Diagnostic tools of energy performance for supermarkets using Artificial Neural Network algorithms
File(s)
Accepted version
Author(s)
Mavromatidis, G
Acha, S
Shah, N
Type
Journal Article
Abstract
Supermarket performance monitoring is of vital importance to ensure systems perform adequately and guarantee operating costs and energy use are kept at a minimum. Furthermore, advanced monitoring techniques can allow early detection of equipment faults that could disrupt store operation. This paper details the development of a tool for performance monitoring and fault detection for supermarkets focusing on evaluating the Store's Total Electricity Consumption as well as individual systems, such as Refrigeration, HVAC, Lighting and Boiler. Artificial Neural Network (ANN) models are developed for each system to provide the energy baseline, which is modelled as a dependency between the energy consumption and suitable explanatory variables. The tool has two diagnostic levels. The first level broadly evaluates the systems performance, in terms of energy consumption, while the second level applies more rigorous criteria for fault detection of supermarket subsystems. A case study, using data from a store in Southeast England, is presented and results show remarkable accuracy for calculating hourly energy use, thus marking the ANN method as a viable tool for diagnosis purposes. Finally, the generic nature of the methodology approach allows the development and application to other stores, effectively offering a valuable analytical tool for better running of supermarkets.
Date Issued
2013-03-21
Date Acceptance
2013-03-09
Citation
Energy and Buildings, 2013, 62, pp.304-314
ISSN
1872-6178
Publisher
Elsevier
Start Page
304
End Page
314
Journal / Book Title
Energy and Buildings
Volume
62
Copyright Statement
© 2013 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
Sainsbury's Supermarkets Ltd
Grant Number
n/a
Subjects
Science & Technology
Technology
Construction & Building Technology
Energy & Fuels
Engineering, Civil
Engineering
CONSTRUCTION & BUILDING TECHNOLOGY
ENERGY & FUELS
ENGINEERING, CIVIL
Supermarket energy use
Energy forecasting
Diagnostics
Fault detection
Predicative maintenance
Artificial neural networks
PREDICTION
SYSTEMS
CONSUMPTION
Building & Construction
09 Engineering
12 Built Environment And Design
Publication Status
Published