comparison with the cool season . However the rooms of façade facing west ( 10A , 11A , 12A and 10B , 11B , 12B ) presented slightly higher percentages of discomfort during the entire monitored season . The six weeks period average discomfort percentage for A rooms on north orientation was 37 %, on east was 45 %, on south 37 %, and on west 48 %. Average discomfort percentage for B rooms on north was 37 %; on east was 41 %, on south 41 %, and on west 44 %. The total average discomfort percentage for all rooms was 41 %.
Table 5 : Number of discomfort hours ( DH ) and the percentage of discomfort hours ( PDH ) per |
room outside the wider comfort zone ( 7K ) for 80 % acceptability along the warm season |
Common rooms |
|
1A |
2A |
3A |
4A |
5A |
6A |
7A |
8A |
9A |
10A |
11A |
12A |
DH |
404 |
409 |
315 |
421 |
479 |
456 |
394 |
412 |
302 |
494 |
510 |
383 |
PDH |
40 % |
41 % |
31 % |
42 % |
48 % |
45 % |
39 % |
41 % |
30 % |
51 % |
53 % |
40 % |
|
|
|
|
|
|
|
Bedrooms |
|
|
|
|
|
|
1B |
2B |
3B |
4B |
5B |
6B |
7B |
8B |
9B |
10B |
11B |
12B |
DH |
369 |
410 |
338 |
346 |
451 |
447 |
509 |
356 |
372 |
426 |
505 |
329 |
PDH |
37 % |
41 % |
34 % |
34 % |
45 % |
44 % |
50 % |
35 % |
37 % |
44 % |
53 % |
34 % |
The Importance of Novel Visualisation Methods As can be evidenced from the above , the process of environmental data logging typically results in significant amounts of data . The standard presentation of this data in 2D graphs is common in many scientific and engineering disciplines , and researchers are generally highly adept at interpreting those .
However , this does not necessarily mean that other stakeholders are equally conversant with such approaches , or equally committed to investing the time and effort required in interpreting such graphs . This can be particularly important as designers , clients , facilities managers , users , and decision makers would all benefit from having a better understanding of building performance in-context .
The benefits of visualisation in general are well-established ; visualisation enables better comprehension of data , it facilitates hypothesis formation , and allows for multi-scale evaluation ( Ware 2012 ). The latter is particularly important for projects such as the one presented here , where a large number of buildings was logged and the comparison of different buildings via graphs becomes complex very quickly . Lai et al . ( 2010 ) review a number of benefits of 3D visualisation discussed in the literature , such as contribution to a user ’ s learning process , intuitive and natural appearance , sense of immersion in the environment , in the context of Environmental Impact Assessment .
Visualising the output in EnViz The collected datasets were visualised in EnViz , a research software application for the visualisation of environmental data . The software is developed in Java SE 7 , utilizing the OpenGL programming interface for the 3D graphics , as implemented via the Lightweight Java Game Library . The 3D models are imported in the COLLADA format via a custom-built parser , and the data logger output in the Excel format , using standard freely available libraries .
Approximately 250,000 measurements were parsed for the purposes of this study . The models were built in SketchUp ; each building was separated in three distinct volumes : Living Area ( Data logger 1 ), Bedroom ( Data logger 2 ), and other areas ( not logged ). The visualisation used was both
Thermal performance of industrialised housing construction in Centra 237