Application of optimized artificial intelligence algorithm to evaluate the heating energy demand of non-residential buildings at European level

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2 Citazioni (Scopus)

Abstract

A reliable preliminary forecast of heating energy demand of a building by using a detailed dynamic simulation software typically requires an in-depth knowledge of the thermal balance, several input data and a very skilled user. The authors will describe how to use Artificial Neural Networks to predict the demand for thermal energy linked to the winter climatization of non-residential buildings. To train the neural network it was necessary to develop an accurate energy database that represents the basis of the training of a specific Artificial Neural Networks. Data came from detailed dynamic simulations performed in the TRNSYS environment. The models were built according to the standards and laws of building energy requirements in seven different European countries, for 3 cities in each country and with 13 different shape factors, obtaining 2184 detailed dynamic simulations of non-residential buildings designed with high energy performances. The authors identified the best ANN topology developing a tool for determining, both quickly and simply, the heating energy demand of a non-residential building, knowing only 12 well-known thermo-physical parameters and without any computational cost or knowledge of the thermal balance. The reliability of this approach is demonstrated by the low standard deviation less than 5 kWh/(m 2 ·year).
Lingua originaleEnglish
pagine (da-a)380-391
Numero di pagine12
RivistaEnergy
Volume176
Stato di pubblicazionePublished - 2019

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Artificial intelligence
Neural networks
Heating
Computer simulation
Thermal energy
Topology
Costs
Hot Temperature

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Pollution
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Cita questo

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title = "Application of optimized artificial intelligence algorithm to evaluate the heating energy demand of non-residential buildings at European level",
abstract = "A reliable preliminary forecast of heating energy demand of a building by using a detailed dynamic simulation software typically requires an in-depth knowledge of the thermal balance, several input data and a very skilled user. The authors will describe how to use Artificial Neural Networks to predict the demand for thermal energy linked to the winter climatization of non-residential buildings. To train the neural network it was necessary to develop an accurate energy database that represents the basis of the training of a specific Artificial Neural Networks. Data came from detailed dynamic simulations performed in the TRNSYS environment. The models were built according to the standards and laws of building energy requirements in seven different European countries, for 3 cities in each country and with 13 different shape factors, obtaining 2184 detailed dynamic simulations of non-residential buildings designed with high energy performances. The authors identified the best ANN topology developing a tool for determining, both quickly and simply, the heating energy demand of a non-residential building, knowing only 12 well-known thermo-physical parameters and without any computational cost or knowledge of the thermal balance. The reliability of this approach is demonstrated by the low standard deviation less than 5 kWh/(m 2 ·year).",
author = "{Lo Brano}, Valerio and Giuseppina Ciulla and Antonino D'Amico and Marzia Traverso",
year = "2019",
language = "English",
volume = "176",
pages = "380--391",
journal = "Energy",
issn = "0360-5442",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Application of optimized artificial intelligence algorithm to evaluate the heating energy demand of non-residential buildings at European level

AU - Lo Brano, Valerio

AU - Ciulla, Giuseppina

AU - D'Amico, Antonino

AU - Traverso, Marzia

PY - 2019

Y1 - 2019

N2 - A reliable preliminary forecast of heating energy demand of a building by using a detailed dynamic simulation software typically requires an in-depth knowledge of the thermal balance, several input data and a very skilled user. The authors will describe how to use Artificial Neural Networks to predict the demand for thermal energy linked to the winter climatization of non-residential buildings. To train the neural network it was necessary to develop an accurate energy database that represents the basis of the training of a specific Artificial Neural Networks. Data came from detailed dynamic simulations performed in the TRNSYS environment. The models were built according to the standards and laws of building energy requirements in seven different European countries, for 3 cities in each country and with 13 different shape factors, obtaining 2184 detailed dynamic simulations of non-residential buildings designed with high energy performances. The authors identified the best ANN topology developing a tool for determining, both quickly and simply, the heating energy demand of a non-residential building, knowing only 12 well-known thermo-physical parameters and without any computational cost or knowledge of the thermal balance. The reliability of this approach is demonstrated by the low standard deviation less than 5 kWh/(m 2 ·year).

AB - A reliable preliminary forecast of heating energy demand of a building by using a detailed dynamic simulation software typically requires an in-depth knowledge of the thermal balance, several input data and a very skilled user. The authors will describe how to use Artificial Neural Networks to predict the demand for thermal energy linked to the winter climatization of non-residential buildings. To train the neural network it was necessary to develop an accurate energy database that represents the basis of the training of a specific Artificial Neural Networks. Data came from detailed dynamic simulations performed in the TRNSYS environment. The models were built according to the standards and laws of building energy requirements in seven different European countries, for 3 cities in each country and with 13 different shape factors, obtaining 2184 detailed dynamic simulations of non-residential buildings designed with high energy performances. The authors identified the best ANN topology developing a tool for determining, both quickly and simply, the heating energy demand of a non-residential building, knowing only 12 well-known thermo-physical parameters and without any computational cost or knowledge of the thermal balance. The reliability of this approach is demonstrated by the low standard deviation less than 5 kWh/(m 2 ·year).

UR - http://hdl.handle.net/10447/357634

UR - https://www.sciencedirect.com/science/article/pii/S0360544219305882

M3 - Article

VL - 176

SP - 380

EP - 391

JO - Energy

JF - Energy

SN - 0360-5442

ER -