# The pymfm framework
# Copyright (C) 2023,
# Institute for Automation of Complex Power Systems (ACS),
# E.ON Energy Research Center (E.ON ERC),
# RWTH Aachen University
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# and associated documentation files (the "Software"), to deal in the Software without restriction,
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# sublicense, and/or sell copies of the Software, and to permit# persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies or
#substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING
# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
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from pymfm.scenario_forecast_kit.forecast_generation import generate_forecast
from pymfm.scenario_forecast_kit.scenario_generation import generate_scenario
[docs]def main():
"""
Example usage of scenario generation.
This function generates a forecast based on input data and then generates a scenario
by merging the generated forecast with additional scenario data.
:return: None
"""
forecast_input_folder = "inputs/forecast"
forecast_output_folder = "outputs/forecast"
time_resolution = 10 # in minutes
forecast_data = generate_forecast(
forecast_input_folder, forecast_output_folder, time_resolution
)
forecast_input_file = "outputs/forecast/forecast_2021-04-01.json"
scenario_input_file = "inputs/scenario/scenario_src_2021-04-01.json"
scenario_output_file = "outputs/scenario/test.json"
generate_scenario(forecast_input_file, scenario_input_file, scenario_output_file)
if __name__ == "__main__":
main()