E3S Web Conf.
Volume 312, 202176th Italian National Congress ATI (ATI 2021)
|Number of page(s)||11|
|Section||Energy Efficiency of Buildings|
|Published online||22 October 2021|
Tool for supporting local energy strategies: forecasting energy plans with Artificial Neural Network in Umbria Region
Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), DUEE Department, 00123 via Anguillarese 301 Santa Maria di Galeria (Rome), Italy
* Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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