Calendar Heat Map

Contents

Calendar Heat Map#

Goal

Visualize daily electricity consumption as a calendar heatmap to identify patterns and anomalies over time.

Data Basis

The dataset flatElectricity.csv contains cumulative electricity meter readings at 15-minute intervals for multiple flats. We use the column FlatA_Ele, convert cumulative readings to consumption by computing differences and sum them per day. Days with missing readings stay empty in the calendar.

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()

# Daily sums of complete days only (all 96 quarter-hours have a reading):
# without min_count, days with missing data would show up as low or zero
# consumption
daily = consumption.resample("D").sum(min_count=96).dropna()

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

	timestamp	value
0	2018-12-01	14.324
1	2018-12-02	14.141
2	2018-12-03	12.444
3	2018-12-04	7.954
4	2018-12-05	5.732

Visualization#

from pyedautils.plots import plot_heatmap_calendar

fig = plot_heatmap_calendar(df, title="Electricity — Calendar Heat Map",
                            ylab="Electricity [kWh/d]")
fig.show()