DETeCT: Data-Efficient low-carbon Technology Classification for multiclass Targets

ProjectDETeCT presents a data-driven framework to classify the adoption of low-carbon technologies (LCTs), such as electric vehicles, heat pumps, and solar PV, using high-frequency smart meter data. Combining domain-informed and time-series features within a Random Forest and stacking model, the approach achieves robust predictive performance on large-scale household data from Flanders. This data repository contains the complete model architecture, underlying data for training and testing the model, and complete results from the corresponding paper.Data sourcesAll data used in this project is published by Fluvius (2025) under an open data license that permits reproduction, redistribution and reuse (https://opendata.fluvius.be/p/licentieopendatafluvius/). Fluvius (2025). Verbruiksprofielen digitale elektriciteitsmeters: kwartierwaarden voor een volledig jaar. https://opendata.fluvius.be/explore/dataset/1_50-verbruiksprofielen-dm-elek-kwartierwaarden-voor-een-volledig-jaar/information/