PyLPG Electricity Model Example
This notebook demonstrates how to use PyLPG electricity model to electricity loads for multiple buildings.
Imports
Import required libraries and set visualization defaults.
import os
import pandas as pd
import matplotlib.pyplot as plt
from entise.core.generator import Generator
from entise.constants import Types
Load Data
We load building parameters from objects.csv and simulation data from the data folder.
# Load data
cwd = "." # notebook runs inside examples/electricity_demandlib
objects = pd.read_csv(os.path.join(cwd, "objects.csv"))
data = {}
common_data_folder = "../common_data"
for file in os.listdir(os.path.join(cwd, common_data_folder)):
if file.endswith(".csv"):
name = file.split(".")[0]
data[name] = pd.read_csv(
os.path.join(cwd, common_data_folder, file),
parse_dates=True,
)
print("Loaded data keys:", list(data.keys()))
Loaded data keys: ['validation_weather', 'weather']
We limit the generation to only the first building for faster generation and visualization. Feel free to expand it to more buildings by removing or changing this line.
objects = objects.iloc[:1]
Instantiate and Configure Model
Initialize the time series generator and configure it with building objects.
gen = Generator()
gen.add_objects(objects)
summary, df = gen.generate(data, workers=1)
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Working in directory: C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1
Creating
executing in C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Air Conditioning Load.House.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Apparent.HH1.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Apparent.House.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Cold Water.HH1.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Cold Water.House.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Electricity.HH1.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Electricity.House.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Gas.House.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Hot water.HH1.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Hot water.House.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Hydrogen.House.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Inner Device Heat Gains.HH1.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Inner Device Heat Gains.House.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.None.HH1.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Reactive.HH1.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Reactive.House.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Space Heating.House.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\Sum.Warm Water.HH1.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\BodilyActivityLevel.High.HH1.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\BodilyActivityLevel.Low.HH1.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\BodilyActivityLevel.Outside.HH1.json
Reading json file C:\Users\ge23nur\Documents\Python Scripts\TS_Creator\.venv\Lib\site-packages\pylpg\C1\results\Results\BodilyActivityLevel.Unknown.HH1.json
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Results Summary
Below is a summary of the annual electricity demands (in kWh/a) and peak loads (W).
print("Summary:")
summary_kwh = (summary / 1000).round(0).astype(int)
summary_kwh.rename(columns=lambda x: x.replace("[W]", "[kW]").replace("[Wh]", "[kWh]"), inplace=True)
print(summary_kwh.to_string())
Summary:
electricity:demand[kWh] electricity:load_max[kW]
1 3245 3
Preparation of Data
# Prepare data
building_id = objects["id"].iat[0]
res = df.get(building_id)
df = res[Types.ELECTRICITY]
df.index = pd.to_datetime(df.index, utc=True)
Visualization of Results
Visualize weekly electricity loads for a selected building.
def plot_weekly_profile():
# Pick the first full week starting Monday (or fallback: first 7 days)
start = df.index.min().normalize()
start = start + pd.Timedelta(days=(7 - start.weekday()) % 7)
end = start + pd.Timedelta(days=7)
s_week = df.loc[(df.index >= start) & (df.index < end)]
fig, ax = plt.subplots(figsize=(15, 5))
ax.plot(s_week.index, s_week.values, alpha=0.9)
ax.set_title(f"Building ID: {building_id} - Electricity Load (Weekly)")
ax.set_xlabel("Time")
ax.set_ylabel("Electricity load (W)")
ax.grid(True)
plt.tight_layout()
plt.show()
plot_weekly_profile()
Seasonal Electricity Loads
Next, we visualize the electricity loads for different seasons
def plot_seasonal_subplots():
df_tmp = df.copy()
df_tmp.columns = ["load"]
df_tmp["month"] = df_tmp.index.month
df_tmp["hour"] = df_tmp.index.hour
# Map month → season (no helper defs)
df_tmp["season"] = "Autumn (SON)"
df_tmp.loc[df_tmp["month"].isin([12, 1, 2]), "season"] = "Winter (DJF)"
df_tmp.loc[df_tmp["month"].isin([3, 4, 5]), "season"] = "Spring (MAM)"
df_tmp.loc[df_tmp["month"].isin([6, 7, 8]), "season"] = "Summer (JJA)"
df_tmp.loc[df_tmp["month"].isin([9, 10, 11]), "season"] = "Autumn (SON)"
prof = df_tmp.groupby(["season", "hour"])["load"].mean().reset_index()
fig, axes = plt.subplots(2, 2, figsize=(14, 8), sharex=True, sharey=True)
axes = axes.flatten()
season_order = ["Winter (DJF)", "Spring (MAM)", "Summer (JJA)", "Autumn (SON)"]
for i, season in enumerate(season_order):
ax = axes[i]
df_season = prof[prof["season"] == season]
ax.plot(df_season["hour"], df_season["load"], alpha=0.9)
ax.set_title(season)
ax.grid(True)
fig.suptitle(f"Building ID: {building_id} - Seasonal Average Daily Profiles", fontsize=14)
fig.supxlabel("Hour of day")
fig.supylabel("Electricity load (W)")
plt.tight_layout()
plt.show()
plot_seasonal_subplots()
