Artificial neural networks for energy analysis of office buildings with daylighting

Wong, S.L.; Wan, Kevin K.W.; Lam, Tony N.T.

doi:10.1016/j.apenergy.2009.06.028

Cited by 282 publications

(118 citation statements)

References 30 publications

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“…Afterwards, error value is calculated from the difference between the network's output and the expected output. Reputation conducted to decrease error to an acceptable value that is called epoch or training cycle [30]. The error is expressed by the root-mean-squared error value (RMSE), which can be calculated with following equation:…”

Section: Artificial Neural Network (Anns)mentioning

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%

“…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. weekends, Saturdays and Sundays) as input variables.…”

Section: Introductionmentioning

confidence: 99%

“…Various authors [23,30,36,40] performed a comparison of ANN models with building dynamic simulation software. Neto and Fiorelli [23] presented a comparison of ANN with a physical simulation tool as EnergyPlus.…”

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

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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: Artificial Neural Network (Anns)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 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

“…The method was applied in simulations to high-latitude data from detached houses and apartments in Sweden to observe their impact. Furthermore, Artificial Neural Networks (ANNs) have been extensively applied in energy systems [12,16,17]. ANNs have characteristics of optimization, generalization ability, adaptability, a legacy of information processing, failure tolerance and low power consumption [18].…”

Section: Introductionmentioning

confidence: 99%

Neural network controller for Active Demand-Side Management with PV energy in the residential sector

et al. 2012

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In this paper, we describe the development of a control system for Demand-Side Management in the residential sector with Distributed Generation. The electrical system under study incorporates local PV energy generation, an electricity storage system, connection to the grid and a home automation system. The distributed control system is composed of two modules: a scheduler and a coordinator, both implemented with neural networks. The control system enhances the local energy performance, scheduling the tasks demanded by the user and maximizing the use of local generation.

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Bibliography

2016

Data Mining and Machine Learning in Building Energy Analysis

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Artificial neural networks for energy analysis of office buildings with daylighting

Cited by 282 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

Neural network controller for Active Demand-Side Management with PV energy in the residential sector

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