Creating a not-responsible GDALEnv in drivers()\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:GDAL_DATA: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\\gdal\n", "DEBUG:Fiona:PROJ_LIB: C:\\Users\\Gabriel\\Documents\\CityEnergyAnalyst\\Dependencies\\Python\\Library\\share\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Got coordinate system\n", "DEBUG:Fiona:Index: 0\n", "DEBUG:fiona.collection:Flushed buffer\n", "DEBUG:fiona.collection:Stopped session\n", "done\n", "Running demand calculation for all buildings in the zone\n", "Using 3 CPU's\n", "Building No. 1 completed out of 10\n", "Building No. 2 completed out of 10\n", "Building No. 3 completed out of 10\n", "Building No. 4 completed out of 10\n", "Building No. 5 completed out of 10\n", "Building No. 6 completed out of 10\n", "Building No. 7 completed out of 10\n", "Building No. 8 completed out of 10\n", "Building No. 9 completed out of 10\n", "Building No. 10 completed out of 10\n", "done - time elapsed: 82.2f seconds\n", "\n", "----------------------------------------\n", "Script completed. Execution time: 82.62s\n" ] } ], "source": [ "cea.api.demand(scenario=path_to_new_scenario, weather='Singapore') # run the demand simulation of the new scenario" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's compare the district energy demand results of our two case studies.\n", "For this simple example, I'm just going to look at the `Total_demand.csv` file that compiles a bunch of data for the district from the individual building demand output files.\n", "\n", "An interesting analysis would, e.g., be to look at the differences in cumulative annual energy demands and peak power demands. We do not expect to see differences in annual energy demand, because the stochasticity factor introduced is normally distributed around 1 and deviations towards higher and lower energy consumption should balance out. However, we do expect to see some deviations in peak loads, but how much?\n", "\n", "The columns I'm going to look at are:\n", "\n", "- `QC_sys_MWhyr` the total annual cooling demand\n", "- `QC_sys0_kW` the peak cooling demand" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# get the new total demand file\n", "inputlocator_new = cea.inputlocator.InputLocator(scenario=path_to_new_scenario)\n", "total_demand_path_random = inputlocator_new.get_total_demand()\n", "df_total_demand_random = pd.read_csv(total_demand_path_random, index_col='Name')\n", "\n", "# get the baseline total demand file\n", "inputlocator_baseline = cea.inputlocator.InputLocator(scenario=path_to_baseline)\n", "total_demand_path_baseline = inputlocator_baseline.get_total_demand()\n", "df_total_demand_baseline = pd.read_csv(total_demand_path_baseline, index_col='Name')\n", "\n", "# plot the yearly cooling energy demand\n", "fig, ax = plt.subplots()\n", "x_data = np.arange(len(df_total_demand_baseline.index))\n", "ax.bar(x_data-0.1, df_total_demand_baseline['QC_sys_MWhyr'], width=0.6, label='baseline')\n", "ax.bar(x_data+0.1, df_total_demand_random['QC_sys_MWhyr'], width=0.6, label='randomized schedule')\n", "ax.set_xticks(x_data)\n", "ax.set_xticklabels(df_total_demand_baseline.index)\n", "plt.ylabel('yearly cooling energy use (MWh/yr)')\n", "plt.legend()\n", "plt.show()\n", "\n", "# plot the peak cooling energy demand\n", "fig, ax = plt.subplots()\n", "ax.bar(x_data-0.1, df_total_demand_baseline['QC_sys0_kW'], width=0.6, label='baseline')\n", "ax.bar(x_data+0.1, df_total_demand_random['QC_sys0_kW'], width=0.6, label='randomized schedule')\n", "ax.set_xticks(x_data)\n", "ax.set_xticklabels(df_total_demand_baseline.index)\n", "plt.ylabel('peak cooling demand (kW)')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interestingly, the buildings' peak cooling loads are affected in different ways. Also, keep in mind that each execution of this code will yield different results, due to the inherent randomness introduced to the schedules.\n", "There seem to be lots of research opportunities." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15" } }, "nbformat": 4, "nbformat_minor": 2 }