Comparison of machine learning algorithms for evaluating building energy efficiency using big data analytics

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.

Artificial Intelligence in the Construction Industry: A Systematic Review of the Entire Construction Value Chain Lifecycle

“…Possibility for Big Data Analytics AI technologies are exposed to an unending quantity of data to learn from and improve on every day at a time when vast amounts of data are being produced in the industry [114,115]. For instance, the research led by Palma [81] explored the integration of convolutional neural networks (CNNs), a subset of deep learning methods, into the realm of architectural heritage by developing a mobile app aimed at monument recognition, pioneering the use of AI in this domain.…”

Examining the mediating role of market performance in the relationship between the big data analytical capability and firm performance

“…They came to the conclusion that the volume, diversity, and pace of BDAC are major elements in giving businesses an advantage in the marketplace. The end goal of the Business Development and Competitiveness Advisory Council (BDAC) is to provide businesses with actionable counsel that will help them develop a competitive advantage, improve their market performance, and increase the value they provide to their customers (Egwim et al, 2022). Businesses who use BDAC have a considerable head start over their competitors in the race to find untapped market opportunities before their competitors do.…”

Systematic review of critical drivers for delay risk prediction: towards a conceptual framework for BIM-based construction projects

“…However, a structured model of data that represents building elements with its usage spanning beyond the pre-construction phase to the post-construction phase known as Building Information Modelling (BIM) in Architecture, Engineering and Construction (AEC) industry has been introduced for a while now (Ameziane, 2000; Egwim et al. , 2021a, b, 2022).…”