tum-ens / urbanheatpro

A bottom-up model for the simulation of heat demand profiles of urban areas

UrbanHeatPro

A Bottom-up model for the simulation of heat demand profiles of urban areas

---

Features

• UrbanHeatPro is a python-based bottom-up model for the simulation of heat demand profiles of urban areas.
• It considers both the space heating demand and hot water demand. So far, the hot water demand is calculated only for residential buildings.
• Characteristic values for the building stock, building thermal properties, building set-temperature and annual hot water consumption are based on statistics for Germany.
• DSM strategies for the reduction of heat demand such as building renovation, heat load reduction, night-set back operation, etc, are easily implemented.
• The size of the study area can start from one building. Buildings to simulate should be included in the input (csv) file.
• By default, the model operates on an hourly time steps. However, the temporal resolution is configurable.

Requirements

Python 3.6 (installation with Anaconda recommended)

Input file (csv)

Each building is described with the following information:

1. Area (required)
Building ground floor area in m²
2. Use (required)
Building use as integer:
• 0 Commercial
• 1 Industrial
• 2 Public
• 3 Residential
3. Bid (optional)
Building identification number as integer
4. Free_walls (required)
Number of walls in contact with ambient temperature. The building is assumed to be a rectangular box with four walls.
5. Construction year class (optional)
Construction year class from TABULA Typology as integer:
• 0 <1859
• 1 1860 - 1918
• 2 1919 - 1948
• 3 1949 - 1957
• 4 1958 - 1968
• 5 1969 - 1978
• 6 1979 - 1983
• 7 1984 - 1994
• 8 1995 - 2001
• 9 2002 - 2009
6. Building type (optional)
Building type from TABULA Typology as integer: 0 Single-Family House (SFH) 1 Terraced House (TH) 2 Multi-family House (MFH) 3 Apartment Block (AB)
7. Refurbishment level (optional)
Refurfishment level for all building elements from TABULA Typology as integer: 0 No refurbishment (Existing state) 1 Usual refurbishment 2 Advanced refurbishment
8. Number of occupants (optional)
Number of occupants living in the building

All columns are required, if no information is given, the cell value is taken as NaN. This input file should be located in the corresponding folder input/buildings

Using UrbanHeatPro

To run the model with the given input data, change the desired information in the runme.py file and run this file in the command line:

python runme.py

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Copyright

Copyright (C) 2018-2021 Anahi Molar-Cruz

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details: http://www.gnu.org/licenses/.

urbanheatpro's Issues

runme.py input file

Hi Ana!
I am trying to model domestic hot water demand and I found your work (very interesting, by the way). When reading the runme.py code I noticed that the filename_buildings line calls either "Test_10buldings.csv" or "Unterhaching_mod.csv" file but I could not find either of them among the files of program. Am I missing something?
Thanks.

Night set-back timestep-wise checked

If I understand correctly, whether a building has a night set-back temperature for space heating is checked on a timestep-basis (per iii), not building-basis:

	`# Night set-back
	# From 23:00 to 6:00 temperature is lowered to 18degC
	# check if building has night set-back
	rand_num = np.random.uniform(0, 1, 1)
	if (rand_num < self._night_set_back):
		
		# check night-set-back hours
		if self.dt_vector[iii].hour >= self.schedule_nsb[0] or self.dt_vector[iii].hour < self.schedule_nsb[1]:
			# set Tset at Tnight_set_back
			self.Tset = self.T_nsb		`

which doesn't make sense, right? If you agree, I can provide a fix that checks the option for each building once.

PEP8

For gods sake, please format your code.

The code is not readable at all.. please format your code to pep8

https://www.python.org/dev/peps/pep-0008/