def setUp(self):
np.random.seed(42)
# Set up data to use for testing (EIA example plant)
self.project = Project_EIA('./examples/operational_AEP_analysis/data')
self.project.prepare()
self.analysis = plant_analysis.MonteCarloAEP(self.project)
def test_daily_gbm(self):
reset_prng()
# ____________________________________________________________________
# Test GBM regression model (can be used at daily time resolution only)
self.analysis = plant_analysis.MonteCarloAEP(
self.project,
reanal_products=["era5"],
time_resolution="D",
reg_model="gbm",
reg_temperature=True,
reg_winddirection=False,
)
# Run Monte Carlo AEP analysis, confirm the results are consistent
self.analysis.run(num_sim=5)
sim_results = self.analysis.results
self.check_simulation_results_gbm_daily(sim_results)
def test_monthly_lin(self):
reset_prng()
# ____________________________________________________________________
# Test linear regression model, at monthly time resolution
self.analysis = plant_analysis.MonteCarloAEP(
self.project,
reanal_products=["merra2", "era5"],
time_resolution="M",
reg_model="lin",
reg_temperature=False,
reg_winddirection=False,
)
# Run Monte Carlo AEP analysis, confirm the results are consistent
self.analysis.run(num_sim=10)
sim_results = self.analysis.results
self.check_simulation_results_lin_monthly(sim_results)
def test_daily_inputs(self):
reset_prng()
# ____________________________________________________________________
# Test inputs to the regression model, at monthly time resolution
self.analysis = plant_analysis.MonteCarloAEP(
self.project,
reanal_products=["merra2", "era5"],
time_resolution="D",
reg_temperature=True,
reg_winddirection=True,
)
df = self.analysis._aggregate.df
# Check the pre-processing functions
self.check_process_revenue_meter_energy_daily(df)
self.check_process_loss_estimates_daily(df)
self.check_process_reanalysis_data_daily(df)
def test_plant_analysis(self):
# ____________________________________________________________________
# Test inputs to the regression model, at monthly time resolution
self.analysis = plant_analysis.MonteCarloAEP(
self.project,
reanal_products=['merra2', 'era5'],
time_resolution='M',
reg_temperature=True,
reg_winddirection=True)
df = self.analysis._aggregate.df
# Check the pre-processing functions
self.check_process_revenue_meter_energy_monthly(df)
self.check_process_loss_estimates_monthly(df)
self.check_process_reanalysis_data_monthly(df)
# ____________________________________________________________________
# Test inputs to the regression model, at daily time resolution
self.analysis = plant_analysis.MonteCarloAEP(
self.project,
reanal_products=['merra2', 'era5'],
time_resolution='D',
reg_temperature=True,
reg_winddirection=True)
df = self.analysis._aggregate.df
# Check the pre-processing functions
self.check_process_revenue_meter_energy_daily(df)
self.check_process_loss_estimates_daily(df)
self.check_process_reanalysis_data_daily(df)
# ____________________________________________________________________
# Test linear regression model, at monthly time resolution
self.analysis = plant_analysis.MonteCarloAEP(
self.project,
reanal_products=['merra2', 'era5'],
time_resolution='M',
reg_model='lin',
reg_temperature=False,
reg_winddirection=False)
# Run Monte Carlo AEP analysis, confirm the results are consistent
self.analysis.run(num_sim=30)
sim_results = self.analysis.results
self.check_simulation_results_lin_monthly(sim_results)
# ____________________________________________________________________
# Test linear regression model, at daily time resolution
self.analysis = plant_analysis.MonteCarloAEP(
self.project,
reanal_products=['merra2', 'era5'],
time_resolution='D',
reg_model='lin',
reg_temperature=False,
reg_winddirection=False)
# Run Monte Carlo AEP analysis, confirm the results are consistent
self.analysis.run(num_sim=30)
sim_results = self.analysis.results
self.check_simulation_results_lin_daily(sim_results)
# ____________________________________________________________________
# Test GAM regression model (can be used at daily time resolution only)
self.analysis = plant_analysis.MonteCarloAEP(
self.project,
reanal_products=['merra2', 'era5'],
time_resolution='D',
reg_model='gam',
reg_temperature=False,
reg_winddirection=True)
# Run Monte Carlo AEP analysis, confirm the results are consistent
self.analysis.run(num_sim=10)
sim_results = self.analysis.results
self.check_simulation_results_gam_daily(sim_results)
# ____________________________________________________________________
# Test GBM regression model (can be used at daily time resolution only)
self.analysis = plant_analysis.MonteCarloAEP(
self.project,
reanal_products=['merra2', 'era5'],
time_resolution='D',
reg_model='gbm',
reg_temperature=True,
reg_winddirection=True)
# Run Monte Carlo AEP analysis, confirm the results are consistent
self.analysis.run(num_sim=10)
sim_results = self.analysis.results
self.check_simulation_results_gbm_daily(sim_results)
# ____________________________________________________________________
# Test ETR regression model (can be used at daily time resolution only)
self.analysis = plant_analysis.MonteCarloAEP(
self.project,
reanal_products=['merra2', 'era5'],
time_resolution='D',
reg_model='etr',
reg_temperature=False,
reg_winddirection=False)
# Run Monte Carlo AEP analysis, confirm the results are consistent
self.analysis.run(num_sim=10)
sim_results = self.analysis.results
self.check_simulation_results_etr_daily(sim_results)