Energy consumption prediction by using machine learning for smart building: Case study in Malaysia. (March 2021)

• Record Type: Journal Article
• Authors: Shapi, Mel Keytingan M.; Ramli, Nor Azuana; Awalin, Lilik J.
• Is Part Of: Developments in the built environment. Volume 5(2021)
• Publication Date: 2021-03
• Subjects: Building energy management system -- Machine learning -- Microsoft Azure -- Energy consumption -- Prediction; Civil engineering -- Periodicals; Sustainable construction -- Periodicals; 624.05
• DOI: 10.1016/j.dibe.2020.100037
• Languages: English
• ISSNs: 2666-1659

Abstract

Abstract: Building Energy Management System (BEMS) has been a substantial topic nowadays due to its importance in reducing energy wastage. However, the performance of one of BEMS applications which is energy consumption prediction has been stagnant due to problems such as low prediction accuracy. Thus, this research aims to address the problems by developing a predictive model for energy consumption in Microsoft Azure cloud-based machine learning platform. Three methodologies which are Support Vector Machine, Artificial Neural Network, and k-Nearest Neighbour are proposed for the algorithm of the predictive model. Focusing on real-life application in Malaysia, two tenants from a commercial building are taken as a case study. The data collected is analysed and pre-processed before it is used for model training and testing. The performance of each of the methods is compared based on RMSE, NRMSE, and MAPE metrics. The experimentation shows that each tenant's energy consumption has different distribution characteristics. Highlights: Cloud based prediction model development does not depend on the performance of the hardware its running on. This analysis would provide an insight on how reliable and capable AzureML studio for developing a prediction model. The consequence of the model training and testing shows that each method performed differently in every cases.