Empowering energy flexibility and climate resilience using collective intelligence based demand side management (CI-DSM)
Nik, Vahid M. LU
and Moazami, Amin
(2021)
8th International Building Physics Conference, IBPC 2021
2069.
- Abstract
This work investigates the effectiveness of Collective intelligence (CI) in demand side management (DSM) in urban areas to cope with extreme climate events. CI is a form of distributed intelligence that emerges in collaborative problem solving and decision making. It is used in a simulation platform to control the energy performance of buildings in an urban area in Stockholm, through developing CI-DSM and setting certain adaptation measures, including phase shifting in HVAC systems and building appliances. CI-DSM is developed based on a simple communication strategy among buildings, using forward (1) and backward (0) signals, corresponding to applying and disapplying the adaptation measures. The performance of CI-DSM is simulated for... (More)
This work investigates the effectiveness of Collective intelligence (CI) in demand side management (DSM) in urban areas to cope with extreme climate events. CI is a form of distributed intelligence that emerges in collaborative problem solving and decision making. It is used in a simulation platform to control the energy performance of buildings in an urban area in Stockholm, through developing CI-DSM and setting certain adaptation measures, including phase shifting in HVAC systems and building appliances. CI-DSM is developed based on a simple communication strategy among buildings, using forward (1) and backward (0) signals, corresponding to applying and disapplying the adaptation measures. The performance of CI-DSM is simulated for three climate scenarios representing typical, extreme cold and extreme warm years in Stockholm. According to the results, CI-DSM increases the autonomy and agility of the system in responding to climate shocks without the need for computationally extensive central decision making systems. CI-DSM helps to gradually and effectively decrease the energy demand and absorb the shock during extreme climate events.
(Less)
- author
- Nik, Vahid M.
LU
and Moazami, Amin
- organization
- publishing date
- 2021
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Journal of Physics: Conference Series : 8th International Building Physics Conference, IBPC 2021 - 8th International Building Physics Conference, IBPC 2021
- volume
- 2069
- edition
- 1
- conference name
- 8th International Building Physics Conference, IBPC 2021
- conference location
- Copenhagen, Virtual, Denmark
- conference dates
- 2021-08-25 - 2021-08-27
- external identifiers
-
- scopus:85121444499
- DOI
- 10.1088/1742-6596/2069/1/012149
- project
- Collective Intelligence for Energy Flexibility
- language
- English
- LU publication?
- yes
- id
- 6573647b-91f6-4a57-8694-ee43c55e2ea1
- date added to LUP
- 2022-01-26 12:14:14
- date last changed
- 2024-01-20 20:39:13
@inproceedings{6573647b-91f6-4a57-8694-ee43c55e2ea1,
abstract = {{<p>This work investigates the effectiveness of Collective intelligence (CI) in demand side management (DSM) in urban areas to cope with extreme climate events. CI is a form of distributed intelligence that emerges in collaborative problem solving and decision making. It is used in a simulation platform to control the energy performance of buildings in an urban area in Stockholm, through developing CI-DSM and setting certain adaptation measures, including phase shifting in HVAC systems and building appliances. CI-DSM is developed based on a simple communication strategy among buildings, using forward (1) and backward (0) signals, corresponding to applying and disapplying the adaptation measures. The performance of CI-DSM is simulated for three climate scenarios representing typical, extreme cold and extreme warm years in Stockholm. According to the results, CI-DSM increases the autonomy and agility of the system in responding to climate shocks without the need for computationally extensive central decision making systems. CI-DSM helps to gradually and effectively decrease the energy demand and absorb the shock during extreme climate events.</p>}},
author = {{Nik, Vahid M. and Moazami, Amin}},
booktitle = {{Journal of Physics: Conference Series : 8th International Building Physics Conference, IBPC 2021}},
language = {{eng}},
title = {{Empowering energy flexibility and climate resilience using collective intelligence based demand side management (CI-DSM)}},
url = {{http://dx.doi.org/10.1088/1742-6596/2069/1/012149}},
doi = {{10.1088/1742-6596/2069/1/012149}},
volume = {{2069}},
year = {{2021}},
}