Work

Effect of passive energy retrofitting strategies by using ANN: a case of a hospital building

Year: 2025

Type: article

Abstract: This study aims to examine the effect of frequently used energy strategies, such as insulation of external walls and roof, glazing type, shading devices, and indoor air temperature of the building, by using the artificial neural networks (ANN) of the Balikesir University Hospital building. The different energy-efficient strategies were simulated after modelling and calibrating the building by way of DesignBuilder. The five other insulation materials and window types were selected, and the overhang and louvre were applied with different lengths as a shading device. Based on the strategies, ANN produced 6250 data points eligible for building construction mode and hospital building use. As a result, when comparing the model output values, ANN gives the results with a satisfactory accuracy of 99% for the estimation and test data. After analysing ANN results, when the current indoor temperature results are set, the maximum saving rate is 18.66% and 72.48% for the heating and cooling periods, respectively.

Source: Proceedings of the Institution of Civil Engineers - Energy

DOI: 10.1680/jener.23.00042

Authors: Ismail Caner, Nadir Ilten, Kadriye Ergün, Shady Attia

Institutions: University of Liège, Balıkesir University

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Sustainable cities and communities

Open Access status: green

Funder: Balikesir Üniversitesi