P. Identifying energy use patterns using 3 D data visualisation 

Exercise purpose:

To investigate the use of 3D data visualisation to identify patterns in energy use.

1. Run the 3D data visualisation tool.

Select '3D pattern' under the 'Exploratory’ menu.

Click the 'Connect' button at the initial pop-up window as seen below.

2. Prepare data.

For the purpose of training, a sample data set is used which consists of schools and social service buildings in a region.

Click the 'Build table' button to call the data set from a file called ‘manweb_1996_1997.csv’ which is located in the folder ‘c:\esru\entrak\sample-data\3d_data’.

(There are more sample files available in the folder.)

3. Set attributions as default.

Select the 'Attribute associations' tab at the bottom to change to 'setting attribution' mode.

You can define attribution of entities to be displayed in the 3D space.

Select values for each item as below.

Axis assignment:

X axis : Energy use (i.e. total electricity consumption, kWh/year)

Y axis : Property size (i.e. volume requiring heating, m³)

Z axis: Energy use per size (i.e. electricity use per unit heating volume, kWh / year m³)

	Code	Description	Converted Numeric Value	Attribution
Type_entity	ED SS	Education Social Service	0 1	Sphere Cone
Main_use	IN PR JU SE SS	Infant School Primary School Junior School Secondary School Social Service	0 1 2 3 4	Red Blue Green Yellow Purple

Alternatively, you can use the pre-defined attribution by clicking the 'Default set' button at the bottom of the 'Attribute associations' window. Make sure to select ‘main_use’ in ‘field for colours’. The default attribution sets it as ‘entity_type’ initially.

4. View the result of 3D graph.

To view the 3D outcome, click 'Build graph' button at the bottom of the 'Attribute associations' window.

To resize a window, drag the window edges.

To shift the view, drag the mouse in the desired direction while pressing the left  button.

To change the axis angle, drag the mouse while pressing the right button.

To zoom in/out, press the 'Alt' key and drag the mouse  while pressing the left button.

5. Interpretation.

You can interpret the outcome. Here is an example.

The secondary schools (green) have larger heating volumes than other educational properties (sphere shape). The reason for the pattern is because the secondary schools possess more facilities than the other educational properties. Other groups of educational properties such as infant schools (red ), primary schools (blue) and junior schools (green) are located in a spatial cluster near the origin. 

The group of social service properties (purple cone shape) also has a distinctive pattern. The energy consumptions of this group are generally smaller than for educational properties. The data points of the entities plotted over the whole range of the total / ht_vol axis shows that the energy performance of the entities is variable. The reason is that a variety of entity types such as day nurseries, elderly homes, training centres and shelters are included in the group.

6.Identify the detailed information of an entity.

You can identify an entity in the 3D view. Choose an entity in the 3D view which seems problematic (e.g. worst energy performer in the same group). Place the mouse prompt on the entity and click it. You can see the details of the entity (e.g. name).

The entity selected below shows the following information:

Name : Netherton Day Nursery

Type_entity: 1 (1.e. social service)

Main_use: 4(i.e. social service)

Energy use: 180,916 kWh/year

Volume: 469 m³

Energy per use: 385.7 kWh/year m³

Note that the values of ‘energy use’, ‘property size’ and ‘energy use per size’ in the figure below are meaningless since they are re-scaled and used for only 3D visualisation.

Exercise result:

 Ability to operate the 3D visualisation tool.

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