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

Domenico Palladino,Iole Nardi

doi:10.1051/e3sconf/202131202016

Abstract

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.

Highlights

Due to the human and other natural factors, CO2 emissions are significantly raised leading to an increase of global average temperatures
Validation of Artificial Neural Network: downstream of the ANN training process, the current energy need of building stock of Umbria Region (BSU) was assessed with ANN (EPglo-ANN-Current) and it was compared with EPglo-Current in order to check the reliability of the ANN itself; 5. 1st Energy Forecasting: once validated, the developed ANN was used to predict the energy need of BSU for each considered efficiency strategies (EES) (EPglo-ANN-ES1) over the years; 6
Once defined the current energy status of the BSU, the developed ANN was used for energy forecasting, by adopting six different EES and three different energy strategies (ES which refer to the criteria for selecting the renovation rate)

Summary

Introduction

Due to the human and other natural factors, CO2 emissions are significantly raised leading to an increase of global average temperatures. The adoption of focused energy strategies is an unavoidable challenge of all European Countries with the common goal of reducing fossil fuels and of increasing the energy efficiency In this framework, it is relevant to identify the most energy-intensive sectors in order to adopt appropriate energy strategies. In order to reduce the CO2 emissions and to improve buildings’ energy efficiency, different national energy strategies are undertaken, such as tax incentives or deduction with the aim of reducing total energy need of building sector of 25.5 Mtep by 2020. These energy strategies allowed an energy saving of 17.6 Mtep in 2014-2019 period [1] near the main goal but still lower than expectations

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Conclusion