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Large neighborhood search for energy aware meeting scheduling in smart buildings

Lim, Boon Ping; van den Briel, Menkes; Thiebaux, Sylvie; Bent, Russell; Backhaus, Scott

Description

One of the main inefficiencies in building management systems is the widespread use of schedule-based control when operating heating, ventilation and air conditioning (HVAC) systems. HVAC systems typically operate on a pre-designed schedule that heats or cools rooms in the building to a set temperature even when rooms are not being used. Occupants, however, influence the thermal behavior of buildings. As a result, using occupancy information for scheduling meetings to occur at specific times...[Show more]

dc.contributor.authorLim, Boon Ping
dc.contributor.authorvan den Briel, Menkes
dc.contributor.authorThiebaux, Sylvie
dc.contributor.authorBent, Russell
dc.contributor.authorBackhaus, Scott
dc.coverage.spatialBarcelona, Spain
dc.date.accessioned2016-06-14T23:18:49Z
dc.date.createdMay 18-22 2015
dc.identifier.isbn9783319180076
dc.identifier.urihttp://hdl.handle.net/1885/102630
dc.description.abstractOne of the main inefficiencies in building management systems is the widespread use of schedule-based control when operating heating, ventilation and air conditioning (HVAC) systems. HVAC systems typically operate on a pre-designed schedule that heats or cools rooms in the building to a set temperature even when rooms are not being used. Occupants, however, influence the thermal behavior of buildings. As a result, using occupancy information for scheduling meetings to occur at specific times and in specific rooms has significant energy savings potential. As shown in Lim et al. [15], combining HVAC control with meeting scheduling can lead to substantial improvements in energy efficiency. We extend this work and develop an approach that scales to larger problems by combining mixed integer programming (MIP) with large neighborhood search (LNS). LNS is used to destroy part of the schedule and MIP is used to repair the schedule so as to minimize energy consumption. This approach is far more effective than solving the complete problem as a MIP problem. Our results show that solutions from the LNS-based approach are up to 36% better than the MIP-based approach when both given 15 minutes.
dc.publisherSpringer Verlag
dc.relation.ispartofseries12th International Conference on Integration of Artificial Intelligence and Operations Research techniques in Constraint Programming, CPAIOR 2015
dc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.titleLarge neighborhood search for energy aware meeting scheduling in smart buildings
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2015
local.identifier.absfor080105 - Expert Systems
local.identifier.absfor080501 - Distributed and Grid Systems
local.identifier.ariespublicationa383154xPUB2397
local.type.statusPublished Version
local.contributor.affiliationLim, Boon Ping, College of Engineering and Computer Science, ANU
local.contributor.affiliationvan den Briel, Menkes, College of Engineering and Computer Science, ANU
local.contributor.affiliationThiebaux, Sylvie, College of Engineering and Computer Science, ANU
local.contributor.affiliationBent, Russell, Los Alamos National Laboratory
local.contributor.affiliationBackhaus, Scott, Los Alamos National Laboratory
local.description.embargo2037-12-31
local.bibliographicCitation.startpage240
local.bibliographicCitation.lastpage254
local.identifier.doi10.1007/978-3-319-18008-3_17
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
dc.date.updated2016-06-14T08:29:34Z
local.identifier.scopusID2-s2.0-84929612328
CollectionsANU Research Publications

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