Please use this identifier to cite or link to this item: https://doi.org/10.1061/41052(346)46
Title: A hybrid learning strategy for the control of window blinds
Authors: Raphael, B. 
Keywords: Building automation and control
Global optimization
Hierarchical clustering
Machine learning
Issue Date: 2009
Source: Raphael, B. (2009). A hybrid learning strategy for the control of window blinds. Proceedings of the 2009 ASCE International Workshop on Computing in Civil Engineering 346 : 462-471. ScholarBank@NUS Repository. https://doi.org/10.1061/41052(346)46
Abstract: Automated control of building systems is gaining increasing attention due to rising energy costs and enhanced environmental consciousness. Automated window blinds have the potential to save energy through striking a balance between daylighting levels and air conditioning load. Currently, most control systems for window blinds operate by setting a limit on the maximum direct solar radiation coming through the window (typically 100W/m 2) or on estimated glare discomfort. Trade-off between the energy required for artificial lighting and air conditioning is not currently evaluated through a global optimization framework. This paper presents a methodology for the control of window blinds using global optimization involving lighting and energy simulations making use of minimum number of sensors. Since these simulations take a lot of time, it is not recommended for real-time control. Instead, machine learning techniques are used to speed up the computations of optimal control actions. A combination of hierarchical clustering and perceptron networks is used to develop a generalized representation of potential solutions which are evaluated off-line. A case-study of an office building is used to evaluate the advantages of the approach. It is shown that significant improvement in efficiency is possible. The average prediction error was less than 15 for the selected case study. © 2009 ASCE.
Source Title: Proceedings of the 2009 ASCE International Workshop on Computing in Civil Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/114043
ISBN: 9780784410523
DOI: 10.1061/41052(346)46
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

51
checked on Jan 13, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.