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1# -*- coding: utf-8 -*-
2# @Author: Martin Raetz
3# @Date: 2019-02-19 18:41:56
4# @Last Modified by: Martin Rätz
5# @Last Modified time: 29.11.2019
7"""
8This script demonstrates how a building can be generated by importing building
9data from excel.
10An appropriate example file with some building data is imported from
11examplefiles/ExcelBuildingData_Sample.xlsx.
13In the excel every room is listed by its own, via a custom defined zoning
14algorithm these rooms are combined to zones.
15The user needs to adjust the zoning to his needs.
16See # Block: Zoning methodologies (define your zoning function here)
18Limitations and assumptions:
19- Outer and inner wall area depend on the calculations done in the excel
20- Ground floor area is only as big the respective net area of the heated room
21volume (NetArea)
22- Floor area is only as big the respective net area of the heated room volume
23(NetArea)
24- Rooftop area is only as big the respective net area of the heated room
25volume (NetArea)
26- Rooftops are flat and not tilted, see "RooftopTilt"
27- Ceiling area is only as big the respective net area of the heated room
28volume (NetArea)
29- Ceiling, floor and inner walls are only respected by half their area,
30since they belong half to the respective
31and half to the adjacent zone
32- Orientations are clockwise in degree, 0° is directed north
34-respective construction types have to be added to the TypeBuildingElements.json
35-respective UsageTypes for Zones have to be added to the UseConditions.json
36-excel file format has to be as shown in the "ExcelBuildingData_Sample.xlsx"
38Information about the required excel format:
39#Documentation in progress!
40-yellowed columns are necessary input to teaser -> don´t change column
41header, keep value names consistent.
42-non yellowed columns may either not be used or be used for your zoning
43algorithm
44-Under the cell ‚Usage type‘ you will see some cells that are blank but have
45their row filled.
46It means the blank cell actually belongs to the Usage type above but in that
47specific row we filled the characteristics
48of the window/wall of a different orientation of the same exact room. That
49means every row is either a new room or a
50new orientation of that room. A room might have two outer walls in two
51different orientation so for each outer wall,
52a an extra row defining the respective orientation is added
53-The entries in the excel sheet must be consistent for python being able to
54convert it.
55-If an inner wall is reaching inside a room but is not the limit of the room,
56it should be accounted with 2x the area
57"""
59import os
60import warnings
61import shutil
62import pandas as pd
63import numpy as np
64from teaser.project import Project
65from teaser.logic.buildingobjects.building import Building
66from teaser.logic.buildingobjects.thermalzone import ThermalZone
67from teaser.logic.buildingobjects.useconditions import UseConditions
68from teaser.logic.buildingobjects.buildingphysics.outerwall import OuterWall
69from teaser.logic.buildingobjects.buildingphysics.floor import Floor
70from teaser.logic.buildingobjects.buildingphysics.rooftop import Rooftop
71from teaser.logic.buildingobjects.buildingphysics.groundfloor import GroundFloor
72from teaser.logic.buildingobjects.buildingphysics.ceiling import Ceiling
73from teaser.logic.buildingobjects.buildingphysics.window import Window
74from teaser.logic.buildingobjects.buildingphysics.innerwall import InnerWall
77def import_data(path=None, sheet_names=None):
78 """
79 Import data from the building data excel file and perform some
80 preprocessing for nan and empty cells.
81 If several sheets are imported, the data is concatenated to one dataframe
83 Parameters
84 ----------
85 path: str
86 path to the excel file that should be imported
87 sheet_names: list or str
88 sheets of excel that should be imported
89 """
91 # process an import of a single sheet as well as several sheets,
92 # which will be concatenated with an continuous index
93 if type(sheet_names) == list:
94 data = pd.DataFrame()
95 _data = pd.read_excel(io=path, sheet_name=sheet_names, header=0, index_col=None)
96 for sheet in sheet_names:
97 data = data.append(_data[sheet], sort=False)
98 data = data.reset_index(drop=False)
99 data["index"] = data["index"] + 2 # sync the index with the excel index
100 else:
101 data = pd.read_excel(io=path, sheet_name=sheet_names, header=0, index_col=0)
103 # Cut of leading or tailing white spaces from any string in the dataframe
104 data = data.applymap(lambda x: x.strip() if type(x) is str else x)
106 # Convert every N/A, nan, empty strings and strings called N/a, n/A, NAN,
107 # nan, na, Na, nA or NA to np.nan
108 data = data.replace(
109 ["", "N/a", "n/A", "NAN", "nan", "na", "Na", "nA", "NA"], np.nan, regex=False
110 )
111 data = data.fillna(np.nan)
113 return data
116def get_list_of_present_entries(list_):
117 """
118 Extracts a list of all in the list available entries, discarding "None"
119 and "nan" entries
121 Parameters
122 ----------
123 list_: list
124 list that shall be processed
125 """
127 _List = []
128 for x in list_:
129 if x not in _List:
130 if not None:
131 if not pd.isna(x):
132 _List.append(x)
133 return _List
136# Block: Zoning methodologies (define your zoning function here)
137# -------------------------------------------------------------
138def zoning_example(data):
139 """
140 This is an example on how the rooms of a building could be aggregated to
141 zones.
143 In this example the UsageType has to be empty in the case that the
144 respective line does not represent another
145 room but a different orientated wall or window belonging to a room that
146 is already declared once in the excel file.
148 Parameters
149 ----------
150 data: pandas.dataframe
151 The data which shall be zoned
152 return data: pandas.dataframe
153 The zoning should return the imported dataset with an additional
154 column called "Zone" which inhibits the
155 information to which zone the respective room shall be part of,
156 and also a column called "UsageType_Teaser" which stores the
157 in UsageType of each row.
158 UsageType must be available in the UseConditions.json.
159 """
161 # account all outer walls not adjacent to the ambient to the entity
162 # "inner wall"
163 # !right now the wall construction of the added wall is not respected,
164 # the same wall construction as regular
165 # inner wall is set
166 for index, line in data.iterrows():
167 if not pd.isna(line["WallAdjacentTo"]):
168 data.loc[index, "InnerWallArea[m²]"] = (
169 data.loc[index, "OuterWallArea[m²]"]
170 + data.loc[index, "WindowArea[m²]"]
171 + data.loc[index, "InnerWallArea[m²]"]
172 )
173 data.loc[index, "WindowOrientation[°]"] = np.NaN
174 data.loc[index, "WindowArea[m²]"] = np.NaN
175 data.loc[index, "WindowConstruction"] = np.NaN
176 data.loc[index, "OuterWallOrientation[°]"] = np.NaN
177 data.loc[index, "OuterWallArea[m²]"] = np.NaN
178 data.loc[index, "OuterWallConstruction"] = np.NaN
180 # make all rooms that belong to a certain room have the same room identifier
181 _list = []
182 for index, line in data.iterrows():
183 if pd.isna(line["BelongsToIdentifier"]):
184 _list.append(line["RoomIdentifier"])
185 else:
186 _list.append(line["BelongsToIdentifier"])
187 data["RoomCluster"] = _list
189 # check for lines in which the net area is zero, marking an second wall
190 # or window
191 # element for the respective room, and in which there is still stated a
192 # UsageType which is wrong
193 # and should be changed in the file
194 for i, row in data.iterrows():
195 if (row["NetArea[m²]"] == 0 or row["NetArea[m²]"] == np.nan) and not pd.isna(
196 row["UsageType"]
197 ):
198 warnings.warn(
199 "In line %s the net area is zero, marking an second wall or "
200 "window element for the respective room, "
201 "and in which there is still stated a UsageType which is "
202 "wrong and should be changed in the file" % i
203 )
205 # make all rooms of the cluster having the usage type of the main usage type
206 _groups = data.groupby(["RoomCluster"])
207 for index, cluster in _groups:
208 count = 0
209 for line in cluster.iterrows():
210 if pd.isna(line[1]["BelongsToIdentifier"]) and not pd.isna(
211 line[1]["UsageType"]
212 ):
213 main_usage = line[1]["UsageType"]
214 for i, row in data.iterrows():
215 if row["RoomCluster"] == line[1]["RoomCluster"]:
216 data.loc[i, "RoomClusterUsage"] = main_usage
217 count += 1
218 if count != 1:
219 warnings.warn(
220 "This cluster has more than one main usage type or none, "
221 "check your excel file for mistakes! \n"
222 "Common mistakes: \n"
223 "-NetArea of a wall is not equal to 0 \n"
224 "-UsageType of a wall is not empty \n"
225 "Explanation: Rooms may have outer walls/windows on different orientations.\n"
226 "Every row with an empty slot in the column UsageType, "
227 "marks another direction of an outer wall and/or"
228 "window entity of the same room.\n"
229 "The connection of the same room is realised by an "
230 "RoomIdentifier equal to the respective "
231 "BelongsToIdentifier. \n Cluster = %s" % cluster
232 )
234 # name usage types after usage types available in the json
235 usage_to_json_usage = {
236 "IsolationRoom": "Bed room",
237 "PatientRoom": "Bed room",
238 "Aisle": "Corridors in the general care area",
239 "Technical room": "Stock, technical equipment, archives",
240 "Washing": "WC and sanitary rooms in non-residential buildings",
241 "Stairway": "Corridors in the general care area",
242 "WC": "WC and sanitary rooms in non-residential buildings",
243 "Storage": "Stock, technical equipment, archives",
244 "Lounge": "Meeting, Conference, seminar",
245 "Office": "Meeting, Conference, seminar",
246 "Treatment room": "Examination- or treatment room",
247 "StorageChemical": "Stock, technical equipment, archives",
248 "EquipmentServiceAndRinse": "WC and sanitary rooms in non-residential buildings",
249 }
251 # rename all zone names from the excel to the according zone name which
252 # is in the UseConditions.json files
253 usages = get_list_of_present_entries(data["RoomClusterUsage"])
254 data["UsageType_Teaser"] = ""
255 for usage in usages:
256 data["UsageType_Teaser"] = np.where(
257 data["RoomClusterUsage"] == usage,
258 usage_to_json_usage[usage],
259 data["UsageType_Teaser"],
260 )
262 # name the column where the zones are defined "Zone"
263 data["Zone"] = data["UsageType_Teaser"]
265 return data
268# -------------------------------------------------------------
269def import_building_from_excel(
270 project, building_name, construction_age, path_to_excel, sheet_names
271):
272 """
273 Import building data from excel, convert it via the respective zoning and feed it to teasers logic classes.
274 Pay attention to hard coded parts, which are marked.
276 Parameters
277 ----------
278 project: Project()
279 TEASER instance of Project
280 building_name: str
281 name of building to be set in the project
282 construction_age: int [y]
283 construction age of the building
284 path_to_excel: str
285 path to excel file to be imported
286 sheet_names: str or list
287 sheet names which shall be imported
288 return data: pandas.DataFrame
289 zoned DataFrame which is finally used to parametrize the teaser classes
290 return project: Project()
291 TEASER instance of Project filled with the imported building data
292 """
294 def warn_constructiontype(element):
295 """Generic warning function"""
296 if element.construction_data is None:
297 warnings.warn(
298 'In zone "%s" the %s construction "%s" could not be loaded from the TypeBuildingElements.json, '
299 "an error will occur due to missing data for calculation."
300 "Check for spelling and the correct combination of building age and construction type."
301 "Here is the list of faulty entries:\n%s"
302 "\nThese entries can easily be found checking the stated index in the produced ZonedInput.xlsx"
303 % (
304 group["Zone"].iloc[0],
305 element.name,
306 group["OuterWallConstruction"].iloc[0],
307 group,
308 )
309 )
311 bldg = Building(parent=project)
312 bldg.name = building_name
313 bldg.year_of_construction = construction_age
314 bldg.internal_gains_mode = 3 # HardCodedInput
315 bldg.with_ahu = True # HardCodedInput
316 if bldg.with_ahu is True:
317 bldg.central_ahu.heat_recovery = True # HardCodedInput
318 bldg.central_ahu.efficiency_recovery = 0.35 # HardCodedInput
319 bldg.central_ahu.temperature_profile = 24 * [273.15 + 18] # HardCodedInput
320 bldg.central_ahu.min_relative_humidity_profile = 24 * [0] # HardCodedInput
321 bldg.central_ahu.max_relative_humidity_profile = 24 * [1] # HardCodedInput
322 bldg.central_ahu.v_flow_profile = 24 * [1] # HardCodedInput
324 # Parameters that need hard coding in teasers logic classes
325 # 1. "use_set_back" needs hard coding at aixlib.py in the init; defines
326 # if the in the useconditions stated
327 # heating_time with the respective set_back_temp should be applied.
328 # use_set_back = false -> all hours of the day
329 # have same set_temp_heat actual value: use_set_back = Check your current version!
330 # !This may has been resolved with the last changes in the development
332 # Parameters to be set for each and every zone (#HardCodedInput)
333 # -----------------------------
334 out_wall_tilt = 90
335 window_tilt = 90
336 ground_floor_tilt = 0
337 floor_tilt = 0
338 ceiling_tilt = 0
339 rooftop_tilt = 0
340 ground_floor_orientation = -2
341 floor_orientation = -2
342 rooftop_orientation = -1
343 ceiling_orientation = -1
344 # -----------------------------
346 # load_building_data from excel_to_pandas DataFrame:
347 data = import_data(path_to_excel, sheet_names)
349 # informative print
350 usage_types = get_list_of_present_entries(data["UsageType"])
351 print("List of present usage_types in the original Data set: \n%s" % usage_types)
353 # define the zoning methodology/function
354 data = zoning_example(data)
356 # informative print
357 usage_types = get_list_of_present_entries(data["Zone"])
358 print("List of zones after the zoning is applied: \n%s" % usage_types)
360 # aggregate all rooms of each zone and for each set general parameter,
361 # boundary conditions
362 # and parameter regarding the building physics
363 zones = data.groupby(["Zone"])
364 for name, zone in zones:
366 # Block: Thermal zone (general parameter)
367 tz = ThermalZone(parent=bldg)
368 tz.name = str(name)
369 tz.area = np.nansum(zone["NetArea[m²]"])
370 # room vice calculation of volume plus summing those
371 tz.volume = np.nansum(
372 np.array(zone["NetArea[m²]"]) * np.array(zone["HeatedRoomHeight[m]"])
373 )
375 # Block: Boundary Conditions
376 # load UsageOperationTime, Lighting, RoomClimate and InternalGains
377 # from the "UseCondition.json"
378 tz.use_conditions = UseConditions(parent=tz)
379 tz.use_conditions.load_use_conditions(
380 zone["UsageType_Teaser"].iloc[0], project.data
381 )
383 # Block: Building Physics
384 # Grouping by orientation and construction type
385 # aggregating and feeding to the teaser logic classes
386 grouped = zone.groupby(["OuterWallOrientation[°]", "OuterWallConstruction"])
387 for name, group in grouped:
388 # looping through a groupby object automatically discards the
389 # groups where one of the attributes is nan
390 # additionally check for strings, since the value must be of type
391 # int or float
392 if not isinstance(group["OuterWallOrientation[°]"].iloc[0], str):
393 if (
394 np.nansum(group["OuterWallArea[m²]"]) > 0
395 ): # only create element if it has an area
396 out_wall = OuterWall(parent=tz)
397 out_wall.name = (
398 "outer_wall_"
399 + str(int(group["OuterWallOrientation[°]"].iloc[0]))
400 + "_"
401 + str(group["OuterWallConstruction"].iloc[0])
402 )
403 out_wall.area = np.nansum(group["OuterWallArea[m²]"])
404 out_wall.tilt = out_wall_tilt
405 out_wall.orientation = group["OuterWallOrientation[°]"].iloc[0]
406 # load wall properties from "TypeBuildingElements.json"
407 out_wall.load_type_element(
408 year=bldg.year_of_construction,
409 construction=group["OuterWallConstruction"].iloc[0],
410 )
411 warn_constructiontype(out_wall)
412 else:
413 warnings.warn(
414 'In zone "%s" the OuterWallOrientation "%s" is '
415 "neither float nor int, "
416 "hence this building element is not added.\nHere is the "
417 "list of faulty entries:\n%s"
418 "\n These entries can easily be found checking the stated "
419 "index in the produced ZonedInput.xlsx"
420 % (
421 group["Zone"].iloc[0],
422 group["OuterWallOrientation[°]"].iloc[0],
423 group,
424 )
425 )
427 grouped = zone.groupby(["WindowOrientation[°]", "WindowConstruction"])
428 for name, group in grouped:
429 # looping through a groupby object automatically discards the
430 # groups where one of the attributes is nan
431 # additionally check for strings, since the value must be of type
432 # int or float
433 if not isinstance(group["WindowOrientation[°]"].iloc[0], str):
434 if (
435 np.nansum(group["WindowArea[m²]"]) > 0
436 ): # only create element if it has an area
437 window = Window(parent=tz)
438 window.name = (
439 "window_"
440 + str(int(group["WindowOrientation[°]"].iloc[0]))
441 + "_"
442 + str(group["WindowConstruction"].iloc[0])
443 )
444 window.area = np.nansum(group["WindowArea[m²]"])
445 window.tilt = window_tilt
446 window.orientation = group["WindowOrientation[°]"].iloc[0]
447 # load wall properties from "TypeBuildingElements.json"
448 window.load_type_element(
449 year=bldg.year_of_construction,
450 construction=group["WindowConstruction"].iloc[0],
451 )
452 warn_constructiontype(window)
453 else:
454 warnings.warn(
455 'In zone "%s" the window orientation "%s" is neither '
456 "float nor int, "
457 "hence this building element is not added. Here is the "
458 "list of faulty entries:\n%s"
459 "\nThese entries can easily be found checking the stated "
460 "index in the produced ZonedInput.xlsx"
461 % (
462 group["Zone"].iloc[0],
463 group["WindowOrientation[°]"].iloc[0],
464 group,
465 )
466 )
468 grouped = zone.groupby(["IsGroundFloor", "FloorConstruction"])
469 for name, group in grouped:
470 if np.nansum(group["NetArea[m²]"]) != 0: # to avoid devision by 0
471 if group["IsGroundFloor"].iloc[0] == 1:
472 ground_floor = GroundFloor(parent=tz)
473 ground_floor.name = "ground_floor" + str(
474 group["FloorConstruction"].iloc[0]
475 )
476 ground_floor.area = np.nansum(group["NetArea[m²]"])
477 ground_floor.tilt = ground_floor_tilt
478 ground_floor.orientation = ground_floor_orientation
479 # load wall properties from "TypeBuildingElements.json"
480 ground_floor.load_type_element(
481 year=bldg.year_of_construction,
482 construction=group["FloorConstruction"].iloc[0],
483 )
484 warn_constructiontype(ground_floor)
485 elif group["IsGroundFloor"].iloc[0] == 0:
486 floor = Floor(parent=tz)
487 floor.name = "floor" + str(group["FloorConstruction"].iloc[0])
488 floor.area = np.nansum(group["NetArea[m²]"]) / 2 # only half of
489 # the floor belongs to this story
490 floor.tilt = floor_tilt
491 floor.orientation = floor_orientation
492 # load wall properties from "TypeBuildingElements.json"
493 floor.load_type_element(
494 year=bldg.year_of_construction,
495 construction=group["FloorConstruction"].iloc[0],
496 )
497 warn_constructiontype(floor)
498 else:
499 warnings.warn(
500 "Values for IsGroundFloor have to be either 0 or 1, "
501 "for no or yes respectively"
502 )
503 else:
504 warnings.warn(
505 'zone "%s" with IsGroundFloor "%s" and construction '
506 'type "%s" '
507 "has no floor nor groundfloor, since the area equals 0."
508 % (
509 group["Zone"].iloc[0],
510 group["IsGroundFloor"].iloc[0],
511 group["FloorConstruction"].iloc[0],
512 )
513 )
515 grouped = zone.groupby(["IsRooftop", "CeilingConstruction"])
516 for name, group in grouped:
517 if np.nansum(group["NetArea[m²]"]) != 0: # to avoid devision by 0
518 if group["IsRooftop"].iloc[0] == 1:
519 rooftop = Rooftop(parent=tz)
520 rooftop.name = "rooftop" + str(group["CeilingConstruction"].iloc[0])
521 rooftop.area = np.nansum(
522 group["NetArea[m²]"]
523 ) # sum up area of respective
524 # rooftop parts
525 rooftop.tilt = rooftop_tilt
526 rooftop.orientation = rooftop_orientation
527 # load wall properties from "TypeBuildingElements.json"
528 rooftop.load_type_element(
529 year=bldg.year_of_construction,
530 construction=group["CeilingConstruction"].iloc[0],
531 )
532 warn_constructiontype(rooftop)
533 elif group["IsRooftop"].iloc[0] == 0:
534 ceiling = Ceiling(parent=tz)
535 ceiling.name = "ceiling" + str(group["CeilingConstruction"].iloc[0])
536 ceiling.area = np.nansum(group["NetArea[m²]"]) / 2 # only half
537 # of the ceiling belongs to a story,
538 # the other half to the above
539 ceiling.tilt = ceiling_tilt
540 ceiling.orientation = ceiling_orientation
541 # load wall properties from "TypeBuildingElements.json"
542 ceiling.load_type_element(
543 year=bldg.year_of_construction,
544 construction=group["CeilingConstruction"].iloc[0],
545 )
546 warn_constructiontype(ceiling)
547 else:
548 warnings.warn(
549 "Values for IsRooftop have to be either 0 or 1, "
550 "for no or yes respectively"
551 )
552 else:
553 warnings.warn(
554 'zone "%s" with IsRooftop "%s" and construction type '
555 '"%s" '
556 "has no ceiling nor rooftop, since the area equals 0."
557 % (
558 group["Zone"].iloc[0],
559 group["IsRooftop"].iloc[0],
560 group["CeilingConstruction"].iloc[0],
561 )
562 )
564 grouped = zone.groupby(["InnerWallConstruction"])
565 for name, group in grouped:
566 if np.nansum(group["InnerWallArea[m²]"]) != 0: # to avoid devision by 0
567 in_wall = InnerWall(parent=tz)
568 in_wall.name = "inner_wall" + str(
569 group["InnerWallConstruction"].iloc[0]
570 )
571 in_wall.area = np.nansum(group["InnerWallArea[m²]"]) / 2 # only
572 # half of the wall belongs to each room,
573 # the other half to the adjacent
574 # load wall properties from "TypeBuildingElements.json"
575 in_wall.load_type_element(
576 year=bldg.year_of_construction,
577 construction=group["InnerWallConstruction"].iloc[0],
578 )
579 warn_constructiontype(in_wall)
580 else:
581 warnings.warn(
582 'zone "%s" with inner wall construction "%s" has no '
583 "inner walls, since area = 0."
584 % (group["Zone"].iloc[0], group["InnerWallConstructio" "n"].iloc[0])
585 )
587 # Block: AHU and infiltration #Attention hard coding
588 # set the supply volume flow of the AHU per zone
589 ahu_dict = {
590 "Bedroom": [15.778, 15.778],
591 "Corridorsinthegeneralcarearea": [5.2941, 5.2941],
592 "Examinationortreatmentroom": [15.743, 15.743],
593 "MeetingConferenceseminar": [16.036, 16.036],
594 "Stocktechnicalequipmentarchives": [20.484, 20.484],
595 "WCandsanitaryroomsinnonresidentialbuildings": [27.692, 27.692],
596 }
597 _i = 0
598 for key in ahu_dict:
599 if tz.name == key:
600 tz.use_conditions.min_ahu = ahu_dict[key][0]
601 tz.use_conditions.max_ahu = ahu_dict[key][1]
602 _i = 1
603 if _i == 0:
604 warnings.warn(
605 "The zone %s could not be found in your ahu_dict. Hence, "
606 "no AHU flow is defined. The default value is "
607 "0 (min_ahu = 0; max_ahu=0" % tz.name
608 )
610 return project, data
613if __name__ == "__main__":
614 result_path = os.path.dirname(__file__)
616 prj = Project()
617 prj.name = "BuildingGeneratedviaExcelImport"
618 prj.data.load_uc_binding()
619 prj.weather_file_path = os.path.join(
620 os.path.dirname(os.path.dirname(__file__)),
621 "data",
622 "input",
623 "inputdata",
624 "weatherdata",
625 "DEU_BW_Mannheim_107290_TRY2010_12_Jahr_BBSR.mos",
626 )
627 prj.modelica_info.weekday = 0 # 0-Monday, 6-Sunday
628 prj.modelica_info.simulation_start = 0 # start time for simulation
630 PathToExcel = os.path.join(
631 os.path.dirname(__file__), "examplefiles", "ExcelBuildingData_Sample.xlsx"
632 )
633 prj, Data = import_building_from_excel(
634 prj, "ExampleImport", 2000, PathToExcel, sheet_names=["ImportSheet1"]
635 )
637 prj.modelica_info.current_solver = "dassl"
638 prj.calc_all_buildings(raise_errors=True)
640 # Hard coding
641 # for zones: zone.model_attr.cool_load = -5000 or -zone.model_attr.heat_load
643 prj.export_aixlib(internal_id=None, path=result_path)
645 # if wished, export the zoned DataFrame which is finally used to
646 # parametrize the teaser classes
647 Data.to_excel(os.path.join(result_path, prj.name, "ZonedInput.xlsx"))
648 # if wished, save the current python script to the results folder to
649 # track the used parameters and reproduce results
650 shutil.copy(__file__, os.path.join(result_path, prj.name))
652 print("%s: That's it :)" % prj.name)