Merge pull request #412 from ibpsa/issue273_tesCasMulZonOffSimAir

Closes #273

data/find_days.py

@@ -17,7 +17,7 @@
 import numpy as np
 
 def find_days(heat, cool, data='simulate', img_name='boptest_bestest_air',
-              plot = False):
+              plot = False, peak_cool_restriction_hour=None):
     '''Find the start and final times for the test case scenarios.
 
     Parameters
@@ -39,6 +39,12 @@ def find_days(heat, cool, data='simulate', img_name='boptest_bestest_air',
         performed.
     plot: boolean
         Set to True to show an overview of the days found
+    peak_cool_restriction_hour: integer, optional
+        Hour if want peak cooling loads to only be considered equal to or after the hour
+        each day.  This can be useful to avoid including hours with peak
+        cooling loads due to morning start up, which may lead to the
+        same peak load for many different days.  None will have no restrictions.
+        Default is None.
 
     Returns
     -------
@@ -87,37 +93,48 @@ def find_days(heat, cool, data='simulate', img_name='boptest_bestest_air',
     df.dropna(axis=0, inplace=True)
     # Since assume two-week test period with one-week warmup,
     # edges of year are not available to choose from
-    df_available = df.loc[pd.Timedelta(days=14):pd.Timedelta(days=365-7)]
+    df_available =  df.loc[pd.Timedelta(days=14):pd.Timedelta(days=365-7)]
+    df_available_cool = df[cool].loc[pd.Timedelta(days=14):pd.Timedelta(days=365-7)]
+    df_cool = df[cool]
+    if peak_cool_restriction_hour is not None:
+        # Limit available cooling hours to those after restriction
+        df_available_cool = df_available_cool[df_available_cool.index.seconds/3600>=peak_cool_restriction_hour]
+        df_cool = df_cool[df_cool.index.seconds/3600>=peak_cool_restriction_hour]
+    df_available_heat = df[heat].loc[pd.Timedelta(days=14):pd.Timedelta(days=365-7)]
+    df_heat = df[heat]
 
 
     # Find peak
     if heat is not None:
-        peak_heat_day = df_available[heat].idxmax().days
+        peak_heat_day = df_available_heat.idxmax().days
         days['peak_heat_day'] = peak_heat_day
         print('Peak heat is day {0}.'.format(peak_heat_day))
 
     if cool is not None:
-        peak_cool_day = df_available[cool].idxmax().days
+        peak_cool_day = df_available_cool.idxmax().days
         days['peak_cool_day'] = peak_cool_day
         print('Peak cool is day {0}.'.format(peak_cool_day))
 
     # Find typical
-    df_daily = df.resample('D').max()
-    df_available_daily = df_available.resample('D').max()
+    df_daily_cool = df_cool.resample('D').max()
+    df_available_daily_cool = df_available_cool.resample('D').max()
+    df_daily_heat = df_heat.resample('D').max()
+    df_available_daily_heat = df_available_heat.resample('D').max()
 
     if heat is not None:
-        median_heat = df_daily[heat][df_daily[heat]>1].median()
-        typical_heat_day = df_available_daily[heat][df_available_daily[heat].values <= median_heat].sort_values(ascending=False).index[0].days
+        median_heat = df_daily_heat[df_daily_heat>1].median()
+        typical_heat_day = df_available_daily_heat[df_available_daily_heat.values <= median_heat].sort_values(ascending=False).index[0].days
         days['typical_heat_day'] = typical_heat_day
         print('Typical heat is day {0}.'.format(typical_heat_day))
 
     if cool is not None:
-        median_cool = df_daily[cool][df_daily[cool]>1].median()
-        typical_cool_day = df_available_daily[cool][df_available_daily[cool].values <= median_cool].sort_values(ascending=False).index[0].days
+        median_cool = df_daily_cool[df_daily_cool>1].median()
+        typical_cool_day = df_available_daily_cool[df_available_daily_cool.values <= median_cool].sort_values(ascending=False).index[0].days
         days['typical_cool_day'] = typical_cool_day
         print('Typical cool is day {0}.'.format(typical_cool_day))
 
     # Find heat cool mix
+    df_available_daily = df_available.resample('D').sum()
     if (heat is not None) and (cool is not None):
         df_available_daily_sum = pd.DataFrame(index=df_available_daily.index, columns=df_available_daily.columns)
         # For every day, integrate heating and cooling load

parsing/parser.py

@@ -42,7 +42,7 @@ def parse_instances(model_path, file_name):
     '''
 
     # Compile fmu
-    fmu_path = compile_fmu(model_path, file_name)
+    fmu_path = compile_fmu(model_path, file_name, jvm_args="-Xmx8g")
     # Load fmu
     fmu = load_fmu(fmu_path)
     # Check version
@@ -179,13 +179,13 @@ def write_wrapper(model_path, file_name, instances):
             # End file -- with hard line ending
             f.write('end wrapped;\n')
         # Export as fmu
-        fmu_path = compile_fmu('wrapped', [wrapped_path]+file_name)
+        fmu_path = compile_fmu('wrapped', [wrapped_path]+file_name, jvm_args="-Xmx8g")
     # If there are not, write and export wrapper model
     else:
         # Warn user
         warnings.warn('No signal exchange block instances found in model.  Exporting model as is.')
         # Compile fmu
-        fmu_path = compile_fmu(model_path, file_name)
+        fmu_path = compile_fmu(model_path, file_name, jvm_args="-Xmx8g")
         wrapped_path = None
 
     return fmu_path, wrapped_path

releasenotes.md

@@ -8,6 +8,11 @@ Released on xx/xx/xxxx.
 
 - Fix and clarify ``README.md`` for the ``/initialize`` and other API end points.  This is for [#408](https://github.com/ibpsa/project1-boptest/issues/408).
 
+**The following new test cases have been added:**
+
+- ``multizone_office_simple_air``, a 5-zone building based on the U.S. DOE medium office reference building located in Chicago, IL, USA, served by a single-duct Variable Air Volume (VAV) with terminal reheat, air-cooled chiller, and air-to-water heat pump.  This is for [#273](https://github.com/ibpsa/project1-boptest/issues/273).
+
+
 ## BOPTEST v0.2.0
 
 Released on 03/05/2022.

testcases/README.md

@@ -11,4 +11,5 @@ This directory contains test cases for BOPTEST.  A summary of available test cas
 | ``bestest_hydronic``| BESTEST Case 900 room model with gas boiler and radiator.|**Electricity Prices**: <br />``'constant'``, <br />``'dynamic'``, <br />``'highly_dynamic'``<br />**Time Periods**: <br />``'peak_heat_day'``, <br />``'typical_heat_day'``|
 | ``bestest_hydronic_heat_pump``| BESTEST Case 900 room model, scaled by four in floor area, that uses a heat pump as heating production system and floor heating as heating emission system.|**Electricity Prices**: <br />``'constant'``, <br />``'dynamic'``, <br />``'highly_dynamic'``<br />**Time Periods**: <br />``'peak_heat_day'``, <br />``'typical_heat_day'``|
 | ``multizone_residential_hydronic``| Multi-zone residential hydronic model with gas boiler, radiators, and ideal cooling system. |**Electricity Prices**: <br />``'constant'``, <br />``'dynamic'``, <br />``'highly_dynamic'``<br />**Time Periods**: <br />``'peak_heat_day'``, <br />``'typical_heat_day'``|
-| ``singlezone_commercial_hydronic``| Single-zone commercial building model representing a university building with district heating source, zone radiator, occupancy based on real data, and air handling unit providing fresh air with CO2 control and heat recovery. |**Electricity Prices**: <br />``'constant'``, <br />``'dynamic'``, <br />``'highly_dynamic'``<br />**Time Periods**: <br />``'peak_heat_day'``, <br />``'typical_heat_day'``|
+| ``singlezone_commercial_hydronic``| Single-zone commercial building model representing a university building with district heating source, zone radiator, occupancy based on real data, and air handling unit providing fresh air with CO2 control and heat recovery. |**Electricity Prices**: <br />``'constant'``, <br />``'dynamic'``, <br />``'highly_dynamic'``<br />**Time Periods**: <br />``'peak_heat_day'``, <br />``'typical_heat_day'``|
+| ``multizone_office_simple_air`` | Multi-zone commercial building model based on U.S. DOE medium office building with single-duct VAV with terminal reheat, air-cooled chiller, and air-to-water heat pump.|**Electricity Prices**: <br />``'constant'``, <br />``'dynamic'``, <br />``'highly_dynamic'``<br />**Time Periods**:<br />``'peak_heat_day'``, <br />``'typical_heat_day'``, <br />``'peak_cool_day'``, <br />``'typical_cool_day'``, <br />``'mix_day'``|

testcases/multizone_office_simple_air/doc/images/gain_schedule.csv

@@ -0,0 +1,10 @@
+0, 0.05
+8, 0.05
+9, 0.9
+12, 0.9
+12, 0.8
+13, 0.8
+13, 1
+17, 1
+19, 0.1
+24, 0.05