# (if its not, this could go negative)
gas_energy = gas_energy - (non_heat_gas / 365.0)
# temporary hack to test effect of scaling instead on
# gas_energy = gas_energy * (space_and_water_gas[year] / total_gas)
# print(gas_energy.head(7))
print('GAS ENERGY SMALLEST')
print(gas_energy.nsmallest())
# account for boiler efficiency of 90%
# (this is inconsistent as 80% is used for the EU buildings figures)
gas_energy = gas_energy * 0.8
# optionally convert to net demand
if net:
electric = readers.read_electric(electric_filename)
# factor the generation to match the demand
# (imagine we added just enough solar and wind annually and then we can
# look at net to see how much storage we might need )
electric_total = electric['EMBEDDED_WIND_GENERATION'] + electric[
'EMBEDDED_SOLAR_GENERATION']
electric_total = electric_total * (gas_energy.sum() / electric_total.sum())
print(electric_total.head())
print(electric_total.tail())
# convert all to net demand
gas_energy = gas_energy - electric_total
space_and_water = space_and_water - electric_total
watson_space_and_water = watson_space_and_water - electric_total
hdd_space_and_water15 = hdd_space_and_water15 - electric_total
hdd_space_and_water12 = hdd_space_and_water12 - electric_total
# create mean to represent whole country
era_total = era_cf.sum(axis=1) / len(locations)
midas_total = midas_cf.sum(axis=1) / len(locations)
print(era_total)
print(midas_total)
# read in the ninja PV cf's for 2018
ninja = readers.read_ninja_country(ninja_filename_cfs)
ninja = ninja['2018-01-01 00:00:00':'2018-12-31 23:00:00']
# convert to hourly by summing
ninja = ninja.resample('D').mean()
print(ninja)
# read in national grid embedded generation and capacity
national_grid = readers.read_electric(national_grid_filename)
print(national_grid)
# and calculate capacity factor.
national_grid_cf = national_grid['EMBEDDED_SOLAR_GENERATION'] / national_grid[
'EMBEDDED_SOLAR_CAPACITY']
print(national_grid_cf)
# plot all 3
era_total.plot(label='era5')
midas_total.plot(label='midas')
ninja['national'].plot(label='ninja')
national_grid_cf.plot(label='national grid')
plt.title('2018 PV power capacity factors')
plt.xlabel('Day of the year')
plt.ylabel('Capacity factor')
plt.legend(loc='upper right')
# water - water (100%)
ashp_water = water * 0.9 / dt_water.apply(cop_ashp)
# (10%) GSHP = 10.29 - 0.21 DT + 0.0012 DT2
# space - radiator (90%)
gshp_space_radiator = space * 0.1 * 0.9 / dt_radiator.apply(cop_gshp)
# space - floor (10%)
gshp_space_floor = space * 0.1 * 0.1 / dt_floor.apply(cop_gshp)
# water - water (10%)
gshp_water = water * 0.1 / dt_water.apply(cop_gshp)
electric_heat = ashp_space_radiator + ashp_space_floor + ashp_water + gshp_space_radiator + gshp_space_floor + gshp_water
print(electric_heat)
# read electric demand
electric = readers.read_electric(supply_filename)
# factor the generation to match the demand
# (imagine we added just enough solar and wind annually and then we can
# look at net to see how much storage we might need )
supply = electric['EMBEDDED_WIND_GENERATION'] + electric[
'EMBEDDED_SOLAR_GENERATION']
# print('Annual supply before up sample {}'.format(supply.sum()) )
# supply = supply.resample('60min').pad()
# supply = upsample_df(supply.resample,'60min')
# print('Annual supply after up sample {}'.format(supply.sum()) )
# supply = supply.resample('60min')
print('SUPPLY')
print(supply.index)
print(supply)
supply = supply * (electric_heat.sum() / supply.sum())
# output plots
gas.plot(label='Gas Energy', fontsize=15)
heat.plot(label='Heat Demand', fontsize=15)
electric.plot(label='Electricity', fontsize=15)
plt.title('Electricity gas and heat 2018')
plt.xlabel('Day of the year', fontsize=15)
plt.ylabel('Daily Demand (TWh)', fontsize=15)
plt.legend(loc='upper right', fontsize=16)
plt.show()
# generation
year = '2018'
generation_filename = '/home/malcolm/uclan/data/ElectricityDemandData_' + year + '.csv'
generation = readers.read_electric(generation_filename)
pv = generation['EMBEDDED_SOLAR_GENERATION']
wind = generation['EMBEDDED_WIND_GENERATION']
pv = pv.resample('D').sum()
wind = wind.resample('D').sum()
pv = pv['2018-01-01':'2018-05-30']
wind = wind['2018-01-01':'2018-05-30']
gas.plot(label='Gas Energy', fontsize=15)
heat.plot(label='Heat Demand', fontsize=15)
electric.plot(label='Electricity', fontsize=15)
wind.plot(label='Wind Generation', fontsize=15)
pv.plot(label='Solar Generation', fontsize=15)
plt.title('Electricity gas and heat 2018')
plt.xlabel('Day of the year', fontsize=15)
plt.ylabel('Daily Demand (TWh)', fontsize=15)