Evaluating transferability of recommender systems from entity-level data to spatially aggregated household data in the context of green technology adoption

Energy and AI, vol. 26, pp. 100888

Abstract

This study investigates the transferability of recommender systems for photovoltaic (PV) adoption prediction from entity-level data to spatially aggregated household data, addressing a major limitation imposed by privacy regulations such as the GDPR. By demonstrating how recommendation systems can operate on publicly available geospatial and socio-economic datasets, the work establishes a scalable framework for high-resolution green technology adoption analysis and energy system planning. The proposed methodology extends a hierarchical clustering–based recommendation framework through the integration of a Local Outlier Factor (LOF) noise-reduction layer to mitigate the increased heterogeneity and information loss induced by spatial aggregation. The framework is evaluated on a large-scale geospatial dataset comprising 87 features and approximately 417k building entries. Cohen’s d, Wasserstein distance, and CCDF-based analyses are used to assess the bias and structural changes introduced into the data by the noise reduction process, alongside variations in clustering distance metrics and LOF hyperparameters. The results demonstrate that recommender systems can be successfully transferred to spatially aggregated data, although spatial aggregation substantially increases noise and reduces baseline predictive performance. Incorporating the LOF-based robustness layer improves recall by 10% to 80% while maintaining limited structural distortion, thereby identifying a practical trade-off between predictive performance and bias introduction. The findings provide a robust foundation for recommender systems in green technology adoption research and highlight the broader applicability of spatially aggregated recommendation frameworks in data-restricted domains.

Authors 2

1. Kaan Duran corresponding Aachen

   RWTH Aachen University · E.ON (Germany)

   Affiliation as printed

   E.ON Digital Technology GmbH, Ricklinger Stadtweg 123, Hannover, 30459, Germany

   RWTH Aachen University - Institute for Automation of Complex Power Systems, Mathieustraße 10, Aachen, 52074, Germany

2. Antonello Monti Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University - Institute for Automation of Complex Power Systems, Mathieustraße 10, Aachen, 52074, Germany