Energy Consumption Prediction for a Recreation Facility Using Data-Driven Techniques

Paul Banda (), Muhammed A. Bhuiyan, Kevin Zhang and Andy Song
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Paul Banda: RMIT University
Muhammed A. Bhuiyan: RMIT University
Kevin Zhang: RMIT University
Andy Song: RMIT University

A chapter in Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate, 2021, pp 1683-1694 from Springer

Abstract: Abstract Leisure centres are multifunctional buildings that have irregular energy consumption patterns and consume more energy compared to most building types. However, they have little representation in building performance energy prediction literature. This work presents an energy consumption prediction effort for a leisure centre using data-driven techniques, namely Light-gbm, support vector regression and multi-linear regression models. Climatic and energy use data collected over sixteen months were pre-processed, normalized and split into training and testing sets for regression analysis. The results showed that the ensemble-based Lightgbm model had superior performance in a multi-input prediction setting. The support vector regression model and multi-linear regression had a marginal difference between themselves at the prediction task. The MAE, RMSE and R2 evaluation metrics ranged from good to very good among the created models. The previous energy consumption observation is determined as the essential variable for energy consumption at this multi-functional building type. The developed predictive models can be an alternative method for the better attainment of efficient energy management.

Keywords: Data driven techniques; Energy consumption prediction; Leisure centre (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-15-8892-1_118

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DOI: 10.1007/978-981-15-8892-1_118

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