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Article
Publication date: 15 March 2024

Obed Ofori Yemoh, Richard Opoku, Gabriel Takyi, Ernest Kwadwo Adomako, Felix Uba and George Obeng

This study has assessed the thermal performance of locally fabricated bio-based building envelopes made of coconut and corn husk composite bricks to reduce building wall heat…

Abstract

Purpose

This study has assessed the thermal performance of locally fabricated bio-based building envelopes made of coconut and corn husk composite bricks to reduce building wall heat transmission load and energy consumption towards green building adaptation.

Design/methodology/approach

Samples of coconut fiber (coir) and corn husk fiber bricks were fabricated and tested for their thermophysical properties using the Transient Plane Source (TPS) 2500s instrument. A simulation was conducted using Dynamic Energy Response of Building - Lunds Tekniska Hogskola (DEROB-LTH) to determine indoor temperature variation over 24 h. The time lag and decrement factor, two important parameters in evaluating building envelopes, were also determined.

Findings

The time lag of the bio-based composite building envelope was found to be in the range of 4.2–4.6 h for 100 mm thickness block and 10.64–11.5 h for 200 mm thickness block. The decrement factor was also determined to be in the range of 0.87–0.88. The bio-based composite building envelopes were able to maintain the indoor temperature of the model from 25.4 to 27.4 °C, providing a closely stable indoor thermal comfort despite varying outdoor temperatures. The temperature variation in 24 h, was very stable for about 8 h before a degree increment, providing a comfortable indoor temperature for occupants and the need not to rely on air conditions and other mechanical forms of cooling. Potential energy savings also peaked at 529.14 kWh per year.

Practical implications

The findings of this study present opportunities to building developers and engineers in terms of selecting vernacular materials for building envelopes towards green building adaptation, energy savings, reduced construction costs and job creation.

Originality/value

This study presents for the first time, time lag and decrement factor for bio-based composite building envelopes for green building adaptation in hot climates, as found in Ghana.

Details

International Journal of Building Pathology and Adaptation, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2398-4708

Keywords

Article
Publication date: 26 September 2022

Christian Nnaemeka Egwim, Hafiz Alaka, Oluwapelumi Oluwaseun Egunjobi, Alvaro Gomes and Iosif Mporas

This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings.

Abstract

Purpose

This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings.

Design/methodology/approach

This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics.

Findings

Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting.

Research limitations/implications

While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK.

Practical implications

This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system.

Originality/value

This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.

Details

Journal of Engineering, Design and Technology , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1726-0531

Keywords

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