Tool for supporting local energy strategies: forecasting energy plans with Artificial Neural Network in Umbria Region

In order to reduce the greenhouse gas emission and to improve the energy efficiency of buildings, European Member States have to plan medium-to-long term strategies as reliable as possible. In this context, the present work aims to discuss the potentiality of Artificial Neural Network (ANN) as a sup...

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Autores principales:	Palladino Domenico, Nardi Iole
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/018bfff8ae35410b8d04bc16d1238ec0
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spelling	oai:doaj.org-article:018bfff8ae35410b8d04bc16d1238ec02021-11-08T15:18:51ZTool for supporting local energy strategies: forecasting energy plans with Artificial Neural Network in Umbria Region2267-124210.1051/e3sconf/202131202016https://doaj.org/article/018bfff8ae35410b8d04bc16d1238ec02021-01-01T00:00:00Zhttps://www.e3s-conferences.org/articles/e3sconf/pdf/2021/88/e3sconf_ati2021_02016.pdfhttps://doaj.org/toc/2267-1242In order to reduce the greenhouse gas emission and to improve the energy efficiency of buildings, European Member States have to plan medium-to-long term strategies as reliable as possible. In this context, the present work aims to discuss the potentiality of Artificial Neural Network (ANN) as a support tool for medium-to-long term forecasting analysis of energy efficiency strategies in Umbria Region (central Italy) chosen as case study. Parametric energy simulations of several archetypes buildings were carried out in compliance with the current Italian regulations by changing the form, thermal properties, boundary conditions, and technical building systems. An ANN able to forecast primary energy need was trained to forecast the energy need of building-stock of Umbria Region and to evaluate the effectiveness of several potential energy actions (such as thermal coat or technical building systems replacement) over the years. Results confirm the potential of use of ANN as a support tool in energy forecasting analysis for local Authorities. ANN is capable of forecasting different future scenarios allowing correctly planning energy actions to be implemented as well as their priority. The results open to several scenarios of interest, such as the application of the same approach at national level.Palladino DomenicoNardi IoleEDP SciencesarticleEnvironmental sciencesGE1-350ENFRE3S Web of Conferences, Vol 312, p 02016 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	Environmental sciences GE1-350
spellingShingle	Environmental sciences GE1-350 Palladino Domenico Nardi Iole Tool for supporting local energy strategies: forecasting energy plans with Artificial Neural Network in Umbria Region
description	In order to reduce the greenhouse gas emission and to improve the energy efficiency of buildings, European Member States have to plan medium-to-long term strategies as reliable as possible. In this context, the present work aims to discuss the potentiality of Artificial Neural Network (ANN) as a support tool for medium-to-long term forecasting analysis of energy efficiency strategies in Umbria Region (central Italy) chosen as case study. Parametric energy simulations of several archetypes buildings were carried out in compliance with the current Italian regulations by changing the form, thermal properties, boundary conditions, and technical building systems. An ANN able to forecast primary energy need was trained to forecast the energy need of building-stock of Umbria Region and to evaluate the effectiveness of several potential energy actions (such as thermal coat or technical building systems replacement) over the years. Results confirm the potential of use of ANN as a support tool in energy forecasting analysis for local Authorities. ANN is capable of forecasting different future scenarios allowing correctly planning energy actions to be implemented as well as their priority. The results open to several scenarios of interest, such as the application of the same approach at national level.
format	article
author	Palladino Domenico Nardi Iole
author_facet	Palladino Domenico Nardi Iole
author_sort	Palladino Domenico
title	Tool for supporting local energy strategies: forecasting energy plans with Artificial Neural Network in Umbria Region
title_short	Tool for supporting local energy strategies: forecasting energy plans with Artificial Neural Network in Umbria Region
title_full	Tool for supporting local energy strategies: forecasting energy plans with Artificial Neural Network in Umbria Region
title_fullStr	Tool for supporting local energy strategies: forecasting energy plans with Artificial Neural Network in Umbria Region
title_full_unstemmed	Tool for supporting local energy strategies: forecasting energy plans with Artificial Neural Network in Umbria Region
title_sort	tool for supporting local energy strategies: forecasting energy plans with artificial neural network in umbria region
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/018bfff8ae35410b8d04bc16d1238ec0
work_keys_str_mv	AT palladinodomenico toolforsupportinglocalenergystrategiesforecastingenergyplanswithartificialneuralnetworkinumbriaregion AT nardiiole toolforsupportinglocalenergystrategiesforecastingenergyplanswithartificialneuralnetworkinumbriaregion
_version_	1718442010501709824

Tool for supporting local energy strategies: forecasting energy plans with Artificial Neural Network in Umbria Region

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