Upload modelchain_example.py

modelchain_example.py

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+"""
+The ``modelchain_example`` module shows a simple usage of the windpowerlib by
+using the :class:`~.modelchain.ModelChain` class. The modelchains are
+implemented to ensure an easy start into the Windpowerlib. They work like
+models that combine all functions provided in the library. Via parameters
+desired functions of the windpowerlib can be selected. For parameters not being
+specified default parameters are used.
+
+There are mainly three steps. First you have to import your weather data, then
+you need to specify your wind turbine, and in the last step call the
+windpowerlib functions to calculate the feed-in time series.
+
+Install the windpowerlib and optionally matplotlib to see the plots:
+
+   pip install windpowerlib
+   pip install matplotlib
+
+Go down to the "run_example()" function to start the example.
+
+SPDX-FileCopyrightText: 2019 oemof developer group <[email protected]>
+SPDX-License-Identifier: MIT
+"""
+import os
+import pandas as pd
+import requests
+import logging
+from windpowerlib import ModelChain, WindTurbine, create_power_curve
+
+try:
+    from matplotlib import pyplot as plt
+except ImportError:
+    plt = None
+
+
+def get_weather_data(filename="weather.csv", **kwargs):
+    r"""
+    Imports weather data from a file.
+
+    The data include wind speed at two different heights in m/s, air
+    temperature in two different heights in K, surface roughness length in m
+    and air pressure in Pa. The height in m for which the data applies is
+    specified in the second row.
+    In case no weather data file exists, an example weather data file is
+    automatically downloaded and stored in the same directory as this example.
+
+    Parameters
+    ----------
+    filename : str
+        Filename of the weather data file. Default: 'weather.csv'.
+
+    Other Parameters
+    ----------------
+    datapath : str, optional
+        Path where the weather data file is stored.
+        Default is the same directory this example is stored in.
+
+    Returns
+    -------
+    :pandas:`pandas.DataFrame<frame>`
+        DataFrame with time series for wind speed `wind_speed` in m/s,
+        temperature `temperature` in K, roughness length `roughness_length`
+        in m, and pressure `pressure` in Pa.
+        The columns of the DataFrame are a MultiIndex where the first level
+        contains the variable name as string (e.g. 'wind_speed') and the
+        second level contains the height as integer at which it applies
+        (e.g. 10, if it was measured at a height of 10 m). The index is a
+        DateTimeIndex.
+
+    """
+
+    if "datapath" not in kwargs:
+        kwargs["datapath"] = os.path.dirname(__file__)
+
+    file = os.path.join(kwargs["datapath"], filename)
+
+    # download example weather data file in case it does not yet exist
+    if not os.path.isfile(file):
+        logging.debug("Download weather data for example.")
+        req = requests.get("https://osf.io/59bqn/download")
+        with open(file, "wb") as fout:
+            fout.write(req.content)
+
+    # read csv file
+    weather_df = pd.read_csv(
+        file,
+        index_col=0,
+        header=[0, 1],
+        date_parser=lambda idx: pd.to_datetime(idx, utc=True),
+    )
+
+    # change time zone
+    weather_df.index = weather_df.index.tz_convert("Europe/Berlin")
+
+    return weather_df
+
+
+def initialize_wind_turbines():
+    r"""
+    Initializes three :class:`~.wind_turbine.WindTurbine` objects.
+
+    This function shows three ways to initialize a WindTurbine object. You can
+    either use turbine data from the OpenEnergy Database (oedb) turbine library
+    that is provided along with the windpowerlib, as done for the
+    'enercon_e126', or specify your own turbine by directly providing a power
+    (coefficient) curve, as done below for 'my_turbine', or provide your own
+    turbine data in csv files, as done for 'my_turbine2'.
+
+    To get a list of all wind turbines for which power and/or power coefficient
+    curves are provided execute `
+    `windpowerlib.wind_turbine.get_turbine_types()``.
+
+    Returns
+    -------
+    Tuple (:class:`~.wind_turbine.WindTurbine`,
+           :class:`~.wind_turbine.WindTurbine`,
+           :class:`~.wind_turbine.WindTurbine`)
+
+    """
+    # ************************************************************************
+    # **** Data is provided in the oedb turbine library **********************
+
+    enercon_e126 = {
+        "turbine_type": "E-126/4200",  # turbine type as in register
+        "hub_height": 135,  # in m
+    }
+    e126 = WindTurbine(**enercon_e126)
+
+    # ************************************************************************
+    # **** Specification of wind turbine with your own data ******************
+    # **** NOTE: power values and nominal power have to be in Watt
+
+    my_turbine = {
+        "nominal_power": 3e6,  # in W
+        "hub_height": 105,  # in m
+        "power_curve": pd.DataFrame(
+            data={
+                "value": [
+                    p * 1000
+                    for p in [0.0, 26.0, 180.0, 1500.0, 3000.0, 3000.0]
+                ],  # in W
+                "wind_speed": [0.0, 3.0, 5.0, 10.0, 15.0, 25.0],
+            }
+        ),  # in m/s
+    }
+    my_turbine = WindTurbine(**my_turbine)
+
+    # ************************************************************************
+    # **** Specification of wind turbine with data in own file ***************
+
+    # Read your turbine data from your data file using functions like
+    # pandas.read_csv().
+    # >>> import pandas as pd
+    # >>> my_data = pd.read_csv("path/to/my/data/file")
+    # >>> my_power = my_data["my_power"]
+    # >>> my_wind_speed = my_data["my_wind_speed"]
+
+    my_power = pd.Series(
+        [0.0, 39000.0, 270000.0, 2250000.0, 4500000.0, 4500000.0]
+    )
+    my_wind_speed = (0.0, 3.0, 5.0, 10.0, 15.0, 25.0)
+
+    my_turbine2 = {
+        "nominal_power": 6e6,  # in W
+        "hub_height": 115,  # in m
+        "power_curve": create_power_curve(
+            wind_speed=my_wind_speed, power=my_power
+        ),
+    }
+    my_turbine2 = WindTurbine(**my_turbine2)
+
+    return my_turbine, e126, my_turbine2
+
+
+def calculate_power_output(weather, my_turbine, e126, my_turbine2):
+    r"""
+    Calculates power output of wind turbines using the
+    :class:`~.modelchain.ModelChain`.
+
+    The :class:`~.modelchain.ModelChain` is a class that provides all necessary
+    steps to calculate the power output of a wind turbine. You can either use
+    the default methods for the calculation steps, as done for 'my_turbine',
+    or choose different methods, as done for the 'e126'. Of course, you can
+    also use the default methods while only changing one or two of them, as
+    done for 'my_turbine2'.
+
+    Parameters
+    ----------
+    weather : :pandas:`pandas.DataFrame<frame>`
+        Contains weather data time series.
+    my_turbine : :class:`~.wind_turbine.WindTurbine`
+        WindTurbine object with self provided power curve.
+    e126 : :class:`~.wind_turbine.WindTurbine`
+        WindTurbine object with power curve from the OpenEnergy Database
+        turbine library.
+    my_turbine2 : :class:`~.wind_turbine.WindTurbine`
+        WindTurbine object with power coefficient curve from example file.
+
+    """
+
+    # ************************************************************************
+    # **** ModelChain with non-default specifications ************************
+    modelchain_data = {
+        "wind_speed_model": "logarithmic",  # 'logarithmic' (default),
+        # 'hellman' or
+        # 'interpolation_extrapolation'
+        "density_model": "ideal_gas",  # 'barometric' (default), 'ideal_gas' or
+        # 'interpolation_extrapolation'
+        "temperature_model": "linear_gradient",  # 'linear_gradient' (def.) or
+        # 'interpolation_extrapolation'
+        "power_output_model": "power_coefficient_curve",  # 'power_curve'
+        # (default) or 'power_coefficient_curve'
+        "density_correction": True,  # False (default) or True
+        "obstacle_height": 0,  # default: 0
+        "hellman_exp": None,
+    }  # None (default) or None
+    # initialize ModelChain with own specifications and use run_model method
+    # to calculate power output
+    mc_e126 = ModelChain(e126, **modelchain_data).run_model(weather)
+    # write power output time series to WindTurbine object
+    e126.power_output = mc_e126.power_output
+
+    # ************************************************************************
+    # **** ModelChain with default parameter *********************************
+    mc_my_turbine = ModelChain(my_turbine).run_model(weather)
+    # write power output time series to WindTurbine object
+    my_turbine.power_output = mc_my_turbine.power_output
+
+    # ************************************************************************
+    # **** ModelChain with non-default value for "wind_speed_model" **********
+    mc_example_turbine = ModelChain(
+        my_turbine2, wind_speed_model="hellman"
+    ).run_model(weather)
+    my_turbine2.power_output = mc_example_turbine.power_output
+
+    return
+
+
+def plot_or_print(my_turbine, e126, my_turbine2):
+    r"""
+    Plots or prints power output and power (coefficient) curves.
+
+    Parameters
+    ----------
+    my_turbine : :class:`~.wind_turbine.WindTurbine`
+        WindTurbine object with self provided power curve.
+    e126 : :class:`~.wind_turbine.WindTurbine`
+        WindTurbine object with power curve from the OpenEnergy Database
+        turbine library.
+    my_turbine2 : :class:`~.wind_turbine.WindTurbine`
+        WindTurbine object with power coefficient curve from example file.
+
+    """
+
+    # plot or print turbine power output
+    if plt:
+        e126.power_output.plot(legend=True, label="Enercon E126")
+        my_turbine.power_output.plot(legend=True, label="myTurbine")
+        my_turbine2.power_output.plot(legend=True, label="myTurbine2")
+        plt.xlabel("Time")
+        plt.ylabel("Power in W")
+        plt.show()
+    else:
+        print(e126.power_output)
+        print(my_turbine.power_output)
+        print(my_turbine2.power_output)
+
+    # plot or print power curve
+    if plt:
+        if e126.power_curve is not False:
+            e126.power_curve.plot(
+                x="wind_speed",
+                y="value",
+                style="*",
+                title="Enercon E126 power curve",
+            )
+            plt.xlabel("Wind speed in m/s")
+            plt.ylabel("Power in W")
+            plt.show()
+        if my_turbine.power_curve is not False:
+            my_turbine.power_curve.plot(
+                x="wind_speed",
+                y="value",
+                style="*",
+                title="myTurbine power curve",
+            )
+            plt.xlabel("Wind speed in m/s")
+            plt.ylabel("Power in W")
+            plt.show()
+        if my_turbine2.power_curve is not False:
+            my_turbine2.power_curve.plot(
+                x="wind_speed",
+                y="value",
+                style="*",
+                title="myTurbine2 power curve",
+            )
+            plt.xlabel("Wind speed in m/s")
+            plt.ylabel("Power in W")
+            plt.show()
+    else:
+        if e126.power_coefficient_curve is not False:
+            print(e126.power_coefficient_curve)
+        if e126.power_curve is not False:
+            print(e126.power_curve)
+
+
+def run_example():
+    r"""
+    Runs the basic example.
+
+    """
+    # You can use the logging package to get logging messages from the
+    # windpowerlib. Change the logging level if you want more or less messages:
+    # logging.DEBUG -> many messages
+    # logging.INFO -> few messages
+    logging.getLogger().setLevel(logging.DEBUG)
+
+    weather = get_weather_data("weather.csv")
+    my_turbine, e126, my_turbine2 = initialize_wind_turbines()
+    calculate_power_output(weather, my_turbine, e126, my_turbine2)
+    plot_or_print(my_turbine, e126, my_turbine2)
+
+
+if __name__ == "__main__":
+    run_example()