iTCM-Datasets

These datasets are collected for the GBIC project to conduct research about indoor human thermal comfort. The GBIC research project proposes to develop online thermal comfort models via a deep-learning approach and apply them to behavioral studies to drive “greener, smarter and healthier buildings” in the tropics (e.g., Singapore). Leveraging privacy-preserving data analytics over information acquired from smartphone crowdsourcing and in-situ wearables measurements, the project plans to develop and validate an integrative, economical and scalable thermal comfort management system.

Metadata

Canonical URL: https://researchdata.ntu.edu.sg

Title: Related data for: Heterogeneous Transfer Learning for Thermal Comfort Modeling

Related Publication: Weizheng Hu, Yong Luo, Zongqing Lu, and Yonggang Wen. 2019. Heterogeneous Transfer Learning for Thermal Comfort Modeling. In Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys ’19). Association for Computing Machinery, New York, NY, USA, 61–70. doi: 10.1145/3360322.3360843

Grant Information: Building and Construction Authority (BCA) Singapore: NRF2015ENC-GBICRD001-012

Subject: Computer and Information Science; Medicine, Health and Life Sciences

Keywords: thermal comfort, heart rate, skin temperature, HVAC, air-conditioning, thermal sensation, predicted mean vote (PMV)

Instruction

Those files with name no_3_thermal are used in Buildsys 2019 paper, the reason is:

We notice that the total number of +3 (hot) votes is deficient. There are only 31 “hot” votes received from 14 participants in our datasets. Thus, we consider that these “hot” votes cannot correctly reflect most of the participants' hot sensations and decide to remove them from both iTCM datasets.

.
├── BuildSys Paper Datasets
│   ├── buildsys_19_dataset_4314.csv
│   ├── buildsys_19_dataset_person1_346.csv
│   ├── buildsys_19_dataset_person2_385.csv
│   └── buildsys_19_dataset_person3_345.csv

// Datasets without "hot" (+3) votes (Used in Buildsys 2019 paper)
│   ├── buildsys_19_dataset_4293_no_3_thermal_index.csv ()
│   ├── buildsys_19_dataset_person1_345_no_3_thermal_index.csv
│   ├── buildsys_19_dataset_person2_380_no_3_thermal_index.csv
│   ├── buildsys_19_dataset_person3_341_no_3_thermal_index.csv
├── Documents
│   ├── 1) readme.txt
│   ├── 2) Experiment Introduction.docx
│   ├── 3) Dataset Features & Codes.docx
│   └── 4) Dataset v1 Information.docx
└── README.md

2 directories, 13 files

Sample Data

You may find the explaination of each field in 3) Dataset Features & Codes.docx.

id	user_uuid	hour	age	weight	height	gender	at	rh	met	cl	hr	st	ati(-3-2)	ati(0-5)
1	1b5d595c-1bb9-4f20-a78a-217958689877	21	19	74	170	1	25.33358	55.03636	1.192368839	0.54	74	31	0	3
2	1b5d595c-1bb9-4f20-a78a-217958689877	21	19	74	170	1	25.2573745	58.3366765	1.169438669	0.54	73	32	-1	2
3	1b5d595c-1bb9-4f20-a78a-217958689877	21	19	74	170	1	25.311282	59.00144	1.207590569	0.54	92	32	0	3
4	1b5d595c-1bb9-4f20-a78a-217958689877	22	19	74	170	1	25.334916	58.009603	1.113437381	0.54	72	32	-1	2
5	1b5d595c-1bb9-4f20-a78a-217958689877	22	19	74	170	1	25.333581	56.980052	1.184626185	0.54	77	32	-1	2
6	1b5d595c-1bb9-4f20-a78a-217958689877	22	19	74	170	1	25.1385505	55.5704215	1.146804373	0.54	77	33	1	4
7	1b5d595c-1bb9-4f20-a78a-217958689877	22	19	74	170	1	25.1385505	55.5704215	1.136650669	0.54	79	33	0	3
8	1b5d595c-1bb9-4f20-a78a-217958689877	22	19	74	170	1	25.1918955	54.5402515	1.184330398	0.54	79	33	1	4
9	1b5d595c-1bb9-4f20-a78a-217958689877	22	19	74	170	1	25.261325	53.424551	1.234368697	0.54	83	32	1	4
10	1b5d595c-1bb9-4f20-a78a-217958689877	23	19	74	170	1	25.218705	53.0346485	1.174277726	0.54	80	32	0	3

Scope of Dataset (BuildSys Paper)

Dataset	Size (with hot votes)	Air Temperature Range	Relative Humidity Range
iTCM generic dataset	4293 (4314)	19.6°C - 30.6°C	37.3% - 83.6%
iTCM personal datasets	345 (346) + 380 (385) + 341 (345)	19.6°C - 29.9°C	42.4% - 75.5%

Scope of Dataset (Full Dataset)

Dataset	Size	Air Temperature Range	Relative Humidity Range
iTCM generic dataset	6689	19.6°C - 30.6°C	37.3% - 83.6%
iTCM personal datasets	346 + 385 + 345 + 448 + 385 + 374.	19.3°C - 30.0°C	37.3% - 76.2%

License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.