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An ANN-GA semantic rule-based system to reduce the gap between predicted and actual energy consumption in buildings

Yuce, Baris and Rezgui, Yacine 2017. An ANN-GA semantic rule-based system to reduce the gap between predicted and actual energy consumption in buildings. IEEE Transactions on Automation Science and Engineering 14 (3) , pp. 1351-1363. 10.1109/TASE.2015.2490141

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Abstract

This paper addresses the endemic problem of the gap between predicted and actual energy performance in public buildings. A system engineering approach is used to characterize energy performance factoring in building intrinsic properties, occupancy patterns, environmental conditions, as well as available control variables and their respective ranges. Due to the lack of historical data, a theoretical simulation model is considered. A semantic mapping process is proposed using principle component analysis (PCA) and multi regression analysis (MRA) to determine the governing (i.e., most sensitive) variables to reduce the energy gap with a (near) real-time capability. Further, an artificial neural network (ANN) is developed to learn the patterns of this semantic mapping, and is used as the cost function of a genetic algorithm (GA)-based optimization tool to generate optimized energy saving rules factoring in multiple objectives and constraints. Finally, a novel rule evaluation process is developed to evaluate the generated energy saving rules, their boundaries, and underpinning variables. The proposed solution has been tested on both a simulation platform and a pilot building - a care home in the Netherlands. Validation results suggest an average 25% energy reduction while meeting occupants' comfort conditions.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1545-5955
Funders: European Commission
Date of First Compliant Deposit: 30 March 2016
Date of Acceptance: 8 October 2015
Last Modified: 15 Jul 2019 15:01
URI: http://orca.cf.ac.uk/id/eprint/80531

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