Bridging the gap between complexity and interpretability of a data analytics-based process for benchmarking energy performance of buildings
作者：
Highlights：
• A data-driven benchmarking process of building energy performance is proposed.
• A set of about 100,000 energy performance certificates of buildings is analysed.
• Five classification algorithms are considered for developing the benchmarking tool.
• Clustering analysis and XAI are coupled for extracting human-readable patterns.
• Classification results are explained for understanding the model behaviour.
摘要
•A data-driven benchmarking process of building energy performance is proposed.•A set of about 100,000 energy performance certificates of buildings is analysed.•Five classification algorithms are considered for developing the benchmarking tool.•Clustering analysis and XAI are coupled for extracting human-readable patterns.•Classification results are explained for understanding the model behaviour.
论文关键词：Building energy benchmarking,Energy performance certificates,Classification algorithms,Clustering analysis,Explainable artificial intelligence
论文评审过程：Received 3 June 2021, Revised 29 April 2022, Accepted 27 May 2022, Available online 3 June 2022, Version of Record 17 June 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117649
