eprintid: 10141464
rev_number: 12
eprint_status: archive
userid: 608
dir: disk0/10/14/14/64
datestamp: 2022-01-10 12:04:16
lastmod: 2022-01-10 12:04:16
status_changed: 2022-01-10 12:04:16
type: proceedings_section
metadata_visibility: show
creators_name: Bentley, PJ
creators_name: Lim, SL
creators_name: Jindal, S
creators_name: Narang, S
title: Generating synthetic energy usage data to enable machine learning for sustainable accommodation
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Correlation, Mechatronics, Digital twin, Computational modeling, Machine learning, Tools, Generators
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Machine Learning has the potential to discover new correlations between energy usage in apartments and variables such as seasonality, apartment location, size, efficiency and details of those staying in the apartments, thus helping apartments to become more sustainable and helping those who stay in them to use less energy. The biggest impedance to creating such ML tools is lack of viable data - without the data, the tools cannot be created - yet it is not feasible to wait for several years' worth of good data before creating the tools. Here we present a solution to this problem: the use of a digital twin to generate synthetic data. This approach is viable even when there is no existing data, but when expert knowledge about the relationship between systems exist. To achieve this, we develop a new agent-based synthetic data generator (ASDG) and explore a case study with a corporate housing and luxury alternate accommodation marketplace called TheSqua.re. We show that unlimited quantities of realistic data can be automatically generated, including data for different scenarios, and that it can be used by Machine Learning to discover the underlying correlations.
date: 2021-11-13
date_type: published
publisher: IEEE
official_url: https://doi.org/10.1109/ICECCME52200.2021.9591016
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1907598
doi: 10.1109/ICECCME52200.2021.9591016
isbn_13: 9781665412629
lyricists_name: Bentley, Peter
lyricists_name: Lim, Soo
lyricists_id: PJBEN84
lyricists_id: SLLIM63
actors_name: Bentley, Peter
actors_id: PJBEN84
actors_role: owner
full_text_status: public
publication: International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2021
place_of_pub: Mauritius, Mauritius
event_title: 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
book_title: 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
citation:        Bentley, PJ;    Lim, SL;    Jindal, S;    Narang, S;      (2021)    Generating synthetic energy usage data to enable machine learning for sustainable accommodation.                     In:  2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME).    IEEE: Mauritius, Mauritius.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10141464/1/ICECCME_v10.pdf