From fc5fca955e37510d4f21cba27e102ea1c60b1b55 Mon Sep 17 00:00:00 2001
From: Koa Wells <kekoa.wells@concordia.ca>
Date: Mon, 17 Apr 2023 14:41:43 -0400
Subject: [PATCH] Update ep to include hot water, update how results are saved,
 update requirements.txt

---
 energy_validation.py                   | 154 +++++++++++++++++--------
 requirements.txt                       |   2 +-
 results/energy_validation_results.xlsx | Bin 10656 -> 0 bytes
 3 files changed, 107 insertions(+), 49 deletions(-)
 delete mode 100644 results/energy_validation_results.xlsx

diff --git a/energy_validation.py b/energy_validation.py
index 74f16c6..b86edb2 100644
--- a/energy_validation.py
+++ b/energy_validation.py
@@ -5,6 +5,7 @@ from pathlib import Path
 import pandas as pd
 import platform
 import os
+import xlsxwriter
 
 from hub.imports.geometry_factory import GeometryFactory
 from hub.imports.weather_factory import WeatherFactory
@@ -28,14 +29,40 @@ class EnergyValidation:
         self.climate_file = Path(f'{self.storage_path}/{self.climate_file_name}.cli').resolve()
         self.meb_folder = Path('./results/meb').resolve()
         self.ep_folder = Path('./results/ep').resolve()
-        self.result_file = Path('./results/energy_validation_results.xlsx').resolve()
+
+        self.result_file = Path(f'./results/{datetime.datetime.now().strftime("%m-%d-%Y_%H-%M-%S")}'
+                                                    f'_energy_validation_results.xlsx').resolve()
+        results_xlsx = xlsxwriter.Workbook(self.result_file)
+        simulation_data_worksheet = results_xlsx.add_worksheet('Simulation data')
+        metadata_worksheet = results_xlsx.add_worksheet('Metadata')
+        bold = results_xlsx.add_format({'bold': True})
+
+        simulation_data_worksheet.write('A1', 'Building', bold)
+        simulation_data_worksheet.write('B1', 'Month', bold)
+        simulation_data_worksheet.write('C1', 'Heating demand (kWh/m2)', bold)
+        simulation_data_worksheet.write('D1', 'Cooling demand (kWh/m2)', bold)
+        simulation_data_worksheet.write('E1', 'Appliances demand (kWh/m2)', bold)
+        simulation_data_worksheet.write('F1', 'Lighting demand (kWh/m2)', bold)
+        simulation_data_worksheet.write('G1', 'Domestic hot water demand (kWh/m2)', bold)
+        simulation_data_worksheet.write('H1', 'Source of simulation (kWh/m2)', bold)
+
+        metadata_worksheet.write('A1', 'Building id', bold)
+        metadata_worksheet.write('B1', 'Storeys', bold)
+        metadata_worksheet.write('C1', 'M2 per storey', bold)
+        metadata_worksheet.write('D1', 'Total m2', bold)
+        metadata_worksheet.write('E1', 'Total m3', bold)
+        metadata_worksheet.write('F1', 'Year building', bold)
+        metadata_worksheet.write('G1', 'Type of building', bold)
+
+        results_xlsx.close()
 
         if platform.system() == 'Windows':
             self.encoding = 'windows-1252'
         else:
             self.encoding = 'utf-8'
 
-    def _sort_buildings(self, buildings_to_simulate):
+    @staticmethod
+    def _sort_buildings(buildings_to_simulate):
         sorted_buildings = {}
         for building in buildings_to_simulate:
             code_utili = building['properties']['CODE_UTILI']
@@ -123,12 +150,13 @@ class EnergyValidation:
                 index=False,
                 header=False,
                 )
+
     def _save_ep_results(self, demand, building_area, building_name):
         demand.drop('Date/Time', axis=1, inplace=True)
         # convert from J to kWh/m^2
-        demand *= 2.77778e-7/building_area
+        demand *= (2.77778e-7/building_area)
 
-        # replace indexes with month/day/year format
+        # insert date in month/day/year format
         months = [
             '1/1/2023',
             '2/1/2023',
@@ -146,15 +174,20 @@ class EnergyValidation:
 
         demand.insert(0, 'month', months)
 
-        # insert building_name to first column
+        # insert building_name
         demand.insert(0, 'building_name', building_name)
 
-        # TODO: add water usage once working from ep
-        demand['water_usage'] = 'NA'
+        # insert hot water demand
+        demand['water_usage'] = demand.iloc[:,5] - demand.iloc[:,2]
 
         # add simulation source as ep
         demand['source'] = 'ep'
 
+        # drop unneeded columns
+        demand.drop(4)
+        demand.drop(5)
+        demand.drop(6)
+
         # last, but not least, append our lovely reformatted data to the results spreadsheet
         with pd.ExcelWriter(self.result_file, engine='openpyxl', if_sheet_exists='overlay', mode='a') as writer:
             demand.to_excel(
@@ -215,53 +248,78 @@ class EnergyValidation:
                     city.climate_file = self.climate_file
                     city.name = f'{building_id}_energy_validation'
 
-                    # starting sra
-                    print(f'{building_id} starting sra')
-                    ExportsFactory('sra', city, self.tmp_folder,
-                                   weather_file=self.weather_file, weather_format='epw').export()
-                    sra_file = (self.tmp_folder / f'{city.name}_sra.xml').resolve()
-                    sra_start = datetime.datetime.now()
-                    Sra(sra_file, self.tmp_folder).run()
-                    sra_end = datetime.datetime.now() - sra_start
-                    ResultFactory('sra', city, self.tmp_folder).enrich()
+                    try:
+                        # starting sra
+                        print(f'{building_id} starting sra')
+                        ExportsFactory('sra', city, self.tmp_folder,
+                                       weather_file=self.weather_file, weather_format='epw').export()
+                        sra_file = (self.tmp_folder / f'{city.name}_sra.xml').resolve()
+                        sra_start = datetime.datetime.now()
+                        Sra(sra_file, self.tmp_folder).run()
+                        sra_end = datetime.datetime.now() - sra_start
+                        ResultFactory('sra', city, self.tmp_folder).enrich()
 
-                    # run meb
-                    print(f'{building_id} starting meb')
-                    for building in city.buildings:
-                        building.attic_heated = 0
-                        building.basement_heated = 1
+                        # run meb
+                        print(f'{building_id} starting meb')
+                        for building in city.buildings:
+                            building.attic_heated = 0
+                            building.basement_heated = 1
 
-                    EnergyBuildingsExportsFactory('insel_monthly_energy_balance', city, self.tmp_folder).export()
-                    meb_start = datetime.datetime.now()
-                    Meb(self.tmp_folder).run()
-                    meb_end = datetime.datetime.now() - meb_start
-                    ResultFactory('insel_meb', city, self.tmp_folder).enrich()
-                    results = MEBResults(city, Path('./results/meb/').resolve())
-                    results.print()
+                        EnergyBuildingsExportsFactory('insel_monthly_energy_balance', city, self.tmp_folder).export()
+                        meb_start = datetime.datetime.now()
+                        Meb(self.tmp_folder).run()
+                        meb_end = datetime.datetime.now() - meb_start
+                        ResultFactory('insel_meb', city, self.tmp_folder).enrich()
+                        results = MEBResults(city, Path('./results/meb/').resolve())
+                        results.print()
 
-                    # save meb results to energy_validation_results
-                    total_m2 = city.buildings[0].internal_zones[0].thermal_zones[0].total_floor_area
-                    meb_results = pd.read_csv(Path(f'{self.meb_folder}/demand.csv').resolve(),
-                                              encoding=self.encoding)
-                    meb_metadata = pd.read_csv(Path(f'{self.meb_folder}/metadata.csv').resolve(),
-                                               encoding=self.encoding)
-                    self._save_meb_results(meb_results, meb_metadata, total_m2)
+                        # run energyplus
+                        print(f'{building_id} starting energy plus')
+                        idf_file = EnergyBuildingsExportsFactory('idf', city, self.ep_folder).export()
+                        ep_start = datetime.datetime.now()
+                        idf_file.run()
+                        ep_end = datetime.datetime.now() - ep_start
 
-                    # run energyplus
-                    print(f'{building_id} starting energy plus')
-                    idf_file = EnergyBuildingsExportsFactory('idf', city, self.ep_folder).export_debug()
-                    ep_start = datetime.datetime.now()
-                    idf_file.run()
-                    ep_end = datetime.datetime.now() - ep_start
+                        # save meb results to energy_validation_results
+                        total_m2 = city.buildings[0].internal_zones[0].thermal_zones[0].total_floor_area
+                        meb_results = pd.read_csv(Path(f'{self.meb_folder}/demand.csv').resolve(),
+                                                  encoding=self.encoding)
+                        meb_metadata = pd.read_csv(Path(f'{self.meb_folder}/metadata.csv').resolve(),
+                                                   encoding=self.encoding)
+                        self._save_meb_results(meb_results, meb_metadata, total_m2)
 
-                    # save ep results to energy_validation_results
-                    ep_results = pd.read_csv(Path(f'{self.ep_folder}/{building_id}_energy_validation_mtr.csv').resolve(),
-                                              encoding=self.encoding)
-                    self._save_ep_results(ep_results, total_m2, city.buildings[0].name)
+                        # save ep results to energy_validation_results
+                        ep_results = pd.read_csv(Path(f'{self.ep_folder}/{building_id}_energy_validation_out.csv').resolve(),
+                                                  encoding=self.encoding)
+                        self._save_ep_results(ep_results, total_m2, city.buildings[0].name)
 
-                    print(f"{building_id} sra time: {sra_end}")
-                    print(f"{building_id} meb time: {meb_end}")
-                    print(f"{building_id} ep time: {ep_end}")
+                        print(f"{building_id} sra time: {sra_end}")
+                        print(f"{building_id} meb time: {meb_end}")
+                        print(f"{building_id} ep time: {ep_end}")
+
+                    except:
+                        building_with_error = pd.DataFrame([
+                            building_id,
+                            'NA',
+                            'NA',
+                            'NA',
+                            'NA',
+                            'NA',
+                            'NA',
+                            'NA',
+                            'NA'
+                        ]).transpose()
+                        print(building_with_error)
+
+                        # with pd.ExcelWriter(self.result_file, engine='openpyxl', if_sheet_exists='overlay',
+                        #                     mode='a') as writer:
+                        #     building_with_error.to_excel(
+                        #         writer,
+                        #         startrow=writer.sheets['Simulation data'].max_row,
+                        #         sheet_name='Simulation data',
+                        #         index=False,
+                        #         header=False,
+                        #     )
 
         if cleanup is True:
             [os.remove(os.path.join(self.tmp_folder, file)) for file in os.listdir(self.tmp_folder)]
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index a06638e..6b4f9ff 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1 +1 @@
-cerc-hub
+xlsxwriter
\ No newline at end of file
diff --git a/results/energy_validation_results.xlsx b/results/energy_validation_results.xlsx
deleted file mode 100644
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