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Inputs.py
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Inputs.py
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#Inputs
#----------
#Following report CEC NG use in Residential WH
#http://www.energy.ca.gov/2013publications/CEC-500-2013-060/CEC-500-2013-060.pdf
# Avg hot water draw in gallons/day is ~56 pp 225 of the report (given by climate zone)
#Avg increase in temp is 75...assuming 55/gal/day
import pandas as pd
import numpy as np
from numpy import *
from Inputs_Appliances_Retrofit import *
VacancyRate = 0.0 #0.072 as of 1/1/2015
# 13.9 Million 0 #s households as of 1/1/2016
#NumHHTotal = 13.9 * 10**6
PopGrowth = 0.0082 # 0.0082
DiscRate = 0.04
#---------------------------------------------------------------------
samplesize = .0025 # percentage of the 13.9 million houses that is simulated
#-----------------------------------------------------------------
SFH = 1.0 # 0.58 # *NumHH Single fam-
MFH = (1.0-SFH) # * NumHH Multi fam
SFGrowth = 0.5 # % of share of houses that are SF..PLACEHOLDER for now..same Growth for all CZs for now
P1 = 0.86 #SF with NG in ThisYear
P2 = 1.0 - P1 # SF with Elec
Q1 = 1.0 #0.9 # MF with NG
Q2 = 1.0 - Q1
#NumHH = NumHHTotal * (1-VacancyRate)
#HHThisYear = NumHH
Numcz = 16 #number of CZs 10 (actually 16, for now 10 -for ease)
NumHHCZ = {}
#dimensions of a single family home
windowPerc = 0.15
size1 = ( 42*10*4 + 25*10*4) *(1-windowPerc) # surface area of the 4 outer walls 2100 sf home 2 storied # 42 * 50 * (1-windowPerc) -1 story
size0 = windowPerc * size1
size2 = 42*25 #roof area # for 1 story 42 * 50 #
#below is for a Multi-family home
# windowPercMF = windowPerc
size11 = (38 * 10 *4 + 22 * 10*2)*(1-windowPerc) #*2 one less wall exposed to the elemetns of weather
size00 = windowPerc * size11
size22 = 38 * 22 # roof area
# #right now this is fixed
##-----------------------------------------------------------------------------
#Below are the inputs for Heating technologies
#REF- Lutz et al 2011; LBNL Using National survey data to estimate lifetimes of residential appliances
#MEanlife of furnaces are around 24-25 years
#Meanlife of heat pumps ~ 15