Facade upgrades

.gitignore

@@ -52,3 +52,4 @@ node_modules/
 /test/test_samples_osw/project_national_measures_upgrade_osw
 /test/test_samples_osw/results
 /test/test_samples_osw/run*
+convert_to_json.py

CHANGELOG.md

@@ -17,6 +17,7 @@ Features
 - Add ReEDS balancing areas as a spatial field ([#591](https://github.com/NREL/resstock/pull/591))
 - Update number of bathrooms assumption to match the Building America House Simulation Protocols ([#601](https://github.com/NREL/resstock/pull/601))
 - Speed up sampling algorithm by multiple orders of magnitude for large numbers of samples ([#606](https://github.com/NREL/resstock/pull/606))
+- Use ANSI/RESNET/ICC 301 equations to calculate annual interior, exterior, and garage lighting energy ([#619](https://github.com/NREL/resstock/pull/619))
 
 Fixes
 - Fixes significant runtime bottleneck in TSV fetching in BuildExistingModel & ApplyUpgrade measures ([#543](https://github.com/NREL/resstock/pull/543))
@@ -30,6 +31,7 @@ Fixes
 - Housing characteristics fixes based on more samples in testing ([#592](https://github.com/NREL/resstock/pull/592))
 - Fixes number of bathrooms for single-family attached and multi-family buildings ([#601](https://github.com/NREL/resstock/pull/601))
 - Sync the sample probabilities after a bug fix in tsv_dist ([#609](https://github.com/NREL/resstock/pull/609))
+- Fix name of ReEDS balancing areas ([#613](https://github.com/NREL/resstock/pull/613))
 
 ## ResStock v2.4.0
 

docs/read_the_docs/source/tutorial/setup_analysis_project.rst

@@ -55,7 +55,7 @@ Apply Upgrade
 
 Each "Apply Upgrade" measure defines an upgrade scenario. An upgrade scenario is a collection of options exercised with some logic and costs applied. In the simplest case, we apply the new option to all houses. The available upgrade options are in ``resources/options_lookup.tsv`` in your git repository. 
 
-For this example, we will upgrade all windows by applying the ``Windows|Low-E, Triple, Non-metal, Air, L-Gain`` option to all houses across the country. We do this by entering that in the **Option 1** box on the Apply Upgrade measure. Also, we'll give the upgrade scenario a name: "Triple-Pane Windows" and a cost of $40/ft\ :superscript:`2` of window area by entering the number in **Option 1 Cost Value** and selecting "Window Area (ft^2)" for **Option 1 Cost Multiplier**. 
+For this example, we will upgrade all windows by applying the ``Windows|Triple, Low-E, Non-metal, Air, L-Gain`` option to all houses across the country. We do this by entering that in the **Option 1** box on the Apply Upgrade measure. Also, we'll give the upgrade scenario a name: "Triple-Pane Windows" and a cost of $40/ft\ :superscript:`2` of window area by entering the number in **Option 1 Cost Value** and selecting "Window Area (ft^2)" for **Option 1 Cost Multiplier**. 
 
 Like the **downselect logic**, excluded datapoints (i.e., datapoints for which the upgrade does not apply) will result in "completed invalid workflow". For a full explanation of how to set up the options and logic surrounding them, see :doc:`../upgrade_scenario_config`.
 

economic_analysis/Facades Economic Analysis.py

@@ -0,0 +1,155 @@
+#import packages
+import pandas as pd
+import numpy as np
+import os
+
+#set working directory
+default_dir = "C:/Users/EPRESENT/Documents/Mini Projects/Facades/ResStock Results/test10kNo3"
+
+#load utility bill costs
+df_elec_costs = pd.read_csv("C:/Users/EPRESENT/Documents/Mini Projects/Facades/Economic Analysis/Variable Elec Cost by State from EIA State Data.csv")
+df_ng_costs = pd.read_csv('C:/Users/EPRESENT/Documents/Load Shapes Project/Modeling and Calibration/Calculating Utility Bills/NG costs by state.csv')
+df_fo_costs = pd.read_csv('C:/Users/EPRESENT/Documents/Load Shapes Project/Modeling and Calibration/Calculating Utility Bills/Fuel Oil Prices Averaged by State.csv')
+df_lp_costs = pd.read_csv('C:/Users/EPRESENT/Documents/Load Shapes Project/Modeling and Calibration/Calculating Utility Bills/Propane costs by state.csv')
+
+#define constants
+btu_propane_in_one_gallon = 91452 #source: https://www.eia.gov/energyexplained/units-and-calculators/
+gallons_to_barrels = 42 #source: https://www.eia.gov/energyexplained/units-and-calculators/
+btu_fueloil_in_one_barrel = 6287000 #source: https://www.eia.gov/energyexplained/units-and-calculators/
+
+
+##define functions
+
+#choose results columns to work with
+selected_cols = [
+    'building_id', 
+    'simulation_output_report.total_site_electricity_kwh', 
+    'simulation_output_report.total_site_natural_gas_therm',
+    'simulation_output_report.total_site_fuel_oil_mbtu',
+    'simulation_output_report.total_site_propane_mbtu',
+    'simulation_output_report.total_site_energy_mbtu',
+    'simulation_output_report.upgrade_cost_usd']
+def downselect_cols(df):
+	df = df[selected_cols]
+	return df
+
+#calculate total energy difference for each upgrade, for each home, add to the mini dataframes
+def calc_delta_elec(df_upgrade, df_baseline):
+	return (df_upgrade['simulation_output_report.total_site_electricity_kwh']-df_baseline['simulation_output_report.total_site_electricity_kwh'])
+def calc_delta_ng(df_upgrade, df_baseline):
+	return (df_upgrade['simulation_output_report.total_site_natural_gas_therm']-df_baseline['simulation_output_report.total_site_natural_gas_therm'])
+def calc_delta_fo(df_upgrade, df_baseline):
+	return (df_upgrade['simulation_output_report.total_site_fuel_oil_mbtu']-df_baseline['simulation_output_report.total_site_fuel_oil_mbtu'])
+def calc_delta_lp(df_upgrade, df_baseline):
+	return (df_upgrade['simulation_output_report.total_site_propane_mbtu']-df_baseline['simulation_output_report.total_site_propane_mbtu'])
+def calc_delta_energy(df_upgrade, df_baseline):
+    return(df_upgrade['simulation_output_report.total_site_energy_mbtu']-df_baseline['simulation_output_report.total_site_energy_mbtu'])
+
+#calculate year1 utility bill savings
+def calc_year1_bill_savings(var_util_costs, delta_energy):
+	return (var_util_costs*delta_energy)
+def calc_year1_fo_bill_savings(var_util_costs, delta_energy):
+	return (delta_energy *
+            var_util_costs*
+            gallons_to_barrels*
+            (1/btu_fueloil_in_one_barrel)*
+            1000000)
+def calc_year1_lp_bill_savings(var_util_costs, delta_energy):
+    return(delta_energy *
+           var_util_costs*
+           (1/btu_propane_in_one_gallon)*
+           1000000)
+
+#calculate simple payback period
+def calc_spp(upfront_cost, year1_savings):
+	return(upfront_cost/year1_savings)
+
+#calculate npv
+analysis_period = 30
+discount_rate = 0.034
+def calc_npv(upfront_cost, year1_savings): #note: assuming 30 year lifetime for everything, which is not a general solution to this situation
+	npv_list = []
+	for cost, savings in zip(upfront_cost, year1_savings): #this loops through all the rows in the results_csv (ie each dwelling unit)
+		cost_array = [0]*(analysis_period+1)
+		cost_array[0] = cost
+		savings_array = [savings] * (analysis_period+1)
+		cash_flows = list(np.array(savings_array)-np.array(cost_array))
+		npv = 0
+		for year in range(0,analysis_period + 1):
+			npv += (1/((1 + discount_rate) ** year)) * cash_flows[year]
+		npv_list.append(npv)
+	return npv_list
+
+##Set up and run things!
+#number of upgrade packages
+num_ups = 4
+
+#load baseline results
+results_b = pd.read_parquet(os.path.join(default_dir, "results_up00.parquet"), engine = "auto")
+
+#add utility bill cost unformation to the baseline results
+results_b = pd.merge(results_b, df_elec_costs[['State', 'Variable Elec Cost $/kWh']], left_on = "build_existing_model.state", right_on = 'State', how = 'left')
+results_b = results_b.drop(['State'], 1)
+results_b = pd.merge(results_b, df_ng_costs[['State', 'NG Cost without Meter Charge [$/therm]']], left_on = "build_existing_model.state", right_on = 'State', how = 'left')
+results_b = results_b.drop(['State'], 1)
+results_b = pd.merge(results_b, df_fo_costs[['State', 'Average FO Price [$/gal]']], left_on = "build_existing_model.state", right_on = 'State', how = 'left')
+results_b = results_b.drop(['State'], 1)
+results_b = pd.merge(results_b, df_lp_costs[['State', 'Average Weekly Cost [$/gal]']], left_on = "build_existing_model.state", right_on = 'State', how = 'left')
+results_b = results_b.drop(['State'], 1)
+results_b.rename(columns={
+    'Variable Elec Cost $/kWh': 'Elec Variable Cost [$/kWh]',
+    'NG Cost without Meter Charge [$/therm]': 'NG Variable Cost [$/therm]', 
+    'Average FO Price [$/gal]': 'FO Average Cost [$/gal]',
+    'Average Weekly Cost [$/gal]': 'LP Average Price [$/gal]'},
+    inplace=True)
+
+
+#define string parts for upgrades
+file_start = "results_up"
+file_end = ".parquet"
+up_start = "up"
+
+#do economic calculations for each upgrade package
+for i in range(1, num_ups+1):
+    if i<10:
+        upnum = "0" + str(i)
+    else:
+        upnum = str(i)
+    filename = file_start + upnum + file_end
+    upgrade = up_start + upnum
+    results_up = pd.read_parquet(os.path.join(default_dir, filename), engine = "auto")
+    df = downselect_cols(results_up)
+    #calculate changes in energy use for each fuel
+    delta_elec = calc_delta_elec(results_b, df)
+    results_b[upgrade + "_delta_elec_kWh"] = delta_elec
+    delta_ng = calc_delta_ng(results_b, df)
+    results_b[upgrade + "_delta_ng_therms"] = delta_ng
+    delta_fo = calc_delta_fo(results_b, df)
+    results_b[upgrade + "_delta_fo_mmbtu"] = delta_fo
+    delta_lp = calc_delta_lp(results_b, df)
+    results_b[upgrade + "_delta_lp_mmbtu"] = delta_lp
+    delta_energy = calc_delta_elec(results_b, df)
+    results_b[upgrade + "_delta_allfuels_mmbtu"] = delta_energy
+    #calculate year1 savings for each fuel, and total
+    year1_savings_elec = calc_year1_bill_savings(results_b['Elec Variable Cost [$/kWh]'], delta_elec)
+    results_b[upgrade + "_annualbillsavings_elec_$"] = year1_savings_elec
+    year1_savings_ng = calc_year1_bill_savings(results_b['NG Variable Cost [$/therm]'], delta_ng)
+    results_b[upgrade + "_annualbillsavings_ng_$"] = year1_savings_ng
+    year1_savings_fo = calc_year1_fo_bill_savings(results_b['FO Average Cost [$/gal]'], delta_fo)
+    results_b[upgrade + "_annualbillsavings_fo_$"] = year1_savings_fo
+    year1_savings_lp = calc_year1_fo_bill_savings(results_b['LP Average Price [$/gal]'], delta_lp)
+    results_b[upgrade + "_annualbillsavings_lp_$"] = year1_savings_lp
+    year1_savings_allfuels = year1_savings_elec + year1_savings_ng + year1_savings_fo + year1_savings_lp
+    results_b[upgrade + "_annualbillsavings_allfuels_$"] = year1_savings_allfuels
+    #calculate simple payback period considering all fuels
+    simple_payback_period = calc_spp(df['simulation_output_report.upgrade_cost_usd'], year1_savings_allfuels)
+    results_b[upgrade + "_spp_allfuels_yrs"] = simple_payback_period
+    #calculate npv considering all fuels
+    npv = calc_npv(df['simulation_output_report.upgrade_cost_usd'], year1_savings_allfuels)
+    results_b[upgrade + "_npv_allfuels_$"] = npv
+
+#save results
+results_b.to_csv('results_with_economics_allfuels.csv')
+
+
+#visualizations

economic_analysis/Fuel Price Input Files/Fuel Oil Prices Averaged by State.csv

@@ -0,0 +1,52 @@
+State,Average FO Price [$/gal],Source
+AK,3.236461538,US
+AL,3.236461538,US
+AR,3.236461538,US
+AZ,3.236461538,US
+CA,3.236461538,US
+CO,3.236461538,US
+CT,3.314115385,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+DC,4.042192308,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+DE,3.127192308,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+FL,2.882423077,Lower Atlantic PADD
+GA,2.882423077,Lower Atlantic PADD
+HI,3.236461538,US
+IA,2.452692308,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+ID,3.236461538,US
+IL,2.660384615,Midwest PADD
+IN,2.753846154,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+KS,2.660384615,Midwest PADD
+KY,2.665423077,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+LA,3.236461538,US
+MA,3.269,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+MD,3.233076923,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+ME,3.013192308,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+MI,2.683692308,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+MN,2.662307692,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+MO,2.660384615,Midwest PADD
+MS,3.236461538,US
+MT,3.236461538,US
+NC,2.887115385,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+ND,2.660384615,Midwest PADD
+NE,2.528192308,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+NH,3.114807692,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+NJ,3.300769231,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+NM,3.236461538,US
+NV,3.236461538,US
+NY,3.538846154,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+OH,2.689230769,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+OK,3.236461538,US
+OR,3.236461538,US
+PA,2.872269231,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+RI,3.356269231,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+SC,2.882423077,Lower Atlantic PADD
+SD,2.660384615,Midwest PADD
+TN,3.236461538,US
+TX,3.236461538,US
+UT,3.236461538,US
+VA,2.879961538,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+VT,2.858269231,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+WA,3.236461538,US
+WI,2.692230769,https://www.eia.gov/dnav/pet/pet_pri_wfr_a_EPD2F_PRS_dpgal_w.htm
+WV,3.285538462,Central Atlantic PADD
+WY,3.236461538,US

economic_analysis/Fuel Price Input Files/NG costs by state.csv

@@ -0,0 +1,53 @@
+State,NG Cost without Meter Charge [$/therm]
+US,0.830879584
+AL,1.173929594
+AK,0.973198308
+AZ,0.905718119
+AR,0.850417794
+CA,0.937476469
+CO,0.583869659
+CT,1.267521203
+DE,0.964717461
+DC,1.068721099
+FL,1.479354089
+GA,1.239373765
+HI,3.603197324
+ID,0.451371175
+IL,0.658241947
+IN,0.675756069
+IA,0.619201987
+KS,0.724306222
+KY,0.846856452
+LA,0.780898464
+ME,1.395879731
+MD,1.024927384
+MA,1.270318428
+MI,0.649000707
+MN,0.636784599
+MS,0.764356372
+MO,0.836540341
+MT,0.530786118
+NE,0.588836098
+NV,0.675023867
+NH,1.341689274
+NJ,0.783392189
+NM,0.434452883
+NY,1.091072481
+NC,0.992095947
+ND,0.528200964
+OH,0.773916553
+OK,0.723729264
+OR,0.753906279
+PA,0.973046117
+RI,1.320026286
+SC,0.959215397
+SD,0.532388629
+TN,0.689276808
+TX,0.750002248
+UT,0.586772001
+VT,1.118904706
+VA,1.018218906
+WA,0.770398568
+WV,0.772416077
+WI,0.586606117
+WY,0.62202886

economic_analysis/Fuel Price Input Files/Propane costs by state.csv

@@ -0,0 +1,52 @@
+State,Average Weekly Cost [$/gal],Source
+AK,2.421076923,US
+AL,2.568769231,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+AR,2.185076923,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+AZ,2.421076923,US
+CA,2.421076923,US
+CO,2.116884615,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+CT,2.979115385,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+DC,3.218692308,Central Atlantic PADD
+DE,3.149307692,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+FL,4.783769231,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+GA,2.322538462,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+HI,2.421076923,US
+IA,1.329269231,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+ID,2.461,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+IL,1.635269231,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+IN,2.047576923,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+KS,1.543038462,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+KY,2.225461538,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+LA,2.448923077,Gulf Coast PADD
+MA,3.135076923,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+MD,3.275269231,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+ME,2.998384615,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+MI,2.017461538,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+MN,1.621192308,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+MO,1.770269231,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+MS,2.529192308,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+MT,1.894192308,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+NC,2.824769231,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+ND,1.423769231,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+NE,1.342846154,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+NH,3.283423077,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+NJ,3.847692308,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+NM,2.421076923,US
+NV,2.421076923,US
+NY,3.271846154,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+OH,2.671846154,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+OK,1.914346154,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+OR,2.421076923,US
+PA,3.036384615,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+RI,3.542884615,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+SC,3.228615385,Lower Atlantic PADD
+SD,1.484076923,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+TN,3.017923077,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+TX,2.437384615,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+UT,2.546384615,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+VA,3.100115385,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+VT,3.463730769,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+WA,2.421076923,US
+WI,1.573,http://www.eia.gov/dnav/pet/pet_pri_dist_a_epd2_prt_dpgal_a.htm
+WV,3.218692308,Central Atlantic PADD
+WY,2.169807692,Rocky Mountain PADD

economic_analysis/Fuel Price Input Files/Variable Elec Cost by State from EIA State Data.csv

@@ -0,0 +1,52 @@
+State,Variable Elec Cost $/kWh
+AK,0.211166119
+AL,0.116957441
+AR,0.089003122
+AZ,0.11447786
+CA,0.172740358
+CO,0.10709471
+CT,0.204215862
+DC,0.116538313
+DE,0.114961811
+FL,0.10799404
+GA,0.108707305
+HI,0.30156265
+IA,0.113057037
+ID,0.088352415
+IL,0.116169513
+IN,0.115385373
+KS,0.11589661
+KY,0.09897968
+LA,0.08983531
+MA,0.201757218
+MD,0.120907239
+ME,0.161096264
+MI,0.141719649
+MN,0.117241289
+MO,0.101934242
+MS,0.104370421
+MT,0.099662113
+NC,0.104915977
+ND,0.093995108
+NE,0.097690144
+NH,0.183788419
+NJ,0.143410923
+NM,0.109473478
+NV,0.108811236
+NY,0.162086726
+OH,0.112333519
+OK,0.093106229
+OR,0.09915016
+PA,0.126081544
+RI,0.199458158
+SC,0.120936837
+SD,0.105955422
+TN,0.10051095
+TX,0.108842605
+UT,0.090257338
+VA,0.111779166
+VT,0.158910521
+WA,0.086808067
+WI,0.126915429
+WV,0.103256296
+WY,0.100175294