Evaluating Energy Performance Certificate Data with Data Science

Anastasiadou, M., Santos, V., & Dias, M. S. (2021). Evaluating Energy Performance Certificate Data with Data Science. In 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET) (pp. 1-5). IEEE. https://doi.org/10.1109/ICECET52533.2021.9698806The related problems of improving existing buildings' energy performance, reducing energy consumption, and improving indoor comfort and their many consequences are well known. Considering increasing urbanization and climate change, governments define strategies to enhance and measure buildings' energy performance and energy efficiency. This work aims to contribute to the improvement of buildings' characteristics by conducting a thorough systematic literature review and adopting a data science approach to these problems, presenting initial results with an open-access energy performance certificate dataset from the Lombardy Region, in Italy. We provide a pre-processing method to the data, applicable for future research, aiming to address challenges such as automatic classification of existing buildings' energy performance certification, and predicting energy-efficient retrofit measures, using machine learning techniques. The analysis of this dataset is challenging because of the high variability and dimensionality of this dataset. For this purpose, a robust iterative process was developed. First, the data dimensionality was reduced with Pearson Correlation to find the best set of variables against the non-renewable global energy performance index (EPgl, nren). Then, the outliers were handled by utilizing Box Plot and Isolation Forest algorithms. The main contribution is to inform private and public building sectors on dealing with high dimensional data to achieve enhanced energy performance and predict energy-efficient retrofit measures to improve their energy performance.authorsversionpublishe

Exploring energy certificates of buildings through unsupervised data mining techniques

Energy Certificates of Buildings (ECB) provide interesting information on the standard energy performance, thermo-physical and geometrical related properties of existing buildings. The analysis of such data collection is challenging due to data volume and heterogeneity of attributes. This paper presents EPICA a data mining framework to automatically explore a collection of ECB to extract interesting knowledge items. To this aim, EPICA first reduces the data dimensionality through the Principal Component Analysis, then a clustering algorithm is exploited to discover groups of ECB with similar features. Each group is then locally characterized by a set of relevant generalized association rules able to summarize interesting relations among variables influencing energy performance of buildings at different coarse granularities. Experimental results, obtained on real data collected from an energy certification dataset related to Piedmont Region, in North Western of Italy, shows the effectiveness of EPICA in extracting a manageable set of human-readable knowledge items characterizing the groups of buildings with different energy performance levels

Text miner's little helper: scalable self-tuning methodologies for knowledge exploration

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