Diagnostic tools of energy performance for supermarkets using Artificial Neural Network algorithms

Mavromatidis, Georgios; Acha, Salvador; Shah, Nilay

doi:10.1016/j.enbuild.2013.03.020

Cited by 77 publications

(50 citation statements)

References 30 publications

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“…The graphics clearly show that the training of the model was successfully accomplished since the model fits with software data. This case is also supported by the fact that R 2 values were very close to 1 [14,31,32,34,51].…”

Section: Ann Modelsupporting

confidence: 68%

“…weekends, Saturdays and Sundays) as input variables. Day of the week, hour of the day, external temperature and store average temperature were selected as input variables by Mavromatidis et al [32]. Also, Chou and Bui [37] proposed the prediction of the heating and cooling load of buildings by ANNs with 8 inputs (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing distribution).…”

Section: Introductionmentioning

confidence: 99%

“…They are not programmed in the traditional way but they are trained using past history data representing the behaviour of a system [14]. ANNs can be defined as the learning, understanding and thinking ability of computers [10] that have been widely used for a range of applications in the area of energy modelling [11,[30][31][32][33][34][35][36][37]. Several studies published on predicting energy consumption, cooling load and heat load of buildings using ANN methods [23,[30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

“…As a conclusion; when the calculated values compared with the outputs of the network, it was proven that ANN gives satisfactory results with deviation of 3.43% and successful prediction rate of 94.8-98.5%. Mavromatidis et al [32] implemented an ANN model for prediction of energy performance of a supermarket building. Day of the week, hour of the day, external temperature and store average temperature were selected as input variables.…”

Section: Introductionmentioning

confidence: 99%

“…Many authors [9,11,23,30,32,36,37] considered various input parameters for their models to estimate energy consumption and heat load. Hygh et al [9] used 27 building parameters including orientation, aspect ratio and shading projection while Wong et al [30] used external weather conditions (daily average dry-bulb temperature, daily average wet-bulb temperature, daily global solar radiation and daily average clearness index), building envelope designs (solar aperture, daylight aperture, overhang and side-fins projections) and day time (i.e.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation

Turhan

Kazanasmaz

Uygun

et al. 2014

Energy and Buildings

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a b s t r a c tThe several parameters affect the heat load of a building; geometry, construction, layout, climate and the users. These parameters are complex and interrelated. Comprehensive models are needed to understand relationships among the parameters that can handle non-linearities. The aim of this study is to predict heat load of existing buildings benefiting from width/length ratio, wall overall heat transfer coefficient, area/volume ratio, total external surface area, total window area/total external surface area ratio by using artificial neural networks and compare the results with a building energy simulation tool called KEP-IYTE-ESS developed by Izmir Institute of Technology. A back propagation neural network algorithm has been preferred and both simulation tools were applied to 148 residential buildings selected from 3 municipalities of Izmir-Turkey. Under the given conditions, a good coherence was observed between artificial neural network and building energy simulation tool results with a mean absolute percentage error of 5.06% and successful prediction rate of 0.977. The advantages of ANN model over the energy simulation software are observed as the simplicity, the speed of calculation and learning from the limited data sets.

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Section: Ann Modelsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation

Turhan

Kazanasmaz

Uygun

et al. 2014

Energy and Buildings

View full text Add to dashboard Cite

show abstract

Enhancing Corporate Environmental Performance Through Reporting and Roadmaps

2013

Self Cite

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Managing the carbon footprint of companies and addressing their respective decarbonisation plans is a challenging endeavour. The aim of this study is to help companies' better understand the issues around decarbonisation and environmental performance by suggesting a holistic management process they could embark on. This process comprises two crucial steps which are: a) sustainability reporting and b) low-carbon roadmaps. These steps are covered and further developed based on a detailed study of the UK food retail sector. This sector is relevant due to its economical and environmental importance, but most importantly it has a significant record of available environmental reports in the public domain and a large potential to influence consumers, policy makers, and multiple supply chains. Sustainability reporting is assessed by analysing environmental KPIs disclosed in Corporate Social Responsibility (CSR) reports and then these are compared against industry standards. This analysis highlights a general lack of consistency and transparency in CSR reporting of UK food retailers. Consequently, a low carbon roadmap based on relevant KPIs and on the 'backasting' framework is presented as a case study in order to showcase how a hypothetical UK food retailer can employ a low carbon roadmap to meet strategic targets. The case study demonstrates that ambitious environmental targets are achievable if robust corporate action plans are followed and steered adequately to meet long term targets. Furthermore, the case study also indicates capital might be misallocated in favour of highly visible environmental stores and on-site energy generation technologies with expensive carbon abatement potential whilst in theory more could be done by applying cost-effective energy efficiency measures which have the potential to deliver substantial carbon savings. Highlights: General lack of consistency and transparency in CSR reporting of UK food retailer  In average, no real improvement of transparency in CSR reporting since 2005  Energy saving measures have a greater potential compared to on-site generation  A learning process from the lighthouse store results is required  Ambitious environmental targets are achievable

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Fault Detection and Diagnosis as a Predictor of Energy Consumption in Special-Purpose Buildings

Alghanmi,

Akilu,

Qiao

2024

Lecture Notes in Energy

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Diagnostic tools of energy performance for supermarkets using Artificial Neural Network algorithms

Cited by 77 publications

References 30 publications

Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation

Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation

Enhancing Corporate Environmental Performance Through Reporting and Roadmaps

Fault Detection and Diagnosis as a Predictor of Energy Consumption in Special-Purpose Buildings

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