Bridging the gap between complexity and interpretability of a data analytics-based process for benchmarking energy performance of buildings. (15th November 2022)

• Record Type: Journal Article
• Authors: Galli, Antonio; Piscitelli, Marco Savino; Moscato, Vincenzo; Capozzoli, Alfonso
• Is Part Of: Expert systems with applications. Volume 206(2022)
• Publication Date: 2022-11-15
• Subjects: Building energy benchmarking -- Energy performance certificates -- Classification algorithms -- Clustering analysis -- Explainable artificial intelligence; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117649
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Artificial intelligence (AI) is fast becoming a general purpose technology with outstanding impacts in industries worldwide, thus supporting the Industry 4.0 revolution. In particular, the energy sector is one of those that has taken more advantages from the implementation of AI approaches, especially Machine Learning models, for several applications, including energy performance benchmarking of buildings. However, the black-box approach could lead to a lack of result interpretability thus preventing the effective application of AI in some real-world scenarios. For this reason, eXplainable Artificial Intelligence (XAI) tools can be effectively embedded within an AI-based Energy Analytics methodology in order to enhance the explainability of the model results. In this paper, we propose an explainable AI-based benchmarking framework for estimating the membership to specific energy performance classes of a large set of Energy Performance Certificates (EPCs) of flats. The classification is obtained by leveraging different black-box classifiers characterized by high accuracy, yet their inference mechanism is not human-readable. Therefore, a generalizable XAI methodology, based on the combination of a local explainer together with a clustering algorithm, is employed to explain the model results and causal effects between the predictors and target variable to better understand the model behaviour, and the motivations behind correct and wrong performed classifications. The paper provides a general methodological approach capable to exploit a limited number of instances to extract, explain and interpret inference mechanisms learnt by the model that are useful for the end-user. The framework was tested on about 100, 000 EPCs of flats located in Italy. 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.