An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks

Buratti, Cinzia; Barbanera, Marco; Palladino, Domenico

doi:10.1016/j.apenergy.2014.01.053

Cited by 83 publications

(39 citation statements)

References 16 publications

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“…The sample data was collected using an energy database as in previous works [14][15][16][17][18][19][20][21][22][23][24][25][26]: all the energy certificates received until 2012 by the Regional Authorities were inserted in this database, already described in a previous work in which an original and useful energy index for checking the energy certificates was developed [14]. All the data included in the energy database were analyzed and used for the energy indicator calculation.…”

Section: State Of Artmentioning

confidence: 99%

“…Energy certificates are a useful tool to provide information on both building features and energy performance. An energy database was developed in which all the energy certificates received by the Regional Authorities from 2009 to 2012 were inserted [14]; the energy database contains about 6,500 energy certificates of residential and non-residential buildings.…”

Section: Introductionmentioning

confidence: 99%

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Energy Performance Database of Building Heritage in the Region of Umbria, Central Italy

et al. 2015

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Abstract:Household energy consumption has been increasing in the last decades; the residential sector is responsible for about 40% of the total final energy use in Europe. Energy efficiency measures can both reduce energy needs of buildings and energy-related CO2 emissions. For this reason, in recent years, the European Union has been making efforts to enhance energy saving in buildings by introducing various policies and strategies; in this context, a common methodology was developed to assess and to certify energy performance of buildings. The positive effects obtained by energy efficiency measures need to be verified, but measuring and monitoring building energy performance is time consuming and financially demanding. Alternatively, energy efficiency can also be evaluated by specific indicators based on energy consumption. In this work, a methodology to investigate the level of energy efficiency reached in the Umbria Region (Central Italy) is described, based on data collected by energy certificates. In fact, energy certificates, which are the outcomes of simulation models, represent a useful and available tool to collect data related to the energy use of dwellings. A database of building energy performance was developed, in which about 6500 energy certificates of residential buildings supplied by Umbria region were inserted. On the basis of this data collection, average energy and CO2 indicators related to the building heritage in Umbria were estimated and compared to national and international indicators derived from official sources. Results showed that the adopted methodology in this work can be an alternative method for the evaluation of energy indicators; in fact, the ones OPEN ACCESSEnergies 2015, 8 7262 calculated considering simulation data were similar to the ones reported in national and international sources. This allowed to validate the adopted methodology and the efficiency of European policies.

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Section: State Of Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Performance Database of Building Heritage in the Region of Umbria, Central Italy

et al. 2015

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“…Their most important feature is that ANN is not programmed but it is trained on the basis of experimental data [28][29][30]; the theory on which the Neural Network based on is already described in a previous study [15].…”

Section: Artificial Neural Network Patternmentioning

confidence: 99%

“…In some of these works [14][15][16][17][18][19][20][21][22][23] the ANN approach was also compared with other methods used for the evaluation of energy consumptions; Tso et al [18] compared three different methods (regression analysis, decision trees and Neural Network) for predicting energy consumptions, highlighting how both decision tree, and Neural Network approaches are viable alternatives to the regression method.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies were carried out for predicting the thermal behavior of buildings envelope [7][8][9][10][11][12][13] and for evaluating the energy demand [14][15][16][17][18][19][20][21][22][23] by using Artificial Neural Network (ANN). Pandey et al [7] evaluated the indoor temperature using two prototype rooms (1m × 1m × 1m) in which different cooling techniques were built and tested; specifically ANN was developed using different training functions and considering the external climate conditions (outdoor temperature, wind speed, and solar intensity).…”

Section: Introductionmentioning

confidence: 99%

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Building Behavior Simulation by Means of Artificial Neural Network in Summer Conditions

et al. 2014

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Many studies in Italy showed that buildings are responsible for about 40% of total energy consumption, due to worsening performance of building envelope; in fact, a great number of Italian buildings were built before the 1970s and 80s. In particular, the energy consumptions for cooling are considerably increased with respect to the ones for heating. In order to reduce the cooling energy demand, ensuring indoor thermal comfort, a careful study on building envelope performance is necessary. Different dynamic software could be used in order to evaluate and to improve the building envelope during the cooling period, but much time and an accurate validation of the model are required. However, when a wide experimental data is available, the Artificial Neural Network (ANN) can be an alternative, simple and fast tool to use. In the present study, the indoor thermal conditions in many dwellings built in Umbria Region were investigated in order to evaluate the envelope performance. They were recently built and have very low energy consumptions. Based on the experimental data, a feed forward network was trained, in order to evaluate the different envelopes performance. As input parameters the outdoor climatic conditions and the thermal characteristics of building envelopes were set, while, as a target parameter, the indoor air temperature was provided. A good training of network was obtained with a high regression value (0.9625) and a very small error (0.007 °C) on air temperature. The network was also used to simulate the envelope behavior with new innovative glazing systems, in order to evaluate and to improve the energy performance.

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Artificial neural network based modeling for the prediction of yield and surface area of activated carbon from biomass

Ming-yang

Kelley

Yao

2019

Biofuels Bioprod Bioref

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Activated carbon (AC) is an adsorbent material with broad industrial applications. Understanding and predicting the yield and quality of AC produced from different feedstock is critical for biomass screening and process design. In this study, multi‐layer feedforward artificial neural network (ANN) models were developed to predict the total yield and surface area of AC produced from various biomass feedstock using pyrolysis and steam activation. In total, 168 data samples identified from experiments in literature were used to train, validate, and test the ANN models. The trained ANN models showed high accuracy (R2 > 0.9) and demonstrated good alignment with the independent experimental data. The impacts of using datasets based on different biomass characterization methods (i.e., ultimate analysis and proximate analysis) were evaluated and compared. Finally, a contribution analysis was conducted to understand the impact of different process factors on AC yield and surface area. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd

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An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks

Cited by 83 publications

References 16 publications

Energy Performance Database of Building Heritage in the Region of Umbria, Central Italy

Energy Performance Database of Building Heritage in the Region of Umbria, Central Italy

Building Behavior Simulation by Means of Artificial Neural Network in Summer Conditions

Artificial neural network based modeling for the prediction of yield and surface area of activated carbon from biomass

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