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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. • 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. [ABSTRACT FROM AUTHOR]

Nowadays the introduction of energy marketplaces in several countries pushed the development of machine learning approaches for devising effective predictions about both energy needs and energy productions. In this paper we address the problem of predicting the amount of electrical power produced using non-renewable sources, as getting an estimate of the amount of electrical power produced using the various kinds of non-renewable sources yields a big competitive advantage for energy market investors. Specifically, we devise a forecasting technique obtained by trying and combining various machine learning techniques which is able to provide energy production estimates with a remarkably low error. Finally, since the input data available for predictions are in general not sufficient to determine the amounts of produced energy for the various source types, we provide an estimate of the impact of unknown latent variable on the amounts of produced energy, by devising a prediction model which is capable of estimating the prediction error for the specific data at hand. These informations can be exploited by investors to get an idea of the risk levels of their investments. • New model for predicting the amount of non-renewable energy produced in Italy. • The model uses mostly publicly available information sources. • The experiments allow us to assess that GBRT is the best model for this task. • Experimentally investigation of the possibility of identifying hard cases. • KNN is the only technique that is able to identify hard cases. [ABSTRACT FROM AUTHOR]