15 Calculating Heat Demand Density along Street segments
Contents
© Alexander Jüstel, Fraunhofer IEG, Institution for Energy Infrastructures and Geothermal Systems, RWTH Aachen University, GNU Lesser General Public License v3.0
15 Calculating Heat Demand Density along Street segments#
This notebook illustrates how to calculate the heat demand density along street segments. Here the heat demand of the adjacent buildings will attributed to the closest street segment (LineString).
Importing Libraries#
[1]:
import geopandas as gpd
import matplotlib.pyplot as plt
import pandas as pd
import shapely
from pyheatdemand import processing
C:\Users\ale93371\Anaconda3\envs\pygeomechanical\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:
C:\Users\ale93371\Anaconda3\envs\pygeomechanical\lib\site-packages\numpy\.libs\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll
C:\Users\ale93371\Anaconda3\envs\pygeomechanical\lib\site-packages\numpy\.libs\libopenblas64__v0.3.23-246-g3d31191b-gcc_10_3_0.dll
warnings.warn("loaded more than 1 DLL from .libs:"
Loading Heat Demand Data#
The heat demand data utilized here can be downloaded from https://www.opengeodata.nrw.de/produkte/umwelt_klima/klima/raumwaermebedarfsmodell/.
[2]:
buildings = gpd.read_file('../../../test/data/Aachen_Buildings.shp').drop('id', axis=1)
footprints = buildings.copy(deep=True)
buildings['geometry'] = buildings.centroid
buildings = buildings[buildings['WB_HU']>0].reset_index(drop=True)
buildings.head()
[2]:
| WB_HU | geometry | |
|---|---|---|
| 0 | 768.012128 | POINT (292705.144 5627871.339) |
| 1 | 1010.052042 | POINT (292705.613 5627867.064) |
| 2 | 849.072467 | POINT (292429.547 5627523.452) |
| 3 | 454.138112 | POINT (292561.555 5627797.114) |
| 4 | 2335.658552 | POINT (292403.666 5627494.653) |
Loading Street Segments#
The street segments were downloaded from OpenStreet Maps and were edited for the purpose of this tutorial.
[3]:
roads = gpd.read_file('../../../test/data/Aachen_Streets.shp')
roads.crs = 'EPSG:4326'
roads = roads.to_crs('EPSG:25832')
roads.head()
[3]:
| full_id | osm_id | osm_type | highway | bicycle | service | zone_traff | turn_lanes | source_max | ref | ... | name_etymo | oneway | foot | width | surface | sidewalk | name | maxspeed | lit | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | w5155813 | 5155813 | way | residential | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | 7 | asphalt | both | Hanbrucher StraÃe | 30 | yes | LINESTRING (292758.350 5627929.657, 292768.884... |
| 1 | w5215882 | 5215882 | way | residential | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | no | yes | NaN | asphalt | both | Am Neuenhof | 30 | yes | LINESTRING (292687.050 5627901.427, 292681.924... |
| 2 | w12077876 | 12077876 | way | residential | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | Am Backes | 30 | yes | LINESTRING (292300.152 5627645.147, 292300.800... |
| 3 | w12078449 | 12078449 | way | residential | NaN | NaN | NaN | NaN | NaN | NaN | ... | Q66190 | NaN | NaN | NaN | asphalt | NaN | Leo-Blech-StraÃe | 30 | yes | LINESTRING (292341.172 5627706.044, 292313.565... |
| 4 | w12078450 | 12078450 | way | residential | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | asphalt | NaN | NekesstraÃe | 30 | NaN | LINESTRING (292383.174 5627774.798, 292335.277... |
5 rows × 33 columns
Plotting the data#
[4]:
fig, ax = plt.subplots(1, figsize=(10,10))
footprints.plot(ax=ax, column='WB_HU', markersize=1, legend=True, legend_kwds={'shrink':1, 'label': 'Heat Demand [kWh]'})
roads.plot(ax=ax, linewidth=1)
ax.set_xlim(292300, 292700)
ax.set_ylim(5627400,5628000)
plt.grid()
plt.xlabel('X [m]')
plt.ylabel('Y [m]')
[4]:
Text(61.347222222222214, 0.5, 'Y [m]')
Creating Connections#
The connection between every house and the nearest road is created using create_connections(..).
[5]:
gdf_connections = processing.create_connections(gdf_buildings=buildings,
gdf_roads=roads,
hd_data_column='WB_HU')
gdf_connections
C:\Users\ale93371\Anaconda3\envs\pygeomechanical\lib\site-packages\shapely\linear.py:90: RuntimeWarning: invalid value encountered in line_locate_point
return lib.line_locate_point(line, other)
[5]:
| geometry | WB_HU | |
|---|---|---|
| 0 | LINESTRING (292726.502 5627866.823, 292705.144... | 768.012128 |
| 1 | LINESTRING (292725.657 5627862.826, 292705.613... | 1010.052042 |
| 2 | LINESTRING (292726.502 5627866.823, 292705.144... | 768.012128 |
| 3 | LINESTRING (292725.657 5627862.826, 292705.613... | 1010.052042 |
| 4 | LINESTRING (292413.265 5627537.473, 292429.547... | 849.072467 |
| ... | ... | ... |
| 153 | LINESTRING (292421.857 5627634.247, 292419.748... | 21470.730216 |
| 154 | LINESTRING (292458.715 5627625.513, 292456.294... | 11794.683169 |
| 155 | LINESTRING (292565.484 5627742.419, 292561.759... | 12185.490605 |
| 156 | LINESTRING (292599.944 5627826.338, 292596.116... | 16881.994814 |
| 157 | LINESTRING (292642.319 5627824.162, 292637.298... | 19894.327553 |
158 rows × 2 columns
Plotting the data#
[6]:
fig, ax = plt.subplots(1, figsize=(10,10))
# Roads
roads.plot(ax=ax, linewidth=1)
# Connections between houses and roads
gdf_connections.plot(ax=ax, column='WB_HU', cmap='inferno')
# Houses
footprints.plot(ax=ax, column='WB_HU', cmap='inferno', zorder=100, legend_kwds={'shrink':1, 'label': 'Heat Demand [kWh]'})
ax.set_xlim(292300, 292700)
ax.set_ylim(5627400,5628000)
plt.grid()
plt.xlabel('X [m]')
plt.ylabel('Y [m]')
[6]:
Text(61.347222222222214, 0.5, 'Y [m]')
Calculating the Heat Demand for each Street segment#
The heat demand of every house is attributed to the nearest street segment using calculate_hd_street_segments(..).
[7]:
gdf_hd = processing.calculate_hd_street_segments(gdf_buildings=buildings,
gdf_roads=roads,
hd_data_column='WB_HU')
gdf_hd.head()
[7]:
| WB_HU | full_id | osm_id | osm_type | highway | bicycle | service | zone_traff | turn_lanes | source_max | ... | oneway | foot | width | surface | sidewalk | name | maxspeed | lit | geometry | HD_normalized | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 923948.221657 | w5215882 | 5215882 | way | residential | NaN | NaN | NaN | NaN | NaN | ... | no | yes | NaN | asphalt | both | Am Neuenhof | 30 | yes | LINESTRING (292687.050 5627901.427, 292681.924... | 1741.960760 |
| 8 | 25628.624084 | w13745756 | 13745756 | way | residential | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | Pieter-Bruegel-StraÃe | 30 | yes | LINESTRING (292677.282 5627843.170, 292667.040... | 278.830764 |
| 11 | 72448.930662 | w13745760 | 13745760 | way | residential | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | asphalt | NaN | Jean-Lejeune-StraÃe | 30 | NaN | LINESTRING (292432.216 5627559.482, 292410.911... | 793.926439 |
| 15 | 1778.064170 | w38532829 | 38532829 | way | path | designated | NaN | NaN | NaN | NaN | ... | yes | designated | NaN | asphalt | NaN | NaN | NaN | NaN | LINESTRING (292760.238 5628176.202, 292771.633... | 3.223262 |
| 22 | 38124.672958 | w201078988 | 201078988 | way | service | NaN | driveway | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | LINESTRING (292410.911 5627573.158, 292401.356... | 1952.001877 |
5 rows × 35 columns
Plotting the data#
[8]:
fig, ax = plt.subplots(1, figsize=(10,10))
# Roads
roads.plot(ax=ax, linewidth=1)
gdf_hd.plot(ax=ax, column='HD_normalized', cmap='inferno', linewidth=3, legend=True, legend_kwds={'shrink':1, 'label': 'Heat Demand [kWh]'})
# Connections between houses and roads
gdf_connections.plot(ax=ax, column='WB_HU', cmap='inferno')
# Houses
footprints.plot(ax=ax, column='WB_HU', cmap='inferno', zorder=100)
ax.set_xlim(292300, 292700)
ax.set_ylim(5627400,5628000)
plt.grid()
plt.xlabel('X [m]')
plt.ylabel('Y [m]')
[8]:
Text(61.347222222222214, 0.5, 'Y [m]')
