def create_load(occupancy, at_home):
# *hg Decide for either relative or absolute path
if False:
db_path = os.path.join(os.getcwd(), "data/swh_input_weather_cons.db")
else:
db_path = "data/swh_input_weather_cons.db"
# Connecting to database
try:
db = Sql(db_path)
except:
log.error("Failed to connect to database.")
# Converting sql to pandas DataFrame
try:
inputs = db.tables2dict(close=True)
except:
log.error("Failed to read input tables from DataFrame.")
load, loadid_peakload = SourceAndSink._make_example_loading_inputs(
inputs,
CONSUMER_LABELS,
get_np_random_state(),
occupancy=occupancy,
at_home=at_home,
)
return load
def climate_populate(request):
# Use path to SWH database defined in settings.py
swh_db_path = settings.SWH_DATABASE
log.info('Using SWH database "{}"'.format(swh_db_path))
db = Sql(swh_db_path)
log.info("Connected to database.")
try:
inputs = db.tables2dict(close=True)
log.info("Converted sql to pandas DataFrame.")
except:
msg = "Failed to read input tables from {}."
log.info(msg.format(inputs))
weather = SourceAndSink(input_dfs=inputs)
log.info("Created SourceAndSink object")
# Delete all previously stored climate objects before populating them
n_climate_zones = Climate.objects.all().count()
if n_climate_zones > 0:
Climate.objects.all().delete()
log.info("Deleted {} Climate object(s) from database.".format(
n_climate_zones))
n_climate_zones = 0
# Use climate data source as defined in settings.py
data_source = settings.CLIMATE_DATA_SOURCE
# Cycle from 1 to 16 (these are the climate zone IDs in the external database)
for climate_zone_id in range(1, 17):
climate_zone_id_str = "{:02d}".format(climate_zone_id)
climate_data = weather.irradiation_and_water_main(
climate_zone_id_str,
method="isotropic diffuse",
weather_data_source=data_source,
)
climate = Climate.objects.create_climate(
name=get_climate_zone_name(climate_zone_id_str),
climate_zone=climate_zone_id_str,
data_source=data_source,
)
log.info("\nCreated Climate object {} successfully.".format(climate))
climate.populate_data(climate_data)
log.info("Populated data of Climate object {} successfully.".format(
climate))
n_climate_zones += 1
msg = "\nPopulated {} climate zones.".format(n_climate_zones)
log.info(msg)
return redirect("sys:config_home")
def setUpClass(cls):
"""Initiates the sqlite db engine
for the test db file.
"""
test_db_name = "test.db"
test_db_fulpath = os.path.join(os.path.dirname(__file__), test_db_name)
cls.test_db_fulpath = test_db_fulpath
print(test_db_fulpath)
# create test db if it does not exist
if not os.path.exists(test_db_fulpath):
os.system("touch " + test_db_fulpath)
cls.sql_api = Sql(test_db_fulpath)
# example dict to write to db
cls.df = pd.DataFrame(data=[["a", 1], ["b", 2]],
columns=["comp", "cost"])
# example dict to write to db as table
cls.dict = {"k1": [12, 13, 14], "k2": ["a", "b", "c"]}
# example csv data
cls.path_to_csv = os.path.join(os.path.dirname(__file__), "table.csv")
# sql code to execute
cls.raw_sql = """CREATE TABLE sys_components
def setUpClass(cls):
"""Initiates the sqlite db engine
for the test db file.
"""
test_db_name = 'test.db'
test_db_fulpath = os.path.join(
os.path.dirname(__file__), test_db_name)
cls.test_db_fulpath = test_db_fulpath
print(test_db_fulpath)
# create test db if it does not exist
if not os.path.exists(test_db_fulpath):
os.system('touch ' + test_db_fulpath)
cls.sql_api = Sql(test_db_fulpath)
# example dict to write to db
cls.df = pd.DataFrame(
data=[['a', 1], ['b', 2]],
columns=['comp', 'cost'])
# example dict to write to db as table
cls.dict = {'k1': [12, 13, 14],
'k2': ['a', 'b', 'c']}
# example csv data
cls.path_to_csv = os.path.join(
os.path.dirname(__file__), 'table.csv')
# sql code to execute
cls.raw_sql = '''CREATE TABLE sys_components
def setUp(self):
"""Instantiates a test object"""
# read in data from the database
# assuming tests are run from swh
# directory
weather_db_path = os.path.join(os.getcwd(),
"mswh/comm/mswh_system_input.db")
# connect to the database
db = Sql(weather_db_path)
try:
# read table names for all tables in a
# {table name : sheet name} form
inputs = db.tables2dict(close=True)
except:
msg = "Failed to read input tables from {}."
log.error(msg.format(weather_db_path))
self.weather = SourceAndSink(input_dfs=inputs)
# get labels
self.c = SwhLabels().set_hous_labels()
def setUpClass(self):
"""Assigns values to test variables."""
random_state = np.random.RandomState(123)
self.plot_results = True
# it will save under img on the test directory
self.outpath = os.path.dirname(__file__)
# get labels
self.c = SwhLabels().set_hous_labels()
self.s = SwhLabels().set_prod_labels()
self.r = SwhLabels().set_res_labels()
# generate weather data and annual hourly
# water draw profile
weather_db_path = os.path.join(os.getcwd(),
"mswh/comm/mswh_system_input.db")
db = Sql(weather_db_path)
try:
inputs = db.tables2dict(close=True)
except:
msg = "Failed to read inputs from {}."
log.error(msg.format(weather_db_path))
source_and_sink = SourceAndSink(input_dfs=inputs)
# SF climate is 03, 16 is cold
self.weather = source_and_sink.irradiation_and_water_main(
"03", method="isotropic diffuse")
# community scale household occupancies for 4 households
occ_com = [4, 4, 3, 5]
# individual scale household occupancy
occ_ind = [4]
# are people at home during the day in any of the households:
# 'y' or 'n'
at_home_com = ["n", "n", "n", "n"]
at_home_ind = ["n"]
loads_com, peakload_com = SourceAndSink._make_example_loading_inputs(
inputs,
self.c,
random_state,
occupancy=occ_com,
at_home=at_home_com,
)
(
loads_indiv,
peakload_indiv,
) = SourceAndSink._make_example_loading_inputs(
inputs,
self.c,
random_state,
occupancy=occ_ind,
at_home=at_home_ind,
)
# scaled loads to match 300 L/day
load_array_val = (loads_indiv["End-Use Load"][0] *
(300 * 365 * 0.001) /
loads_indiv["End-Use Load"][0].sum())
loads_indiv_val = pd.DataFrame(
data=[[1, occ_ind[0], load_array_val]],
columns=[self.c["id"], self.c["occ"], self.c["load_m3"]],
)
# performance parameters
# solar thermal
sol_the_sys_params = pd.DataFrame(
data=[
[self.s["the_sto"], self.s["f_upper_vol"], 0.5],
[self.s["the_sto"], self.s["ins_thi"], 0.085],
[self.s["the_sto"], self.s["spec_hea_con"], 0.04],
[self.s["the_sto"], self.s["t_tap_set"], 322.04],
[self.s["the_sto"], self.s["h_vs_r"], 6.0],
[self.s["the_sto"], self.s["dt_appr"], 2.0],
[self.s["the_sto"], self.s["t_max_tank"], 344.15],
[self.s["the_sto"], self.s["eta_coil"], 0.84],
[self.s["the_sto"], self.s["circ"], 0.0],
[self.s["sol_col"], self.s["interc_hwb"], 0.753],
[self.s["sol_col"], self.s["slope_hwb"], -4.025],
[self.s["sol_pump"], self.s["eta_sol_pump"], 0.85],
[self.s["piping"], self.s["pipe_spec_hea_con"], 0.0175],
[self.s["piping"], self.s["pipe_ins_thick"], 0.008],
[self.s["piping"], self.s["flow_factor"], 0.8],
[self.s["piping"], self.s["dia_len_exp"], 0.43082708345352605],
[
self.s["piping"],
self.s["dia_len_sca"],
0.007911283766743384,
],
[
self.s["piping"],
self.s["discr_diam_m"],
"[0.0127, 0.01905, 0.0254, 0.03175, 0.0381,"
"0.0508, 0.0635, 0.0762, 0.1016]",
],
[self.s["piping"], self.s["circ"], False],
[self.s["piping"], self.s["long_br_len_fr"], 1.0],
[self.s["dist_pump"], self.s["eta_dist_pump"], 0.85],
],
columns=[self.s["comp"], self.s["param"], self.s["param_value"]],
)
last_row_for_indiv = (
sol_the_sys_params.shape[0] - 1 -
sol_the_sys_params.loc[sol_the_sys_params[self.s["comp"]] ==
self.s["dist_pump"], :].shape[0])
# conventional gas tank wh
# ~R2.1, EL 1 from the rulemaking analysis
gas_tank_wh_params = pd.DataFrame(
data=[
[self.s["gas_tank"], self.s["tank_re"], 0.78],
[self.s["gas_tank"], self.s["ins_thi"], 0.03],
[self.s["gas_tank"], self.s["spec_hea_con"], 0.081],
[self.s["gas_tank"], self.s["t_tap_set"], 322.04],
],
columns=[self.s["comp"], self.s["param"], self.s["param_value"]],
)
# gas tankless backup
sol_the_inst_gas_bckp_params = pd.DataFrame(
data=[[self.s["gas_burn"], self.s["comb_eff"], 0.85]],
columns=[self.s["comp"], self.s["param"], self.s["param_value"]],
)
# sizing
# assume 4 occupants
# basecase gas tank WH: DOE sizing rule based on
# peak hourly demand +/-2 gal, assuming +/- 0
gas_tank_size_indiv = UnitConv(
peakload_indiv.loc[0, self.c["max_load"]]).m3_gal(unit_in="gal")
gas_tank_wh_size = pd.DataFrame(
data=[[self.s["gas_tank"], gas_tank_size_indiv]],
columns=[self.s["comp"], self.s["cap"]],
)
# individual scale retrofit backup
sol_the_gas_tank_bckp_size = pd.DataFrame(
data=[[1, self.s["gas_tank"], gas_tank_size_indiv]],
columns=[self.c["id"], self.s["comp"], self.s["cap"]],
)
# CSI sizing rules
def demand_estimate(occ):
if occ == 1:
return 20.0
if occ == 2:
return 35.0
else:
return 35.0 + 10.0 * (occ - 2.0)
peakload_com[self.c["dem_estimate"]] = loads_com[self.c["occ"]].apply(
lambda x: demand_estimate(x))
demand_estimate_ind = demand_estimate(occ_ind[0])
demand_estimate_com = peakload_com[self.c["dem_estimate"]].sum()
col_area_scaler = 1.2 # (CSI sizing rule: upper limit 1.25)
tank_vol_scaler = 1.3 # (CSI sizing rule: lower limit 1.25)
col_area_ind_sqft = demand_estimate_ind * col_area_scaler
col_area_com_sqft = demand_estimate_com * col_area_scaler
tank_vol_ind_gal = col_area_ind_sqft * tank_vol_scaler
tank_vol_com_gal = col_area_com_sqft * tank_vol_scaler
col_area_ind = UnitConv(col_area_ind_sqft).sqft_m2()
col_area_com = UnitConv(col_area_com_sqft).sqft_m2()
tank_vol_ind = UnitConv(tank_vol_ind_gal).m3_gal(unit_in="gal")
tank_vol_com = UnitConv(tank_vol_com_gal).m3_gal(unit_in="gal")
# piping
pipe_m_per_hhld = 3.048
detached_k = 6.0
attached_k = 3.0
# distribution pump
dist_pump_size = 10.4376 * len(occ_com)**0.9277
# solar pump
com_solar_pump_size = 7.5101 * sum(occ_com)**0.5322
indiv_solar_pump_size = 7.5101 * sum(occ_ind)**0.5322
# individual
sol_the_sys_sizes_indiv = pd.DataFrame(
data=[
[self.s["sol_col"], col_area_ind],
[self.s["the_sto"], tank_vol_ind],
[self.s["sol_pump"], indiv_solar_pump_size],
[self.s["piping"], pipe_m_per_hhld],
],
columns=[self.s["comp"], self.s["cap"]],
)
sol_the_inst_gas_bckp_size = pd.DataFrame(
data=[[1, self.s["gas_burn"], 50972]],
columns=[self.c["id"], self.s["comp"], self.s["cap"]],
)
# piping for single family attached
sol_the_sys_sizes_com = pd.DataFrame(
data=[
[self.s["sol_col"], col_area_com],
[self.s["the_sto"], tank_vol_com],
[self.s["sol_pump"], com_solar_pump_size],
[self.s["dist_pump"], dist_pump_size],
[
self.s["piping"],
attached_k * pipe_m_per_hhld * len(occ_com),
],
],
columns=[self.s["comp"], self.s["cap"]],
)
# gas tank backup
sol_the_gas_tank_bckp_params = gas_tank_wh_params.copy()
peakload_com["gas_tank_size_com"] = peakload_com[self.c[
"max_load"]].apply(lambda x: UnitConv(x).m3_gal(unit_in="gal"))
peakload_com.index = peakload_com[self.c["id"]]
sol_the_gas_tank_bckp_sizes_com = pd.DataFrame(
columns=[self.c["id"], self.s["comp"], self.s["cap"]],
index=peakload_com.index,
)
for i in peakload_com.index:
sol_the_gas_tank_bckp_sizes_com.loc[i, self.c["id"]] = i
sol_the_gas_tank_bckp_sizes_com.loc[
i, self.s["comp"]] = self.s["gas_tank"]
sol_the_gas_tank_bckp_sizes_com.loc[
i, self.s["cap"]] = peakload_com.loc[i, "gas_tank_size_com"]
sol_the_gas_tank_bckp_sizes_com.index = range(
sol_the_gas_tank_bckp_sizes_com.shape[0])
sol_the_inst_gas_bckp_sizes_com = pd.DataFrame(
columns=[self.c["id"], self.s["comp"], self.s["cap"]],
index=peakload_com.index,
)
def gas_tankles_size_W(occupancy):
size = 24875.0 * occupancy**0.5175
return size
for i in peakload_com.index:
sol_the_inst_gas_bckp_sizes_com.loc[i, self.c["id"]] = i
sol_the_inst_gas_bckp_sizes_com.loc[
i, self.s["comp"]] = self.s["gas_burn"]
sol_the_inst_gas_bckp_sizes_com.loc[
i, self.s["cap"]] = gas_tankles_size_W(occ_com[i - 1])
sol_the_inst_gas_bckp_sizes_com.index = range(
sol_the_inst_gas_bckp_sizes_com.shape[0])
# instantiate systems
# individual system
self.sol_wh_indiv_new = System(
sys_params=sol_the_sys_params.loc[:last_row_for_indiv, :],
backup_params=sol_the_inst_gas_bckp_params,
sys_sizes=sol_the_sys_sizes_indiv,
backup_sizes=sol_the_inst_gas_bckp_size,
weather=self.weather,
loads=loads_indiv,
)
msg = ("Set up the individual solar thermal system with tankless "
"backup test case.")
log.info(msg)
self.sol_wh_indiv_retr = System(
sys_params=sol_the_sys_params.loc[:last_row_for_indiv, :],
backup_params=sol_the_gas_tank_bckp_params,
sys_sizes=sol_the_sys_sizes_indiv,
backup_sizes=sol_the_gas_tank_bckp_size,
weather=self.weather,
loads=loads_indiv,
)
msg = ("Set up the individual solar thermal system with gas tank "
"backup test case.")
log.info(msg)
# community system
self.sol_wh_com_new = System(
sys_params=sol_the_sys_params,
backup_params=sol_the_inst_gas_bckp_params,
sys_sizes=sol_the_sys_sizes_com,
backup_sizes=sol_the_inst_gas_bckp_sizes_com,
weather=self.weather,
loads=loads_com,
)
msg = ("Set up the community solar thermal system with tankless "
"backup test case.")
log.info(msg)
self.sol_wh_com_retr = System(
sys_params=sol_the_sys_params,
backup_params=sol_the_gas_tank_bckp_params,
sys_sizes=sol_the_sys_sizes_com,
backup_sizes=sol_the_gas_tank_bckp_sizes_com,
weather=self.weather,
loads=loads_com,
)
msg = ("Set up the community solar thermal system with gas tank "
"backup test case.")
log.info(msg)
# solar thermal validation
# individual new with
# tank and collector size from
# https://secure.solar-rating.org/Certification/Ratings/#
# RatingsReport.aspx?device=6926&units=METRICS
# solar thermal retrofit solar fractions in the rating sheets are low
# - for the same
# size of collector and storage the new and retrofit should have the
# same solar fraction
# by definition https://en.wikipedia.org/wiki/Solar_savings_fraction
# sizes based on several observed rated systems from OG-300
# with an 80 gal tank and gas tankless backup
sol_the_sys_sizes_val = pd.DataFrame(
data=[
[self.s["sol_col"], 3.9],
[self.s["the_sto"],
UnitConv(80.0).m3_gal(unit_in="gal")],
[self.s["sol_pump"], indiv_solar_pump_size],
[self.s["piping"], 0.0],
],
columns=[self.s["comp"], self.s["cap"]],
)
self.val_sol_wh = System(
sys_params=sol_the_sys_params.loc[:last_row_for_indiv, :],
backup_params=sol_the_inst_gas_bckp_params,
sys_sizes=sol_the_sys_sizes_val,
backup_sizes=sol_the_inst_gas_bckp_size,
weather=self.weather,
loads=loads_indiv_val,
)
msg = ("Set up the individual solar thermal system validation "
"test case.")
log.info(msg)
self.conv_wh = System(
sys_params=gas_tank_wh_params,
sys_sizes=gas_tank_wh_size,
weather=self.weather,
loads=loads_indiv,
)
msg = "Set up the conventional tank wh test case."
log.info(msg)
# With baseline tank with a slightly higher RE and insulation set to
# R12, as used for the solar tank
gas_tank_wh_params_val = pd.DataFrame(
data=[
[self.s["gas_tank"], self.s["tank_re"], 0.82, "-"],
[self.s["gas_tank"], self.s["ins_thi"], 0.04, "m"],
[self.s["gas_tank"], self.s["spec_hea_con"], 0.085, "W/mK"],
[self.s["gas_tank"], self.s["t_tap_set"], 322.04, "K"],
],
columns=[
self.s["comp"],
self.s["param"],
self.s["param_value"],
self.s["param_unit"],
],
)
# with validation loads and validation parameters
self.conv_wh_val = System(
sys_params=gas_tank_wh_params_val,
sys_sizes=gas_tank_wh_size,
weather=self.weather,
loads=loads_indiv_val,
)
msg = "Set up the conventional tank wh validation test case."
log.info(msg)
# project level component parameter
# dataframe for solar electric system
# with an inst. gas backup
sol_el_tank_params = pd.DataFrame(
data=[
[self.s["hp"], self.s["c1_cop"], 1.229e00],
[self.s["hp"], self.s["c2_cop"], 5.549e-02],
[self.s["hp"], self.s["c3_cop"], 1.139e-04],
[self.s["hp"], self.s["c4_cop"], -1.128e-02],
[self.s["hp"], self.s["c5_cop"], -3.570e-06],
[self.s["hp"], self.s["c6_cop"], -7.234e-04],
[self.s["hp"], self.s["c1_heat_cap"], 7.055e-01],
[self.s["hp"], self.s["c2_heat_cap"], 3.945e-02],
[self.s["hp"], self.s["c3_heat_cap"], 1.433e-04],
[self.s["hp"], self.s["c4_heat_cap"], 2.768e-03],
[self.s["hp"], self.s["c5_heat_cap"], -1.069e-04],
[self.s["hp"], self.s["c6_heat_cap"], -2.494e-04],
[self.s["hp"], self.s["heat_cap_rated"], 2350.0],
[self.s["hp"], self.s["cop_rated"], 2.43],
[self.s["pv"], self.s["eta_pv"], 0.16],
[self.s["pv"], self.s["f_act"], 1.0],
[self.s["pv"], self.s["irrad_ref"], 1000.0],
[self.s["inv"], self.s["eta_dc_ac"], 0.85],
[self.s["the_sto"], self.s["f_upper_vol"], 0.5],
[self.s["the_sto"], self.s["ins_thi"], 0.04],
[self.s["the_sto"], self.s["spec_hea_con"], 0.04],
[self.s["the_sto"], self.s["t_tap_set"], 322.04],
[self.s["the_sto"], self.s["h_vs_r"], 6.0],
[self.s["the_sto"], self.s["dt_appr"], 2.0],
[self.s["the_sto"], self.s["t_max_tank"], 344.15],
[self.s["piping"], self.s["pipe_spec_hea_con"], 0.0175],
[self.s["piping"], self.s["pipe_ins_thick"], 0.008],
[self.s["piping"], self.s["flow_factor"], 0.8],
[self.s["piping"], self.s["dia_len_exp"], 0.43082708345352605],
[
self.s["piping"],
self.s["dia_len_sca"],
0.007911283766743384,
],
[
self.s["piping"],
self.s["discr_diam_m"],
"[0.0127, 0.01905, 0.0254, 0.03175, 0.0381,"
"0.0508, 0.0635, 0.0762, 0.1016]",
],
[self.s["piping"], self.s["circ"], False],
[self.s["piping"], self.s["long_br_len_fr"], 1.0],
],
columns=[self.s["comp"], self.s["param"], self.s["param_value"]],
)
# test sizing
sol_el_tank_sizes_indiv = pd.DataFrame(
data=[
[self.s["hp"], 2350.0],
[self.s["pv"], 6.25],
[self.s["the_sto"], UnitConv(80).m3_gal()],
[self.s["dist_pump"], dist_pump_size],
[self.s["piping"], pipe_m_per_hhld],
],
columns=[self.s["comp"], self.s["cap"]],
)
# electric resistance backup parameters
sol_el_tank_inst_gas_bckp_params = pd.DataFrame(
data=[[self.s["el_res"], self.s["eta_el_res"], 1.0]],
columns=[self.s["comp"], self.s["param"], self.s["param_value"]],
)
# electric resistance backup size
sol_el_tank_inst_el_res_bckp_size = pd.DataFrame(
data=[[1, self.s["el_res"], 6500.0]],
columns=[self.c["id"], self.s["comp"], self.s["cap"]],
)
self.hp_wh = System(
sys_params=sol_el_tank_params,
backup_params=sol_el_tank_inst_gas_bckp_params,
sys_sizes=sol_el_tank_sizes_indiv,
backup_sizes=sol_el_tank_inst_el_res_bckp_size,
weather=self.weather,
loads=loads_indiv,
)
def setUpClass(self):
"""Assigns values to test variables.
"""
random_state = np.random.RandomState(123)
self.plot_results = True
# it will save under img on the test directory
self.outpath = os.path.dirname(__file__)
# get labels
self.c = SwhLabels().set_hous_labels()
self.s = SwhLabels().set_prod_labels()
self.r = SwhLabels().set_res_labels()
# generate weather data and annual hourly
# water draw profile
weather_db_path = os.path.join(os.getcwd(),
'mswh/comm/mswh_system_input.db')
db = Sql(weather_db_path)
try:
# read table names for all tables in a
# {table name : sheet name} form
inputs = db.tables2dict(close=True)
except:
msg = 'Failed to read input tables from {}.'
log.error(msg.format(inpath))
source_and_sink = SourceAndSink(input_dfs=inputs)
# SF climate is 03, 16 is cold
self.weather = source_and_sink.irradiation_and_water_main(
'03', method='isotropic diffuse')
# community scale household occupancies for 4 households
occ_com = [4, 4, 3, 5]
# individual scale household occupancy
occ_ind = [4]
# are people at home during the day in any of the households:
# 'y' or 'n'
at_home_com = ['n', 'n', 'n', 'n']
at_home_ind = ['n']
loads_com, peakload_com = SourceAndSink._make_example_loading_inputs(
inputs,
self.c,
random_state,
occupancy=occ_com,
at_home=at_home_com)
loads_indiv, peakload_indiv = SourceAndSink._make_example_loading_inputs(
inputs,
self.c,
random_state,
occupancy=occ_ind,
at_home=at_home_ind)
# scaled loads to match 300 L/day
load_array_val = (loads_indiv['End-Use Load'][0] * (300 * 365 * .001) /
loads_indiv['End-Use Load'][0].sum())
loads_indiv_val = pd.DataFrame(
data=[[1, occ_ind[0], load_array_val]],
columns=[self.c['id'], self.c['occ'], self.c['load_m3']])
# performance parameters
# solar thermal
sol_the_sys_params = pd.DataFrame(
data=[[self.s['the_sto'], self.s['f_upper_vol'], .5],
[self.s['the_sto'], self.s['ins_thi'], .085],
[self.s['the_sto'], self.s['spec_hea_con'], .04],
[self.s['the_sto'], self.s['t_tap_set'], 322.04],
[self.s['the_sto'], self.s['h_vs_r'], 6.],
[self.s['the_sto'], self.s['dt_appr'], 2.],
[self.s['the_sto'], self.s['t_max_tank'], 344.15],
[self.s['the_sto'], self.s['eta_coil'], .84],
[self.s['the_sto'], self.s['circ'], 0.],
[self.s['sol_col'], self.s['interc_hwb'], .753],
[self.s['sol_col'], self.s['slope_hwb'], -4.025],
[self.s['sol_pump'], self.s['eta_sol_pump'], .85],
[self.s['piping'], self.s['pipe_spec_hea_con'], .0175],
[self.s['piping'], self.s['pipe_ins_thick'], .008],
[self.s['piping'], self.s['flow_factor'], .8],
[self.s['piping'], self.s['dia_len_exp'],
0.43082708345352605],
[self.s['piping'], self.s['dia_len_sca'],
0.007911283766743384],
[self.s['piping'], self.s['discr_diam_m'],
'[0.0127, 0.01905, 0.0254, 0.03175, 0.0381,'\
'0.0508, 0.0635, 0.0762, 0.1016]'],
[self.s['piping'], self.s['circ'], False],
[self.s['piping'], self.s['long_br_len_fr'], 1.],
[self.s['dist_pump'], self.s['eta_dist_pump'], .85]],
columns=[self.s['comp'], self.s['param'], self.s['param_value']])
last_row_for_indiv = (
sol_the_sys_params.shape[0] - 1 -
sol_the_sys_params.loc[sol_the_sys_params[self.s['comp']] ==
self.s['dist_pump'], :].shape[0])
# conventional gas tank wh
# ~R2.1, EL 1 from the rulemaking analysis
gas_tank_wh_params = pd.DataFrame(
data=[[self.s['gas_tank'], self.s['tank_re'], .78],
[self.s['gas_tank'], self.s['ins_thi'], .03],
[self.s['gas_tank'], self.s['spec_hea_con'], .081],
[self.s['gas_tank'], self.s['t_tap_set'], 322.04]],
columns=[self.s['comp'], self.s['param'], self.s['param_value']])
# gas tankless backup
sol_the_inst_gas_bckp_params = pd.DataFrame(
data=[[self.s['gas_burn'], self.s['comb_eff'], .85]],
columns=[self.s['comp'], self.s['param'], self.s['param_value']])
# sizing
# assume 4 occupants
# basecase gas tank WH: DOE sizing rule based on
# peak hourly demand +/-2 gal, assuming +/- 0
gas_tank_size_indiv = UnitConv(
peakload_indiv.loc[0, self.c['max_load']]).m3_gal(unit_in='gal')
gas_tank_wh_size = pd.DataFrame(
data=[[self.s['gas_tank'], gas_tank_size_indiv]],
columns=[self.s['comp'], self.s['cap']])
# individual scale retrofit backup
sol_the_gas_tank_bckp_size = pd.DataFrame(
data=[[1, self.s['gas_tank'], gas_tank_size_indiv]],
columns=[self.c['id'], self.s['comp'], self.s['cap']])
# CSI sizing rules
def demand_estimate(occ):
if occ == 1:
return 20.
if occ == 2:
return 35.
else:
return 35. + 10. * (occ - 2.)
peakload_com[self.c['dem_estimate']] = \
loads_com[self.c['occ']].apply(lambda x: demand_estimate(x))
demand_estimate_ind = demand_estimate(occ_ind[0])
demand_estimate_com = \
peakload_com[self.c['dem_estimate']].sum()
col_area_scaler = 1.2 # (CSI sizing rule: upper limit 1.25)
tank_vol_scaler = 1.3 # (CSI sizing rule: lower limit 1.25)
col_area_ind_sqft = demand_estimate_ind * col_area_scaler
col_area_com_sqft = demand_estimate_com * col_area_scaler
tank_vol_ind_gal = col_area_ind_sqft * tank_vol_scaler
tank_vol_com_gal = col_area_com_sqft * tank_vol_scaler
col_area_ind = UnitConv(col_area_ind_sqft).sqft_m2()
col_area_com = UnitConv(col_area_com_sqft).sqft_m2()
tank_vol_ind = UnitConv(tank_vol_ind_gal).m3_gal(unit_in='gal')
tank_vol_com = UnitConv(tank_vol_com_gal).m3_gal(unit_in='gal')
# piping
pipe_m_per_hhld = 3.048
detached_k = 6.
attached_k = 3.
# distribution pump
dist_pump_size = 10.4376 * len(occ_com)**0.9277
# solar pump
com_solar_pump_size = 7.5101 * sum(occ_com)**0.5322
indiv_solar_pump_size = 7.5101 * sum(occ_ind)**0.5322
# individual
sol_the_sys_sizes_indiv = pd.DataFrame(
data=[[self.s['sol_col'], col_area_ind],
[self.s['the_sto'], tank_vol_ind],
[self.s['sol_pump'], indiv_solar_pump_size],
[self.s['piping'], pipe_m_per_hhld]],
columns=[self.s['comp'], self.s['cap']])
sol_the_inst_gas_bckp_size = pd.DataFrame(
data=[[1, self.s['gas_burn'], 50972]],
columns=[self.c['id'], self.s['comp'], self.s['cap']])
# piping for single family attached
sol_the_sys_sizes_com = pd.DataFrame(
data=[[self.s['sol_col'], col_area_com],
[self.s['the_sto'], tank_vol_com],
[self.s['sol_pump'], com_solar_pump_size],
[self.s['dist_pump'], dist_pump_size],
[
self.s['piping'],
attached_k * pipe_m_per_hhld * len(occ_com)
]],
columns=[self.s['comp'], self.s['cap']])
# gas tank backup
sol_the_gas_tank_bckp_params = gas_tank_wh_params.copy()
peakload_com['gas_tank_size_com'] = peakload_com[self.c[
'max_load']].apply(lambda x: UnitConv(x).m3_gal(unit_in='gal'))
peakload_com.index = peakload_com[self.c['id']]
sol_the_gas_tank_bckp_sizes_com = pd.DataFrame(
columns=[self.c['id'], self.s['comp'], self.s['cap']],
index=peakload_com.index)
for i in peakload_com.index:
sol_the_gas_tank_bckp_sizes_com.loc[i, self.c['id']] = i
sol_the_gas_tank_bckp_sizes_com.loc[
i, self.s['comp']] = self.s['gas_tank']
sol_the_gas_tank_bckp_sizes_com.loc[i, self.s['cap']] =\
peakload_com.loc[i, 'gas_tank_size_com']
sol_the_gas_tank_bckp_sizes_com.index = \
range(sol_the_gas_tank_bckp_sizes_com.shape[0])
sol_the_inst_gas_bckp_sizes_com = pd.DataFrame(
columns=[self.c['id'], self.s['comp'], self.s['cap']],
index=peakload_com.index)
def gas_tankles_size_W(occupancy):
size = 24875. * occupancy**0.5175
return size
for i in peakload_com.index:
sol_the_inst_gas_bckp_sizes_com.loc[i, self.c['id']] = i
sol_the_inst_gas_bckp_sizes_com.loc[
i, self.s['comp']] = self.s['gas_burn']
sol_the_inst_gas_bckp_sizes_com.loc[i, self.s['cap']] = \
gas_tankles_size_W(occ_com[i - 1])
sol_the_inst_gas_bckp_sizes_com.index =\
range(sol_the_inst_gas_bckp_sizes_com.shape[0])
# instantiate systems
# individual system
self.sol_wh_indiv_new = System(
sys_params=sol_the_sys_params.loc[:last_row_for_indiv, :],
backup_params=sol_the_inst_gas_bckp_params,
sys_sizes=sol_the_sys_sizes_indiv,
backup_sizes=sol_the_inst_gas_bckp_size,
weather=self.weather,
loads=loads_indiv)
msg = 'Set up the individual solar thermal system with tankless '\
'backup test case.'
log.info(msg)
self.sol_wh_indiv_retr = System(
sys_params=sol_the_sys_params.loc[:last_row_for_indiv, :],
backup_params=sol_the_gas_tank_bckp_params,
sys_sizes=sol_the_sys_sizes_indiv,
backup_sizes=sol_the_gas_tank_bckp_size,
weather=self.weather,
loads=loads_indiv)
msg = 'Set up the individual solar thermal system with gas tank '\
'backup test case.'
log.info(msg)
# community system
self.sol_wh_com_new = System(
sys_params=sol_the_sys_params,
backup_params=sol_the_inst_gas_bckp_params,
sys_sizes=sol_the_sys_sizes_com,
backup_sizes=sol_the_inst_gas_bckp_sizes_com,
weather=self.weather,
loads=loads_com)
msg = 'Set up the community solar thermal system with tankless '\
'backup test case.'
log.info(msg)
self.sol_wh_com_retr = System(
sys_params=sol_the_sys_params,
backup_params=sol_the_gas_tank_bckp_params,
sys_sizes=sol_the_sys_sizes_com,
backup_sizes=sol_the_gas_tank_bckp_sizes_com,
weather=self.weather,
loads=loads_com)
msg = 'Set up the community solar thermal system with gas tank '\
'backup test case.'
log.info(msg)
# solar thermal validation
# individual new with
# tank and collector size from
# https://secure.solar-rating.org/Certification/Ratings/#
# RatingsReport.aspx?device=6926&units=METRICS
# solar thermal retrofit solar fractions in the rating sheets are low
# - for the same
# size of collector and storage the new and retrofit should have the
# same solar fraction
# by definition https://en.wikipedia.org/wiki/Solar_savings_fraction
# sizes based on several observed rated systems from OG-300
# with an 80 gal tank and gas tankless backup
sol_the_sys_sizes_val = pd.DataFrame(
data=[[self.s['sol_col'], 3.9],
[self.s['the_sto'],
UnitConv(80.).m3_gal(unit_in='gal')],
[self.s['sol_pump'], indiv_solar_pump_size],
[self.s['piping'], 0.]],
columns=[self.s['comp'], self.s['cap']])
self.val_sol_wh = System(
sys_params=sol_the_sys_params.loc[:last_row_for_indiv, :],
backup_params=sol_the_inst_gas_bckp_params,
sys_sizes=sol_the_sys_sizes_val,
backup_sizes=sol_the_inst_gas_bckp_size,
weather=self.weather,
loads=loads_indiv_val)
msg = 'Set up the individual solar thermal system validation '\
'test case.'
log.info(msg)
self.conv_wh = System(sys_params=gas_tank_wh_params,
sys_sizes=gas_tank_wh_size,
weather=self.weather,
loads=loads_indiv)
msg = 'Set up the conventional tank wh test case.'
log.info(msg)
# With baseline tank with a slightly higher RE and insulation set to
# R12, as used for the solar tank
gas_tank_wh_params_val = pd.DataFrame(
data=[[self.s['gas_tank'], self.s['tank_re'], .82, '-'],
[self.s['gas_tank'], self.s['ins_thi'], .04, 'm'],
[self.s['gas_tank'], self.s['spec_hea_con'], .085, 'W/mK'],
[self.s['gas_tank'], self.s['t_tap_set'], 322.04, 'K']],
columns=[
self.s['comp'], self.s['param'], self.s['param_value'],
self.s['param_unit']
])
# with validation loads and validation parameters
self.conv_wh_val = System(sys_params=gas_tank_wh_params_val,
sys_sizes=gas_tank_wh_size,
weather=self.weather,
loads=loads_indiv_val)
msg = 'Set up the conventional tank wh validation test case.'
log.info(msg)
# project level component parameter
# dataframe for solar electric system
# with an inst. gas backup
sol_el_tank_params = pd.DataFrame(
data=[[self.s['hp'], self.s['c1_cop'], 1.229E+00],
[self.s['hp'], self.s['c2_cop'], 5.549E-02],
[self.s['hp'], self.s['c3_cop'], 1.139E-04],
[self.s['hp'], self.s['c4_cop'], -1.128E-02],
[self.s['hp'], self.s['c5_cop'], -3.570E-06],
[self.s['hp'], self.s['c6_cop'], -7.234E-04],
[self.s['hp'], self.s['c1_heat_cap'], 7.055E-01],
[self.s['hp'], self.s['c2_heat_cap'], 3.945E-02],
[self.s['hp'], self.s['c3_heat_cap'], 1.433E-04],
[self.s['hp'], self.s['c4_heat_cap'], 2.768E-03],
[self.s['hp'], self.s['c5_heat_cap'], -1.069E-04],
[self.s['hp'], self.s['c6_heat_cap'], -2.494E-04],
[self.s['hp'], self.s['heat_cap_rated'], 2350.],
[self.s['hp'], self.s['cop_rated'], 2.43],
[self.s['pv'], self.s['eta_pv'], .16],
[self.s['pv'], self.s['f_act'], 1.],
[self.s['pv'], self.s['irrad_ref'], 1000.],
[self.s['inv'], self.s['eta_dc_ac'], .85],
[self.s['the_sto'], self.s['f_upper_vol'], .5],
[self.s['the_sto'], self.s['ins_thi'], .04],
[self.s['the_sto'], self.s['spec_hea_con'], .04],
[self.s['the_sto'], self.s['t_tap_set'], 322.04],
[self.s['the_sto'], self.s['h_vs_r'], 6.],
[self.s['the_sto'], self.s['dt_appr'], 2.],
[self.s['the_sto'], self.s['t_max_tank'], 344.15],
[self.s['piping'], self.s['pipe_spec_hea_con'], .0175],
[self.s['piping'], self.s['pipe_ins_thick'], .008],
[self.s['piping'], self.s['flow_factor'], .8],
[self.s['piping'], self.s['dia_len_exp'],
.43082708345352605],
[self.s['piping'], self.s['dia_len_sca'],
.007911283766743384],
[self.s['piping'], self.s['discr_diam_m'],
'[0.0127, 0.01905, 0.0254, 0.03175, 0.0381,'\
'0.0508, 0.0635, 0.0762, 0.1016]'],
[self.s['piping'], self.s['circ'], False],
[self.s['piping'], self.s['long_br_len_fr'], 1.]],
columns=[self.s['comp'], self.s['param'], self.s['param_value']])
# test sizing
sol_el_tank_sizes_indiv = pd.DataFrame(
data=[[self.s['hp'], 2350.], [self.s['pv'], 6.25],
[self.s['the_sto'], UnitConv(80).m3_gal()],
[self.s['dist_pump'], dist_pump_size],
[self.s['piping'], pipe_m_per_hhld]],
columns=[self.s['comp'], self.s['cap']])
# electric resistance backup parameters
sol_el_tank_inst_gas_bckp_params = pd.DataFrame(
data=[[self.s['el_res'], self.s['eta_el_res'], 1.0]],
columns=[self.s['comp'], self.s['param'], self.s['param_value']])
# electric resistance backup size
sol_el_tank_inst_el_res_bckp_size = pd.DataFrame(
data=[[1, self.s['el_res'], 6500.]],
columns=[self.c['id'], self.s['comp'], self.s['cap']])
self.hp_wh = System(sys_params=sol_el_tank_params,
backup_params=sol_el_tank_inst_gas_bckp_params,
sys_sizes=sol_el_tank_sizes_indiv,
backup_sizes=sol_el_tank_inst_el_res_bckp_size,
weather=self.weather,
loads=loads_indiv)