updated wrapper naming scheme

.gitignore

@@ -1,3 +1,5 @@
+logs
+
 # Python compiled files #
 #########################
 *.pyc

afc/defaultConfig.py

@@ -283,6 +283,7 @@ def default_parameter(tariff_name='e19-2020', hvac_control=True,
     parameter['objective']['weight_actuation'] = weight_actuation # Weight of facade actuation
     parameter['objective']['weight_glare'] = weight_glare # Weight of glare penalty in objective
     parameter['objective']['weight_view'] = 0 # Weight of view penalty in objective
+    parameter['objective']['weight_ghg'] = 0 # Weight of GHG emissions
     parameter['solver_options'] = {} # Pyomo solver options
     parameter['solver_options']['seconds'] = int(60) # Maximal solver time, in seconds
     #parameter['solver_options']['maxIterations'] = int(1e6) # Maximal iterations

afc/glare/view_angle.py

@@ -17,7 +17,7 @@ def sun_in_view(view_dist, view_height, window_bottom_height, window_top_height,
         right_azi: Right aziumth when sun in view through the window
     """
     if view_height > window_top_height:
-        print("WARNING: view_angle: Window below viewpoint.")
+        # print("WARNING: view_angle: Window below viewpoint.")
         bot_alt, top_alt, left_azi, right_azi, view_window_angle = 0, 0, 0, 0, 0
     else:
         bot_vw = abs(window_bottom_height - view_height)

afc/optModel.py

@@ -456,7 +456,8 @@ def objective_function(model):
                + model.sum_export_revenue * parameter['objective']['weight_export'] \
                + model.sum_zone_actuation * parameter['objective']['weight_actuation'] \
                + model.sum_glare_penalty * parameter['objective']['weight_glare'] \
-               + model.sum_view_penalty * parameter['objective']['weight_view']
+               + model.sum_view_penalty * parameter['objective']['weight_view'] \
+               + model.co2_total * parameter['objective']['weight_ghg']
                #+ model.sum_regulation_revenue * parameter['objective']['weight_regulation']
     model.objective = Objective(rule=objective_function, sense=minimize, doc='objective function')
     return model
@@ -499,6 +500,8 @@ def afc_output_list():
                         'index': 'temps'}) # room, slab
     ctrlOutputs.append({'data': 'zone_temp_max', 'df_label': 'Temperature %s Max [C]',
                         'index': 'temps'}) # room, slab
+    ctrlOutputs.append({'data': 'co2_profile_total', 'df_label': 'GHG Emissions [kg]'})
+
     for d in ctrlOutputs:
         d['name'] = d['data']
     return ctrlOutputs

afc/utility/plotting.py

@@ -83,8 +83,8 @@ def plot_standard1(data, title=None, plot=True, tight=True):
                  labels=['Room','Slab'])
     axs[5].plot(data[['Temperature 0 Max [C]']], color='blue', linestyle='--')
     axs[5].plot(data[['Temperature 0 Min [C]']], color='red', linestyle='--')
-    axs[5].plot(data[['Temperature 1 Max [C]']], color='blue', linestyle=':')
-    axs[5].plot(data[['Temperature 1 Min [C]']], color='red', linestyle=':')
+    #axs[5].plot(data[['Temperature 1 Max [C]']], color='blue', linestyle=':')
+    #axs[5].plot(data[['Temperature 1 Min [C]']], color='red', linestyle=':')
     plot_plot_xy(axs[6],data[['Glare [-]']],title='Glare Level',ylab='DGP [1]',
                  labels=['Glare'])
     axs[6].plot(data[['Glare Max [-]']], color='black', linestyle='--')

afc/utility/thermostat.py

@@ -0,0 +1,67 @@
+# Advanced Fenestration Controller (AFC) Copyright (c) 2023, The
+# Regents of the University of California, through Lawrence Berkeley
+# National Laboratory (subject to receipt of any required approvals
+# from the U.S. Dept. of Energy). All rights reserved.
+
+""""Advanced Fenestration Controller
+Thermostat control module.
+"""
+
+def afc_to_hvac_setpoint(ctrl_outputs, tdead=0.5):
+    """Utility to convert from AFC to thermostat setpoints."""
+
+    # no control by default
+    hvac_mode = 'X'
+    new_cool_set = float(ctrl_outputs['cool_set'])
+    new_heat_set = float(ctrl_outputs['heat_set'])
+
+    if ctrl_outputs['hvac_control'] and ctrl_outputs['feasible']:
+
+        # float by default
+        hvac_mode = 'F'
+
+        # get new setpoints if not occupied (make sure within bounds)
+        if not ctrl_outputs['occupied']:
+
+            # cooling
+            if ctrl_outputs['power_cool'] > 1:
+                hvac_mode = 'C'
+                new_cool_set = min(ctrl_outputs['cool_set'],
+                                   ctrl_outputs['t_room'])
+                # make sure cool > heat
+                new_cool_set = max(new_cool_set, new_heat_set + tdead)
+
+            # heating
+            elif ctrl_outputs['power_heat'] > 1:
+                hvac_mode = 'H'
+                new_heat_set = max(ctrl_outputs['heat_set'],
+                                   ctrl_outputs['t_room'])
+                # make sure heat < cool
+                new_heat_set = min(new_heat_set, new_cool_set - tdead)
+
+    return {'feasible': ctrl_outputs['feasible'],
+            'mode': hvac_mode,
+            'csp': new_cool_set,
+            'hsp': new_heat_set}
+
+def compute_thermostat_setpoints(df, feasible, control_hvac, occupied):
+    """wrapper to compute thermostat setpoints"""
+
+    cool_set = df['Temperature 0 Max [C]'].values[0]
+    heat_set = df['Temperature 0 Min [C]'].values[0]
+    t_room = df['Temperature 0 [C]'].values[1]
+    power_cool = df['Power Cooling [W]'].iloc[0]
+    power_heat = df['Power Heating [W]'].iloc[0]
+
+    ctrl_outputs = {
+        'hvac_control': control_hvac,
+        'feasible': feasible,
+        't_room': t_room,
+        'cool_set': cool_set,
+        'heat_set': heat_set,
+        'occupied': occupied,
+        'power_cool': power_cool,
+        'power_heat': power_heat,
+    }
+
+    return afc_to_hvac_setpoint(ctrl_outputs, tdead=0.5)

dev/Development-Controller.ipynb

@@ -557,7 +557,8 @@
     "               + model.sum_export_revenue * parameter['objective']['weight_export'] \\\n",
     "               + model.sum_zone_actuation * parameter['objective']['weight_actuation'] \\\n",
     "               + model.sum_glare_penalty * parameter['objective']['weight_glare'] \\\n",
-    "               + model.sum_view_penalty * parameter['objective']['weight_view']\n",
+    "               + model.sum_view_penalty * parameter['objective']['weight_view'] \\\n",
+    "               + model.co2_total * parameter['objective']['weight_ghg']\n",
     "               #+ model.sum_regulation_revenue * parameter['objective']['weight_regulation']\n",
     "    model.objective = Objective(rule=objective_function, sense=minimize, doc='objective function')\n",
     "    return model\n",
@@ -600,6 +601,8 @@
     "                        'index': 'temps'}) # room, slab\n",
     "    ctrlOutputs.append({'data': 'zone_temp_max', 'df_label': 'Temperature %s Max [C]',\n",
     "                        'index': 'temps'}) # room, slab\n",
+    "    ctrlOutputs.append({'data': 'co2_profile_total', 'df_label': 'GHG Emissions [kg]'})\n",
+    "\n",
     "    for d in ctrlOutputs:\n",
     "        d['name'] = d['data']\n",
     "    return ctrlOutputs\n",

examples/example_1.py

@@ -35,7 +35,7 @@ def example1():
     weather_path = os.path.join(os.path.dirname(root), 'dev', 'resources', 'weather', 
         'USA_CA_San.Francisco.Intl.AP.724940_TMY3.csv')
     weather, info = read_tmy3(weather_path, coerce_year=2023)
-    #weather = weather.resample('5T').interpolate()
+    #weather = weather.resample('5min').interpolate()
     st = dtm.datetime(2023, 7, 1)
     wf = weather.loc[st:st+pd.DateOffset(hours=24),]
     df = wf[['temp_air','dni','dhi','wind_speed']].copy()
@@ -49,7 +49,6 @@ def example1():
     inputs = make_inputs(parameter, df)
 
     # Query controller
-    #log = ctrl.do_step(inputs=inputs) # first run to initialize
     log = ctrl.do_step(inputs=inputs) # run controller
 
     # Print results
@@ -59,7 +58,7 @@ def example1():
     #                                    'opt_duration','outputs_duration','duration']))
     #print('Optimization:')
     #pprint.pprint(ctrl.get_output(keys=['opt_objective','opt_duration','opt_termination','duration']))
-    df = pd.DataFrame(ctrl.get_output(keys=['df_output'])['df_output'])
+    df = pd.DataFrame(ctrl.get_output(keys=['output-data'])['output-data'])
     df.index = pd.to_datetime(pd.to_numeric(df.index), unit='ms')
     print('Facade actuation during the day (when DNI > 0).')
     print('Facade 0 = bottom zone, Facade 1 = middle zone, Facade 2 = top zone')

test/test_1.py

@@ -31,7 +31,7 @@ def test1():
     weather_path = os.path.join(os.path.dirname(root), 'dev', 'resources', 'weather', 
         'USA_CA_San.Francisco.Intl.AP.724940_TMY3.csv')
     weather, info = read_tmy3(weather_path, coerce_year=2023)
-    weather = weather.resample('5T').interpolate()
+    weather = weather.resample('5min').interpolate()
     st = dtm.datetime(2023, 7, 1)
     wf = weather.loc[st:st+pd.DateOffset(hours=24),]
     df = wf[['temp_air','dni','dhi','wind_speed']].copy()
@@ -45,17 +45,13 @@ def test1():
     inputs = make_inputs(parameter, df)
 
     # Query controller
-    ctrl.do_step(inputs=inputs) # Initialize
-    print('Log-message:\n', ctrl.do_step(inputs=inputs))
-    print('Duration:\n', ctrl.get_output(keys=['rad_duration','varts_duration','optall_duration','glare_duration',
-                                               'opt_duration','outputs_duration','duration']))
-    print('Optimization:\n', ctrl.get_output(keys=['opt_objective','opt_duration','opt_termination','duration']))
-    df = pd.DataFrame(ctrl.get_output(keys=['df_output'])['df_output'])
+    ctrl.do_step(inputs=inputs)
+    df = pd.DataFrame(ctrl.get_output(keys=['output-data'])['output-data'])
     df.index = pd.to_datetime(df.index, unit='ms')
 
     # check
-    res = ctrl.get_output(keys=['opt_objective','opt_duration','opt_termination','duration'])
-    assert 19 < res['opt_objective'] < 20.5
-    assert res['opt_duration'] < 5
-    assert res['opt_termination'] == 'optimal'
-    assert res['duration'] < 60*5
+    res = ctrl.get_output(keys=['opt-stats', 'duration'])
+    assert 19.5 < res['opt-stats']['objective'] < 20.5
+    assert res['opt-stats']['duration'] < 5
+    assert res['opt-stats']['termination'] == 'optimal'
+    assert res['duration']['all'] < 60*5