Skip to content
Research Article Open access CC BY 4.0

Energy Performance and Improvement Potentials for Selected Heritage Building Adaptation in England

O. K. Akande, D. Odeleye, A. Coday, C. Jimenez Bescos

International Journal of Environment and Climate Change · pp. 189–201 · Published 2 Aug 2015

10.9734/BJECC/2015/19791

Abstract

Public heritage buildings (PHBs) were evaluated with the aim to determine their operational energy performance and the objectives of identifying improvement potentials for their long term sustainable reuse. Six listed churches initially used for worship and later converted to community uses were selected and surveyed as case study buildings using purposive sampling technique. A qualitative analytical approach based on ranking the performance of the surveyed building’s energy consumption assessment compared to others within the same geographical region was adopted. Findings show that a greater number of the surveyed buildings are low-performing with their energy use being exacerbated by the combination and interplay of multiple factors such as building use pattern, efficiency of services and lighting etc. Results of the findings imply that potential and identifiable prospects for efficiency improvements and CO2 emissions reduction exists within the operation of the buildings. Recommended actions for wide-scale improvements in the form of capital replacement, retrofit/refurbishment, behavioural and improved operational management and control were suggested. The study concluded wider opportunities towards achieving energy saving such as energy management programme, building energy refurbishment scheme and use of energy efficient equipment could enhance stainable reuse of PHBs.

Energy performance public heritage buildings sustainability conservation.

Cited by 4

On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings

E. M. Abdelkader, N. Elshaboury, Abobakr Al-Sakkaf · International Journal of Applied Metaheuristic Computing · 2022

A Comprehensive Comparative Analysis of Machine Learning Models for Predicting Heating and Cooling Loads

E. M. Abdelkader, Abobakr Al-Sakkaf, Reem Ahmed · Current Approaches in Science and Technology Research Vol. 6 · 2021

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

4

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.