def setUp(self):
'''Setup for each test.
'''
resources_dir = os.path.join(testing_root_dir, 'models', 'Resources')
self.gen = Data_Generator(resources_dir)
class DataGeneratorTest(unittest.TestCase, utilities.partialTimeseries):
'''Tests the data generator class
'''
def setUp(self):
'''Setup for each test.
'''
resources_dir = os.path.join(testing_root_dir, 'models', 'Resources')
self.gen = Data_Generator(resources_dir)
def test_generate_weather(self):
'''Runs the generate weather data method and compares
trajectories with references.
'''
# Generate weather file
self.gen.generate_weather()
os.chdir(testing_root_dir)
# Set generated file path
gen_filepath = os.path.join(self.gen.resources_dir, 'weather.csv')
# Set reference file path
ref_filepath = os.path.join(testing_root_dir, 'references', 'data',
'default_weather.csv')
# Check the data file has been created
self.assertTrue(os.path.exists(gen_filepath))
# Read data into data frame
df_gen = pd.read_csv(gen_filepath).set_index('time')
# Check trajectories
self.compare_ref_timeseries_df(df_gen, ref_filepath)
def test_generate_prices(self):
'''Runs the generate prices method and compares trajectories
with references.
'''
# Generate weather file
self.gen.generate_prices()
# Set generated file path
gen_filepath = os.path.join(self.gen.resources_dir, 'prices.csv')
# Set reference file path
ref_filepath = os.path.join(testing_root_dir, 'references', 'data',
'default_prices.csv')
# Check the data file has been created
self.assertTrue(os.path.exists(gen_filepath))
# Read data into data frame
df_gen = pd.read_csv(gen_filepath).set_index('time')
# Check trajectories
self.compare_ref_timeseries_df(df_gen, ref_filepath)
def test_generate_emissions(self):
'''Runs the generate emissions method and compares
trajectories with references.
'''
# Generate weather file
self.gen.generate_emissions()
# Set generated file path
gen_filepath = os.path.join(self.gen.resources_dir, 'emissions.csv')
# Set reference file path
ref_filepath = os.path.join(testing_root_dir, 'references', 'data',
'default_emissions.csv')
# Check the data file has been created
self.assertTrue(os.path.exists(gen_filepath))
# Read data into data frame
df_gen = pd.read_csv(gen_filepath).set_index('time')
# Check trajectories
self.compare_ref_timeseries_df(df_gen, ref_filepath)
def test_generate_occupancy(self):
'''Runs the generate occupancy method and compares
trajectories with references.
'''
# Generate weather file
self.gen.generate_occupancy(occ_num=10)
# Set generated file path
gen_filepath = os.path.join(self.gen.resources_dir, 'occupancy.csv')
# Set reference file path
ref_filepath = os.path.join(testing_root_dir, 'references', 'data',
'default_occupancy.csv')
# Check the data file has been created
self.assertTrue(os.path.exists(gen_filepath))
# Read data into data frame
df_gen = pd.read_csv(gen_filepath).set_index('time')
# Check trajectories
self.compare_ref_timeseries_df(df_gen, ref_filepath)
def test_generate_internalGains(self):
'''Runs the generate internal gains method and compares
trajectories with references.
'''
# Generate weather file
self.gen.generate_internalGains()
# Set generated file path
gen_filepath = os.path.join(self.gen.resources_dir,
'internalGains.csv')
# Set reference file path
ref_filepath = os.path.join(testing_root_dir, 'references', 'data',
'default_internalGains.csv')
# Check the data file has been created
self.assertTrue(os.path.exists(gen_filepath))
# Read data into data frame
df_gen = pd.read_csv(gen_filepath).set_index('time')
# Check trajectories
self.compare_ref_timeseries_df(df_gen, ref_filepath)
def test_generate_setpoints(self):
'''Runs the generate setpoints method for testcase 2 and
compares trajectories with references.
'''
# Generate weather file
self.gen.generate_setpoints()
# Set generated file path
gen_filepath = os.path.join(self.gen.resources_dir, 'setpoints.csv')
# Set reference file path
ref_filepath = os.path.join(testing_root_dir, 'references', 'data',
'default_setpoints.csv')
# Check the data file has been created
self.assertTrue(os.path.exists(gen_filepath))
# Read data into data frame
df_gen = pd.read_csv(gen_filepath).set_index('time')
# Check trajectories
self.compare_ref_timeseries_df(df_gen, ref_filepath)
@author: Javier Arroyo ''' from data.data_generator import Data_Generator import os import pandas as pd from pymodelica import compile_fmu from pyfmi import load_fmu from scipy import interpolate # Set the location of the Resource directory relative to this file location file_dir = os.path.dirname(os.path.realpath(__file__)) resources_dir = os.path.join(file_dir, 'Resources') # Create data generator object with time interval to 15 minutes gen = Data_Generator(resources_dir, period=900) # Generate weather data from .mos in Resources folder with default values gen.generate_weather() #===================================================================== # Generate prices #===================================================================== # Electricity prices obtained from: https://www.energyprice.be/products-list/Engie # for the "Easy Indexed" deal for electricity, on June 2020 # More info in https://www.energyprice.be/blog/2017/11/06/electricity-price-belgium/ # and in https://www.energyprice.be/blog/2017/10/23/electricity-off-peak-hours/ # For the highly dynamic scenario, the Belgian day-ahead prices of 2019 are used. # Obtained from: # https://my.elexys.be/MarketInformation/SpotBelpex.aspx # And stored as downloaded in /Resources/BelpexFilter.xlsx
@author: Javier Arroyo ''' from data.data_generator import Data_Generator import os import pandas as pd from pymodelica import compile_fmu from pyfmi import load_fmu from scipy import interpolate import numpy as np # Set the location of the Resource directory relative to this file location file_dir = os.path.dirname(os.path.realpath(__file__)) resources_dir = os.path.join(file_dir, 'Resources') # Create data generator object with time interval of 5 minutes gen = Data_Generator(resources_dir, period=300) # Generate weather data from .mos in Resources folder with default values gen.generate_weather() #===================================================================== # Generate prices #===================================================================== # Electricity prices obtained from: # https://particuliers.engie.fr/electricite/contrat-electricite/contrat-elec-ajust.html # for the Engie's "Elec Ajust" deal before taxes (HTT = 'hors taxes', TTC = 'taxes compris') # for electricity the price before taxes is chosen to allow comparison against # the highly dynamic price scenario that uses spot prices. # The tariff used is the one for households with contracted power installations # higher than 6 kVA. # For the highly dynamic scenario, the French day-ahead prices of 2019 are used.
from data.data_generator import Data_Generator
import os
# Set the location of the Resource directory relative to this file location
file_dir = os.path.dirname(os.path.realpath(__file__))
resources_dir = os.path.join(file_dir, 'Resources')
# Create data generator object with time interval to 15 minutes
gen = Data_Generator(resources_dir, period=900)
# Generate weather data from .mos in Resources folder with default values
gen.generate_weather()
# Generate emission factors data in Denmark
gen.generate_emissions(
emissions_district_heating_power=0.1163,
#kg CO2e/kWh
#obtained from https://www.banktrack.org/download/carbon_accounting_report_2018/dnb_asa_carbon_footprint_report_2018.pdf
emissions_electric_power=0.2090
#kg CO2e/kWh
# obtained from https://www.carbonfootprint.com/docs/2019_06_emissions_factors_sources_for_2019_electricity.pdf
)
#=====================================================================
# Generate prices
#=====================================================================
# Electricity prices obtained from Nordpool DK2: average for Feb, 2019
# https://www.nordpoolgroup.com/Market-data1/Dayahead/Area-Prices/DK/Yearly/?view=table
#
# Dynamic price obtained from Nordpool DK 1: 12 Feb, 2019, peak and off peak price of the selected day
# https://www.nordpoolgroup.com/Market-data1/Dayahead/Area-Prices/DK/Hourly/?view=table
# highly dynamic price obtained from Nord pool DK1: day-ahead price, hourly price on 12 Feb, 2019, repeat electricity price of the selected day
# https://www.nordpoolgroup.com/Market-data1/Dayahead/Area-Prices/DK/Hourly/?view=chart
''' Created on Sep 18, 2019 @author: Javier Arroyo ''' from data.data_generator import Data_Generator import os # Set the location of the Resource directory relative to this file location file_dir = os.path.dirname(os.path.realpath(__file__)) resources_dir = os.path.join(file_dir, 'Resources') # Create data generator object with time interval to 15 minutes gen = Data_Generator(resources_dir, period=900) # Generate weather data from .mos in Resources folder with default values gen.generate_weather()