oemof /
oemof-solph
| Conditions | 5 |
| Total Lines | 95 |
| Code Lines | 69 |
| Lines | 0 |
| Ratio | 0 % |
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Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.
For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.
Commonly applied refactorings include:
If many parameters/temporary variables are present:
| 1 | """ |
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| 18 | def prepare_input_data(plot_resampling=False): |
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| 19 | url_temperature = ( |
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| 20 | "https://oemof.org/wp-content/uploads/2025/12/temperature.csv" |
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| 21 | ) |
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| 22 | url_energy = "https://oemof.org/wp-content/uploads/2025/12/energy.csv" |
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| 23 | |||
| 24 | print( |
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| 25 | "Data is licensed from M. Schlemminger, T. Ohrdes, E. Schneider," |
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| 26 | " and M. Knoop. Under Creative Commons Attribution 4.0 International" |
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| 27 | " License. It is also available at doi: 10.5281/zenodo.5642902." |
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| 28 | " (We use single family home 26 plus the south-facing PV" |
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| 29 | " from that dataset.)" |
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| 30 | ) |
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| 31 | |||
| 32 | file_path = Path(__file__).parent |
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| 33 | |||
| 34 | temperature_file = Path(file_path, "temperature.csv") |
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| 35 | if not temperature_file.exists(): |
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| 36 | urlretrieve(url_temperature, temperature_file) |
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| 37 | df_temperature = pd.read_csv( |
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| 38 | temperature_file, |
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| 39 | index_col="Unix Epoch", |
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| 40 | ) |
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| 41 | timedelta = np.empty(len(df_temperature)) |
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| 42 | timedelta[:-1] = ( |
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| 43 | df_temperature.index[1:] - df_temperature.index[:-1] |
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| 44 | ) / 3600 |
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| 45 | timedelta[-1] = np.nan |
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| 46 | |||
| 47 | df_temperature.index = pd.to_datetime( |
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| 48 | df_temperature.index, |
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| 49 | unit="s", |
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| 50 | utc=True, |
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| 51 | ) |
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| 52 | |||
| 53 | building_area = 120 # m² (from publication) |
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| 54 | specific_heat_demand = 60 # kWh/m²/a (educated guess) |
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| 55 | holidays = dict(Germany().holidays(2019)) |
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| 56 | |||
| 57 | # We estimate the heat demand from the ambient temperature using demandlib. |
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| 58 | # This returns energy per time step in units of kWh. |
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| 59 | df_temperature["heat demand (kWh)"] = demandlib.bdew.HeatBuilding( |
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| 60 | df_temperature.index, |
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| 61 | holidays=holidays, |
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| 62 | temperature=df_temperature["Air Temperature (°C)"], |
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| 63 | shlp_type="EFH", |
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| 64 | building_class=1, |
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| 65 | wind_class=1, |
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| 66 | annual_heat_demand=building_area * specific_heat_demand, |
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| 67 | name="EFH", |
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| 68 | ).get_bdew_profile() |
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| 69 | |||
| 70 | df_temperature["heat demand (W)"] = ( |
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| 71 | df_temperature["heat demand (kWh)"] * 1e3 / timedelta |
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| 72 | ) |
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| 73 | |||
| 74 | energy_file = Path(file_path, "energy.csv") |
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| 75 | if not energy_file.exists(): |
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| 76 | urlretrieve(url_energy, energy_file) |
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| 77 | df_energy = pd.read_csv( |
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| 78 | energy_file, |
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| 79 | index_col=0, |
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| 80 | ) |
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| 81 | df_energy.index = pd.to_datetime( |
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| 82 | df_energy.index, |
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| 83 | unit="s", |
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| 84 | utc=True, |
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| 85 | ) |
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| 86 | |||
| 87 | if plot_resampling: |
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| 88 | p_pv = {} |
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| 89 | resolutions = [ |
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| 90 | "1 min", |
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| 91 | "5 min", |
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| 92 | "10 min", |
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| 93 | "15 min", |
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| 94 | "30 min", |
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| 95 | "1 h", |
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| 96 | "2 h", |
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| 97 | "3 h", |
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| 98 | "6 h", |
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| 99 | ] |
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| 100 | |||
| 101 | for resolution in resolutions: |
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| 102 | p_pv[resolution] = df_energy["PV (W)"].resample(resolution).mean() |
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| 103 | plt.plot( |
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| 104 | np.linspace(0, 8760, len(p_pv[resolution])), |
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| 105 | sorted(p_pv[resolution]/1e3)[::-1], |
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| 106 | label=resolution, |
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| 107 | ) |
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| 108 | |||
| 109 | plt.xlim(-10, 510) |
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| 110 | plt.ylim(7, 16) |
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| 111 | plt.legend() |
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| 112 | plt.show() |
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| 113 | |||
| 117 |
