An increase in energy demand in buildings continues to give rise to air pollution with a consequent impact on human health. To curb this trend, energy efficiency assessment plays a crucial role in helping to understand the energy in buildings and to recommend strategies to improve efficiency. Unfortunately, many existing approaches to assessing the energy efficiency of buildings are failing to do it accurately. Hence, the recommended energy efficiency strategies thereafter are failing to achieve the expected result. One approach in recent times uses datadriven predictive analytics techniques like machine learning (ML) algorithms to assess a building's energy efficiency towards improving its performance. However, as many ML algorithms exist, the selection of the right one is important for a successful assessment. Unfortunately, many of the existing works in this regard have simply adopted an ML algorithm without a justified rationale which may result in poor selection of the good performing ML algorithm. Therefore, in this study, a premise to compare the performance of ML algorithms for the assessment of energy efficiency of buildings was proposed. First, consolidated energy efficiency ratings of buildings from different data sources are used to develop predictive models using several ML algorithms. Thereafter, identification of best performing model was done by comparing evaluation metrics like RMSE, R-Squared, and Adjusted R-Squared. From the comparison, Extra Trees predictive model came top with RMSE, R-Squared, and Adjusted R-Squared of 2.79, 93%, and 93% respectively. This approach helps in the initial selection of suitable and better-performing ML algorithms.

The literature suggests that the success of strategic alliances between large and small firms is influenced by four broad factors: motivational, cultural and resource capability synergies; shared commitments, goals and roles; participative planning, operationalisation and administration; and regular open communications. This study suggests that even though mismatches and incongruencies may be evident, the alliance formation and endurance are influenced by two other factors: largely similar perceptions by both groups regarding the alliance’s performance determinants; and a strong expectation by the large firm group of high future net benefits from aligning with small firms. Performance is, invariably, contingent upon implementing a number of ‘pre-emptive’ steps during the course of the alliance.

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.