def run_as_script():
import cea.globalvar as gv
import cea.inputlocator as inputlocator
gv = gv.GlobalVariables()
scenario_path = gv.scenario_reference
locator = inputlocator.InputLocator(scenario_path=scenario_path)
output_parameters = ['QHf_MWhyr', 'QCf_MWhyr', 'Ef_MWhyr', 'QEf_MWhyr']
method = 'sobol' # method
samples = 1000
graph(locator, output_parameters, method, samples)
def run_as_script():
import cea.globalvar as gv
import cea.inputlocator as inputlocator
gv = gv.GlobalVariables()
scenario_path = gv.scenario_reference
locator = inputlocator.InputLocator(scenario_path=scenario_path)
weather_path = locator.get_default_weather()
output_parameters = ['QHf_MWhyr', 'QCf_MWhyr', 'Ef_MWhyr', 'QEf_MWhyr']
method = 'morris'
groups_var = ['THERMAL']
num_samples = 1000 # generally 1000 or until it converges
sensitivity_main(locator, weather_path, gv, output_parameters, groups_var,
num_samples, method)
"""
===========================
Energyplus file reader
===========================
File history and credits:
C. Miller script development 10.08.14
J. A. Fonseca adaptation for CEA tool 18.05.16
J. A. Fonseca adding sky temperature Epuls model 20.10.16
"""
import pandas as pd
import cea.inputlocator
import cea.globalvar as globalvar
import numpy as np
gv = globalvar.GlobalVariables()
__author__ = "Clayton Miller"
__copyright__ = "Copyright 2014, Architecture and Building Systems - ETH Zurich"
__credits__ = ["Clayton Miller", "Jimeno A. Fonseca"]
__license__ = "MIT"
__version__ = "0.1"
__maintainer__ = "Daren Thomas"
__email__ = "*****@*****.**"
__status__ = "Production"
def epw_reader(weather_path):
epw_labels = [
'year', 'month', 'day', 'hour', 'minute', 'datasource', 'drybulb_C',
'dewpoint_C', 'relhum_percent', 'atmos_Pa', 'exthorrad_Whm2',
def run_as_script():
import cea.globalvar as gv
import cea.inputlocator as inputlocator
gv = gv.GlobalVariables()
scenario_path = gv.scenario_reference
locator = inputlocator.InputLocator(scenario=scenario_path)
#Options
optimize = False
raw_data_plot = True
multicriteria = False
plot_pareto = False
clustering = True
cluster_plot = True
building_names = [
'dorm', 'lab', 'office'
] #['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B09']#['B01']#['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B09']
building_load = 'Ef_kWh'
type_data = 'measured'
if optimize:
for name in building_names:
data = demand_CEA_reader(locator=locator,
building_name=name,
building_load=building_load,
type=type_data)
start_generation = None # or the number of generation to start from
number_individuals = 16
number_generations = 50
optimization_clustering_main(locator=locator,
data=data,
start_generation=start_generation,
number_individuals=number_individuals,
number_generations=number_generations,
building_name=name,
gv=gv)
if multicriteria:
for i, name in enumerate(building_names):
generation = 50
weight_fitness1 = 100 # accurracy
weight_fitness2 = 100 # complexity
weight_fitness3 = 70 # compression
what_to_plot = "paretofrontier"
output_path = locator.get_calibration_cluster_mcda(generation)
# read_checkpoint
input_path = locator.get_calibration_cluster_opt_checkpoint(
generation, name)
result = mcda_cluster_main(input_path=input_path,
what_to_plot=what_to_plot,
weight_fitness1=weight_fitness1,
weight_fitness2=weight_fitness2,
weight_fitness3=weight_fitness3)
result["name"] = name
if i == 0:
result_final = pd.DataFrame(result).T
else:
result_final = result_final.append(pd.DataFrame(result).T,
ignore_index=True)
result_final.to_csv(output_path)
if plot_pareto:
for name in building_names[:1]:
data = demand_CEA_reader(locator=locator,
building_name=name,
building_load=building_load,
type=type_data)
days_of_analysis = len(data)
generation_to_plot = 50
annotate_benchmarks = True
annotate_fitness = False
show_in_screen = False
save_to_disc = True
what_to_plot = "paretofrontier" #paretofrontier, halloffame, or population
optimal_individual = pd.read_csv(
locator.get_calibration_cluster_mcda(generation_to_plot))
optimal_individual = optimal_individual.loc[
optimal_individual["name"] == name]
labelx = 'Accurracy (A) [-]'
labely = 'Complexity (B) [-]'
labelz = r'Compression ($\Gamma$) [-]'
output = os.path.join(
locator.get_calibration_clustering_plots_folder(),
"plot_gen_" + str(generation_to_plot) + "_building_name_" +
name + ".png")
# read_checkpoint
input_path = locator.get_calibration_cluster_opt_checkpoint(
generation_to_plot, name)
frontier_2D_3OB(
input_path=input_path,
what_to_plot=what_to_plot,
output_path=output,
labelx=labelx,
labely=labely,
labelz=labelz,
days_of_analysis=days_of_analysis,
show_benchmarks=annotate_benchmarks,
show_fitness=annotate_fitness,
show_in_screen=show_in_screen,
save_to_disc=save_to_disc,
optimal_individual=optimal_individual,
)
if clustering:
name = 'lab'
data = demand_CEA_reader(locator=locator,
building_name=name,
building_load=building_load,
type=type_data)
word_size = 4
alphabet_size = 17
clustering_sax(locator=locator,
data=data,
word_size=word_size,
alphabet_size=alphabet_size,
gv=gv)
if cluster_plot:
show_benchmark = True
save_to_disc = True
show_in_screen = False
show_legend = False
labelx = "Hour of the day"
labely = "Electrical load [kW]"
input_path = locator.get_calibration_cluster('clusters_mean')
data = pd.read_csv(input_path)
output_path = os.path.join(
locator.get_calibration_clustering_plots_folder(),
"w_a_" + str(word_size) + "_" + str(alphabet_size) +
"_building_name_" + name + ".png")
plot_day(
data=data,
output_path=output_path,
labelx=labelx,
labely=labely,
save_to_disc=save_to_disc,
show_in_screen=show_in_screen,
show_legend=show_legend) #, show_benchmark=show_benchmark)
if raw_data_plot:
name = 'lab'
show_benchmark = True
save_to_disc = True
show_in_screen = False
show_legend = True
labelx = "Hour of the day"
labely = "Electrical load [kW]"
data = demand_CEA_reader(locator=locator,
building_name=name,
building_load=building_load,
type=type_data)
data = pd.DataFrame(
dict((str(key), value) for (key, value) in enumerate(data)))
# input_path = locator.get_calibration_cluster('clusters_mean')
output_path = os.path.join(
locator.get_calibration_clustering_plots_folder(),
"raw_building_name_" + name + ".png")
plot_day(data=data,
output_path=output_path,
labelx=labelx,
labely=labely,
save_to_disc=save_to_disc,
show_in_screen=show_in_screen,
show_legend=show_legend) # , show_benchmark=show_benchmark)
def sensAnalysis(locator, extraCosts, extraCO2, extraPrim, solarFeat, ntwFeat, gen):
"""
:param locator: path to input locator
:param extraCosts: costs calculated before optimization of specific energy services
(process heat and electricity)
:param extraCO2: green house gas emissions calculated before optimization of specific energy services
(process heat and electricity)
:param extraPrim: primary energy calculated before optimization ofr specific energy services
(process heat and electricity)
:param solarFeat: solar features call to class
:param ntwFeat: network features call to class
:param gen: generation on which the sensitivity analysis is performed
:type locator: string
:type extraCosts: float
:type extraCO2: float
:type extraPrim: float
:type solarFeat: class
:type ntwFeat: class
:type gen: int
:return: returns the most sensitive parameter among the group provided
:rtype: string
"""
gV = glob.GlobalVariables()
step = gV.sensibilityStep
bandwidth = sensBandwidth()
os.chdir(locator.get_optimization_master_results_folder())
pop, eps, testedPop, ntwList, fitness = sFn.readCheckPoint(locator, gen, 0)
toolbox = base.Toolbox()
total_demand = pd.read_csv(locator.get_total_demand())
buildList = total_demand.Name.values
FactorResults = np.zeros((step + 1, bandwidth.nFactors * 2))
def sensOneFactor(obj, factor_name, mini, maxi, colNumber):
iniValue = getattr(obj, factor_name)
index = 0
for delta in np.arange(mini, maxi + 1E-5, (maxi-mini)/step):
FactorResults[index][colNumber + 0] = delta
if abs(delta) > 1E-10:
setattr(obj, factor_name, iniValue * (1+delta))
newpop = []
for ind in pop:
newInd = toolbox.clone(ind)
newpop.append(newInd)
(costs, CO2, prim) = eI.evaluation_main(newInd, buildList, locator, solarFeat, ntwFeat, obj,,
newInd.fitness.values = (costs, CO2, prim)
index += 1
setattr(obj, factor_name, iniValue)
sensOneFactor(gV, 'ELEC_PRICE', bandwidth.minElec, bandwidth.maxElec, 0)
sensOneFactor(gV, 'NG_PRICE', bandwidth.minNG, bandwidth.maxNG, 2)
sensOneFactor(gV, 'BG_PRICE', bandwidth.minBG, bandwidth.maxBG, 4)
indexSensible = FactorResults.argmax()%(2*bandwidth.nFactors)
if indexSensible == 1:
mostSensitive = 'Electricity price'
elif indexSensible == 3:
mostSensitive = 'NG price'
else:
mostSensitive = 'BG price'
print FactorResults
print mostSensitive
return mostSensitive
gen = 4
def run_as_script(scenario_path=None):
import cea.globalvar
import pandas as pd
import cea.optimization.distribution.network_opt_main as network_opt
from cea.optimization.preprocessing.preprocessing_main import preproccessing
gv = cea.globalvar.GlobalVariables()
if scenario_path is None:
scenario_path = gv.scenario_reference
locator = cea.inputlocator.InputLocator(scenario_path=scenario_path)
total_demand = pd.read_csv(locator.get_total_demand())
building_names = total_demand.Name.values
gv.num_tot_buildings = total_demand.Name.count()
weather_file = locator.get_default_weather()
extraCosts, extraCO2, extraPrim, solarFeat = preproccessing(locator, total_demand, building_names,
weather_file, gv)
ntwFeat = network_opt.network_opt_main()
sensAnalysis(locator, extraCosts, extraCO2, extraPrim, solarFeat, ntwFeat, gen)
print 'sensitivity analysis succeeded'
if __name__ == '__main__':
run_as_script(r'C:\reference-case-zug\baseline')
def sensAnalysis(locator, extraCosts, extraCO2, extraPrim, solarFeat, ntwFeat,
gen):
"""
:param locator: path to input locator
:param extraCosts: costs calculated before optimization of specific energy services
(process heat and electricity)
:param extraCO2: green house gas emissions calculated before optimization of specific energy services
(process heat and electricity)
:param extraPrim: primary energy calculated before optimization ofr specific energy services
(process heat and electricity)
:param solarFeat: solar features call to class
:param ntwFeat: network features call to class
:param gen: generation on which the sensitivity analysis is performed
:type locator: string
:type extraCosts: float
:type extraCO2: float
:type extraPrim: float
:type solarFeat: class
:type ntwFeat: class
:type gen: int
:return: returns the most sensitive parameter among the group provided
:rtype: string
"""
gV = glob.GlobalVariables()
step = gV.sensibilityStep
bandwidth = sensBandwidth()
os.chdir(locator.get_optimization_master_results_folder())
pop, eps, testedPop, ntwList, fitness = sFn.readCheckPoint(locator, gen, 0)
toolbox = base.Toolbox()
total_demand = pd.read_csv(locator.get_total_demand())
buildList = total_demand.Name.values
FactorResults = np.zeros((step + 1, bandwidth.nFactors * 2))
def sensOneFactor(obj, factor_name, mini, maxi, colNumber):
iniValue = getattr(obj, factor_name)
index = 0
for delta in np.arange(mini, maxi + 1E-5, (maxi - mini) / step):
FactorResults[index][colNumber + 0] = delta
if abs(delta) > 1E-10:
setattr(obj, factor_name, iniValue * (1 + delta))
newpop = []
for ind in pop:
newInd = toolbox.clone(ind)
newpop.append(newInd)
(costs, CO2, prim) = eI.evaluation_main(
newInd, buildList, locator, extraCosts, extraCO2,
extraPrim, solarFeat, ntwFeat, obj)
newInd.fitness.values = (costs, CO2, prim)
index += 1
setattr(obj, factor_name, iniValue)
sensOneFactor(gV, 'ELEC_PRICE', bandwidth.minElec, bandwidth.maxElec, 0)
sensOneFactor(gV, 'NG_PRICE', bandwidth.minNG, bandwidth.maxNG, 2)
sensOneFactor(gV, 'BG_PRICE', bandwidth.minBG, bandwidth.maxBG, 4)
indexSensible = FactorResults.argmax() % (2 * bandwidth.nFactors)
if indexSensible == 1:
mostSensitive = 'Electricity price'
elif indexSensible == 3:
mostSensitive = 'NG price'
else:
mostSensitive = 'BG price'
print FactorResults
print mostSensitive
return mostSensitive
