Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

Neto, Alberto Hernandez; Fiorelli, Flávio Augusto Sanzovo

doi:10.1016/j.enbuild.2008.06.013

Cited by 514 publications

(202 citation statements)

References 11 publications

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“…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]. Ekici and Aksoy [31] implemented ANNs for prediction of building energy consumption in Turkey.…”

Section: Introductionmentioning

confidence: 99%

“…These tools are widely used for analyzing energy consumption and determining heat load of the buildings [21][22][23][24][25][26]. Boyano et al [25] studied energy demand and potential savings in buildings based on simulation results.…”

Section: Introductionmentioning

confidence: 99%

“…Neto and Fiorelli [23] presented a comparison of ANN with a physical simulation tool as EnergyPlus. In this study, climate variables such as relative humidity, external dry-bulb temperature and solar radiation were used as input variables in order to predict daily energy consumption of the building.…”

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%

“…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 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: Introductionmentioning

confidence: 99%

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

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show abstract

Optimal control of TABS using a combined method of MPC and heat load prediction

Koga

Shiraishi

2022

Japan Architectural Review

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Thermally activated building systems (TABS), which use the building frame as a radiative and thermal storage site, have attracted attention as a means of achieving a thermal environment in office spaces that combines comfort and energy conservation. However, TABS have a large thermal capacity and a slow thermal response, making it desirable to introduce a control method based on model predictive control (MPC). There are problems with this method, such as its inability to predict periodic fluctuations in thermal load. Therefore, in this study, we propose a control method combining MPC and heat load prediction using a Neural Network, a type of supervised learning in machine learning, for an office space with TABS. The method was validated by co‐simulation using CFD and MATLAB/Simulink on a one‐span (7.20 × 7.20 × 3.87 m) TABS model. As a result, the proposed method reduced the integrated water flow rate and improved the energy performance while improving the control performance.

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Bibliography

2016

Data Mining and Machine Learning in Building Energy Analysis

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Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

Cited by 514 publications

References 11 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

Optimal control of TABS using a combined method of MPC and heat load prediction

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