Enhancing the smart readiness of buildings : Combining Collective intelligence and Reinforcement learning in Building Energy Management
Hosseini, Mohammad ; Mazaheri, Ahmad and Nik, Vahid M. LU
(2024)
11th BuildSim Nordic Conference, BuildSim Nordic 2024
562.
- Abstract
This research introduces a novel Energy Management approach, named CIRLEM, aiming to enhance the smartness of buildings by focusing on technical systems operations, environmental variations, and occupants' needs. Deployed in a simulated environment using Building Performance Simulation and Python integration, the study evaluates CIRLEM's performance under future extreme cold weather scenarios, employing a set of representative climate data. The pilot case, two building blocks in Sweden, undergoes assessment for energy demand, peak power, and thermal comfort. Results indicate that CIRLEM, particularly when driven by demand and price signals, effectively reduces energy demand and costs, demonstrating strong adaptability to extreme weather... (More)
This research introduces a novel Energy Management approach, named CIRLEM, aiming to enhance the smartness of buildings by focusing on technical systems operations, environmental variations, and occupants' needs. Deployed in a simulated environment using Building Performance Simulation and Python integration, the study evaluates CIRLEM's performance under future extreme cold weather scenarios, employing a set of representative climate data. The pilot case, two building blocks in Sweden, undergoes assessment for energy demand, peak power, and thermal comfort. Results indicate that CIRLEM, particularly when driven by demand and price signals, effectively reduces energy demand and costs, demonstrating strong adaptability to extreme weather conditions. Thermal comfort is maintained regarding the temperature limits and variations. Ongoing developments attempt to refine the reward function and signal generation for thermal comfort enhancement and real-world implementation.
(Less)
- author
- Hosseini, Mohammad
; Mazaheri, Ahmad
and Nik, Vahid M.
LU
- organization
- publishing date
- 2024-08
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- E3S Web of Conferences
- volume
- 562
- publisher
- EDP Sciences
- conference name
- 11th BuildSim Nordic Conference, BuildSim Nordic 2024
- conference location
- Espoo, Finland
- conference dates
- 2024-06-09 - 2024-06-11
- external identifiers
-
- scopus:85201409657
- DOI
- 10.1051/e3sconf/202456210004
- language
- English
- LU publication?
- yes
- id
- dc08d64d-6e5f-4196-afca-21413b13b2ca
- date added to LUP
- 2024-10-28 13:17:40
- date last changed
- 2025-04-04 15:19:51
@inproceedings{dc08d64d-6e5f-4196-afca-21413b13b2ca,
abstract = {{<p>This research introduces a novel Energy Management approach, named CIRLEM, aiming to enhance the smartness of buildings by focusing on technical systems operations, environmental variations, and occupants' needs. Deployed in a simulated environment using Building Performance Simulation and Python integration, the study evaluates CIRLEM's performance under future extreme cold weather scenarios, employing a set of representative climate data. The pilot case, two building blocks in Sweden, undergoes assessment for energy demand, peak power, and thermal comfort. Results indicate that CIRLEM, particularly when driven by demand and price signals, effectively reduces energy demand and costs, demonstrating strong adaptability to extreme weather conditions. Thermal comfort is maintained regarding the temperature limits and variations. Ongoing developments attempt to refine the reward function and signal generation for thermal comfort enhancement and real-world implementation.</p>}},
author = {{Hosseini, Mohammad and Mazaheri, Ahmad and Nik, Vahid M.}},
booktitle = {{E3S Web of Conferences}},
language = {{eng}},
publisher = {{EDP Sciences}},
title = {{Enhancing the smart readiness of buildings : Combining Collective intelligence and Reinforcement learning in Building Energy Management}},
url = {{http://dx.doi.org/10.1051/e3sconf/202456210004}},
doi = {{10.1051/e3sconf/202456210004}},
volume = {{562}},
year = {{2024}},
}