This document describes a visual analytics project that aims to create an interactive dashboard to monitor household energy consumption data. The dashboard will visualize the dataset using various charts like density plots, bar plots, line plots, heatmaps, box plots, and scatter plots. It will allow users to interact with the visualizations through actions like clicking, hovering, zooming, filtering, and switching. The dataset comes from a smart home dataset with weather information and contains energy usage data from different devices and weather parameters collected every second. The team's GitHub page contains all project resources.

1. Visual Analytics Project 2019/2020
Home
Energy
Monitoring
Fabio Montello 1834411
Leonardo Salvucci 1601997

2. Our goal is to create a complex but user-friendly dashboard that
allows a user to monitor household hourly, daily and weekly
consumption.
We will illustrate the visualization techniques designed to analyze
the dataset and interact with it.
Project goals

3. Pipeline Dataset
Data cleaning and preparation
Technologies
Python and d3.js
Visualization
Density plot, bar plot, line plot, heatmap, box plot, scatterplot, etc.
Actions
Click, mouseover, zoom, lateral sliding, switch etc.
Filters
Filtering datasets by user interaction

4. Dataset
Smart-home-dataset-with-weather-information
Description: https://www.kaggle.com/taranvee/smart-home-dataset-with-weather-information/
Download: https://www.kaggle.com/taranvee/smart-home-dataset-with-weather-information/download/
• Size: 124MB
• Format: CSV
• Range date: 1/01/2016 5:00:00 – 7/01/2016 00:58:29
• Parameters: Time, use [kW], gen [kW], House overall [kW], Dishwasher [kW], Furnace 1
[kW], Furnace 2 [kW], Home office [kW], Fridge [kW], Wine cellar [kW], Garage door [kW],
Kitchen 12 [kW], Kitchen 14 [kW], Kitchen 38 [kW], Barn [kW], Well [kW], Microwave [kW],
Living room [kW], Solar [kW], temperature, icon, humidity, visibility, summary, apparent
Temperature, pressure, windSpeed, cloudCover, windBearing, precipIntensity, dewPoint,
precipProbability
• Sampling: Every second
• Shape: (503.911 X 32) = 16.125.152

5. Dataset Manipulation
• Bootstrap: 1/01/2016 00:00:00 – 7/01/2016 23:59:59
• Cleaning of parameters: Time, use [kW], gen [kW], House overall [kW], Dishwasher [kW],
Furnace 1 [kW], Furnace 2 [kW], Home office [kW], Fridge [kW], Wine cellar [kW], Garage
door [kW], Kitchen 12 [kW], Kitchen 14 [kW], Kitchen 38 [kW], Barn [kW], Well [kW],
Microwave [kW], Living room [kW], Solar [kW], temperature, icon, humidity, visibility,
summary, apparente Temperature, pressure, windSpeed, cloudCover, windBearing,
precipIntensity, dewPoint, precipProbability
• Parameter re-calculation: use [kW]
• Sampling: Every minute
• Shape: (10.080 X 26) = 262.080
• CSV generation: dataset.csv, m_correlation.csv and tsne.csv

6. Overview
Dashboard Weather analysis Weekly overview

7. Charts – Density plot
Description
Weekly representation of the total
consumption by devices and the
quantity generated by solar
panels
Interactions
1) MOUSEOVER: Highlighting
of the selected parameters
2) ZOOM: Data filtering
3) SWITCH BUTTON: Switch
to consumption for individual
devices
4) BRUSH SLIDER: Side
navigation in the graph
Parameters
Gen, Use

8. Charts – Density plot
Description
Weekly representation of consumption
by single device, with different colors
Interactions
1) MOUSEOVER: Highlighting
of the selected parameters and
update label value
2) CLICK CHECKBOX:
Add/remove the device view
3) ZOOM: Data filtering
4) SWITCH BUTTON: Switch
to general consumption
5) LATERAL SLIDING: Side
navigation in the graph
Parameters
Barn, Dishwasher, Fridge, Furnace 1,
Furnace 2, Garage door, Home office,
Kitchen 12, Kitchen 14, Kitchen 38,
Living room, Microwave, Well, Wine
cellar

9. Charts – Correlation matrix
Description
Representation of the relationship
between parameters of the dataset
Interactions
1) MOUSEOVER: Highlighting
of the selected parameters
Parameters
Barn, Dishwasher, Fridge, Furnace 1,
Furnace 2 , Garage door, Home office
,Kitchen 12 ,Kitchen 14 ,Kitchen 38 ,
Living room , Microwave ,Well ,Wine
cellar ,dewPoint, gen , humidity,
precipIntensity,precipProbability,
pressure, temperature, use, visibility,
windBearing, windSpeed

10. Charts – Scatter & Parallel plot
Description
t-SNE projection on two
dimensions of weather
parameters. Color scale for energy
consumption.
Interactions
1) SCATTER PLOT AREA
SELECTION: Filtering data in the
parallel graph
2) PARALLEL COORDINATES
AXIS SELECTION: Filtering data
in the scatter plot
3) AXIS SORTING: Reordering
the axis according to user’s
interest
Parameters
dewPoint , humidity, precipIntensity,
precipProbability, pressure,
temperature, visibility, windBearing,
windSpeed

11. PCA vs t-SNE
t-distributed Stochastic Neighbor
Embedding
Principal Component Analysis
Total Variance: 0.46

12. Charts – Heat Map
Description
Weekly representation of the
device’s consumption. Each box
represents (based on the color
scale) the sum of the average of
each device’s consumption on that
day/hour
Interactions
1) MOUSEOVER: Display of
the corresponding value
2) CLICK: Opening of the bar
plot
Parameters
Barn, Dishwasher, Fridge, Furnace 1,
Furnace 2 , Garage door, Home office,
Kitchen 12 ,Kitchen 14, Kitchen 38 ,
Living room, Microwave , Well, Wine
cellar

13. Charts – Bar plot
Description
Device consumption for a specific
day/hour. Each bar represents the
average consumption of each device at
that time
Interactions
1) MOUSEOVER: Display of
the corresponding value
2) CLICK: Opening of the line
plot
3) SWITCH: Showing average
for hour of the week or for daily
consumption
4) BACK: Go to the previous
state
Parameters
Time, Barn, Dishwasher, Fridge, Furnace
1, Furnace 2 , Garage door, Home office,
Kitchen 12 ,Kitchen 14, Kitchen 38 ,
Living room, Microwave , Well, Wine
cellar

14. Charts – Line plot
Description
Representation of the device’s
consumption for a specific
hour/day. Each point of the graph
represents the consumption at the
instant considered
Interactions
1) MOUSEOVER: Display of
the exact value of time and Kw.
A dashed line is added to
facilitate comparison
2) BACK: Return to the
previous state
Parameters
Time, Barn, Dishwasher, Fridge,
Furnace 1, Furnace 2 , Garage door,
Home office, Kitchen 12 ,Kitchen 14,
Kitchen 38 , Living room, Microwave ,
Well, Wine cellar

15. Charts – Box Plot 1
Description
It represents 90% of the weekly
surveys for each device
Interactions
1) MOUSEOVER: Display of
the list of corresponding
values. Three lines are added
to facilitate comparison
between the devices
2) CLICK: View the entire
distribution and open the bar
plots, concurrently
Parameters
Barn, Dishwasher, Fridge, Furnace 1,
Furnace 2 , Garage door, Home
office, Kitchen 12, Kitchen 14,
Kitchen 38, Living room, Microwave,
Well, Wine cellar

16. Charts – Box Plot 2
Description
It represents 90% of the weekly
surveys for each device
Interactions
1) MOUSEOVER: Display of
the list of corresponding
values. Three lines are added
to facilitate comparison
between the devices
2) CLICK: Open the bar plots
3) RESET: Reset the chart
Parameters
Barn, Dishwasher, Fridge, Furnace
1, Furnace 2 , Garage door, Home
office, Kitchen 12, Kitchen 14,
Kitchen 38, Living room, Microwave,
Well, Wine cellar

17. Charts – Bar Plots
Description
Combination of bar plots for
displaying the weekly consumption
for individual device, with
corresponding consumption for hour
Parameters
Time, Barn, Dishwasher, Fridge,
Furnace 1, Furnace 2, Garage door,
Home office, Kitchen 12, Kitchen
14, Kitchen 38, Living room,
Microwave, Well, Wine cellar
Interactions
1) MOUSEOVER: Display of the
corresponding values
2) CLICK: The bar clicked is
highlighted and the corresponding
graph is updated

18. Demo

19. Useful links & Team
Leonardo Salvucci
1601997
Fabio Montello
1834411
All resources are available on GitHub:
https://github.com/fabiomontello/va-IOT-project

20. Thanks for your
attention!
Visual Analytics 2019/2020