# Electricity Usage Visualization

by Qiqi Zeng and Jiali Huang

To visualize the electricity usage of 45 commercial users in One Province.

Given data included:

- Electricity usage of 45 commercial users, usage sampled every 15mins;

- Local temperature;

- Location data for 45 commercial users (Latitude and longitude, collected from Google map).

The time of the data ranged from Jun.2010 to Mar.2012. However, most of the data are incomplete.

The size of data seemed to be really LARGE, and we merged the electricity usage data into a 10097*96 matrix.

We marked the users on the map (as shown below), and found it difficult to show all the points on a map zooming from the real distance.

Finally, we used a table to convey the relative position.

DRAFT:

Our design roughly included 3 parts: Map (filter), Wave chart and Rectangle matrix.

In the map, we can select the user we want to concentrated on. The rectangle matrix shows the relationship between DATA and TIME PERIOD, and the wave chart shows the details of the selected date.

At first we intended to achieve zooming the matrix by dragging a region in the graph, but finally we haven't finished this part.

### Qualitive

     RED<----->BLUE
more         less
Grey: no data

[0, MAX] had been divided into 11 parts, and we used different colors to tell from them. Thus, we can see the global data qualitively.

### Quantitive

[0, MAXintheDAY] was regarded as the y-coordination, so as to show the data quantitively.

When mouse hovered on a rectangle in the main chart, the below wave chart will show the electricty usage of the corresponding day.

### Interaction

• Click the rectangle on the map and you can know the details about that user.
• When mouse hovered on a rectangle on the map, it will be highlighted; when mouse hovered on a rectangle in the main chart, the corresponding rectangle below will be highlighted.

We can learn a lot from the visualization. Now here are some examples of the conclusion.

### Case 1

The electricity usage of a supermarket.

From the usage pattern, we can learn that the supermarket possibly opens at 9:30 am and closes at 21:30. During the Spring Festival, the supermarket adjusts its business hours. It has a higher level of usage during winter possibly due to the usage of air conditioning.

### Case 2

The electricity usage of a restaurant.

We can learn the electricity usage of the restaurant are in a high level during lunch time (12:00-13:00) and evening time (19:00 - 23:00) and in a low level during the rest of day.

### Case 3

The electricity usage of a hotel.

We can see that the hotel consumes more electricity during the night because clients mostly come to stay at night.

When the user consumes an unusual high level of electricity in some days, it will pull up the maximum and most of the data will be crushed into a small range of the total scope.