Ideas for Improvement of the Districtgenerator

[c0nb4]

This list works as an general idea for ideas to improve the districtgenerator. Thinks that are marked, are done in this repo instead of the forked branch.

General Code Requirements:

• Rename the classes module, so the districtgenerator is installes as districtgenerator.
• Change imports from numpy to pandas, as it is more explicit. (e.g. with the weather files, it is very error prune).

Assumptions:

• Average height and floors. Change from 3 and 3.15.
• Average appartment size per Appartment Block.

[c0nb4]

Code:

• Change naming convention so it fits teaser, e.g. SFH to single_family_house. Avoids unnecassary parsing and provides better code quality.
• Provide option to save different scenarios with different names, by different naming conventions.

[c0nb4]

• Change config.

Currently, two files need to be configured [ site_data, time_data ] and default data is in [physics_data, design_weather_data, desingn_building_data]. Additionally, some aspects need to be configured, but are not used again. e,g. Location, climate zone and altitude all refer to the same files.

I think it would be smarter to calculate the nearest present weather filer or use a personal file based on the location [latitude, longitude].

[c0nb4]

• Enable export of assumptions, such as occupancy e.g.

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• The ISO 13790 is not valid anymore. It has been replaced by DIN EN ISO 52016-1. It should be checked whether there are differences in calculation and the results. However, in 2020 Lichtmeß argued, that the 5R1C model in ISO 13790 offers the benefit of better explainability.

[c0nb4]

[X] Create option to deal with different weather data formats. See exemplary code: https://github.com/IWUGERMANY/DIBS---Dynamic-ISO-Building-Simulator/blob/master/iso_simulator/radiation.py

[c0nb4]

[ ] consistent naming of class functions. In Envelope snake_style is used, in the rest CamelCase.

[c0nb4]

[ ] Change of setpoints between different buildings. Currently all buildings have the same setpoints for heating and cooling.

[c0nb4]

• Check if intepolation is necessary. If length is already at the necessary resolution provided in the input files (typically one hour) the interpolation in generate enviornment can be skpped.

  interpolate input data to achieve required data resolution

    # transformation from values for points in time to values for time intervals
    self.site["SunDirect"] = np.interp(np.arange(0, self.time["dataLength"]+1, self.time["timeResolution"]),
                                       np.arange(0, self.time["dataLength"]+1, self.time["dataResolution"]),
                                       temp_sunDirect)[0:-1]
    self.site["SunDiffuse"] = np.interp(np.arange(0, self.time["dataLength"]+1, self.time["timeResolution"]),
                                        np.arange(0, self.time["dataLength"]+1, self.time["dataResolution"]),
                                        temp_sunDiff)[0:-1]
    self.site["T_e"] = np.interp(np.arange(0, self.time["dataLength"]+1, self.time["timeResolution"]),
                                 np.arange(0, self.time["dataLength"]+1, self.time["dataResolution"]),
                                 temp_temp)[0:-1]
  

[c0nb4]

• Save data with export.
• Check runtime, takes currently forever for large buildings.

[c0nb4]

• Check how files are currently saved,
  

There seems to be no logic here.