Median Weeks

Contents

Median Weeks#

Goal

Show the median weekly electricity consumption pattern as a heatmap with hours on one axis and weekdays on the other.

Data Basis

The dataset flatElectricity.csv contains cumulative electricity meter readings at 15-minute intervals. We convert cumulative readings to consumption values for FlatA_Ele and sum them per hour (complete hours only).

Data Preparation#

import pandas as pd

url = "https://raw.githubusercontent.com/retomarek/edap/main/edap/sampleData/flatElectricity.csv"
raw = pd.read_csv(url, sep=";")

# Parse timestamp and set as index
raw["time"] = pd.to_datetime(raw["time"])
raw = raw.set_index("time")

# Consumption per 15 minutes from the cumulative meter readings (kWh)
consumption = raw["FlatA_Ele"].diff()

# Hourly sums of complete hours only (all 4 quarter-hours have a reading)
hourly = consumption.resample("h").sum(min_count=4).dropna()

# Build clean 2-column DataFrame [timestamp, value]
df = hourly.reset_index()
df.columns = ["timestamp", "value"]
df.head()

	timestamp	value
0	2018-12-01 00:00:00	0.084
1	2018-12-01 01:00:00	0.073
2	2018-12-01 02:00:00	0.084
3	2018-12-01 03:00:00	0.075
4	2018-12-01 04:00:00	0.086

Visualization#

from pyedautils.plots import plot_heatmap_median_weeks

fig = plot_heatmap_median_weeks(df, title="Electricity — Median Weeks",
                                ylab="Electricity [kWh/h]")
fig.show()