PyARC the Python Algorithm for Residential load profiles reConstruction

• Published in: SoftwareX Vol. 28; p. 101878
• Main Authors: Giannuzzo, Lorenzo, Schiera, Daniele Salvatore, Minuto, Francesco Demetrio, Lanzini, Andrea
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2024; Elsevier
• Subjects: Data scarcity; Load profiles reconstruction; Machine-learning; Non-intrusive methodology; Python-based software; Data scarcity; Python-based software; Non-intrusive methodology; Machine-learning; Load profiles reconstruction
• ISSN: 2352-7110; 2352-7110
• DOI: 10.1016/j.softx.2024.101878

Load profiling for residential aggregates encounters challenges due to data scarcity and the inadequacy of standard profiles obtained from statistical analyses. In the absence of hourly data, many methods rely on standard profiles, which could lead to significant errors in consumption estimation, especially for evaluating specific aggregates. This article presents PyARC, a Python-based algorithm trainable with customizable consumption data, which addresses the problem related to evaluating the energy consumption of specific aggregates by using typological profiles extracted from similar users, thereby improving accuracy. The algorithm's innovative approach uses Association Rule Mining and Random Forest Classification to reconstruct the load profiles of aggregates, providing a more robust solution for estimating the electrical load with limited data.