Interactive Machine Learning-Powered Dashboard for Energy Analytics in Residential Buildings

  1. Garcia-Perez, Diego 1
  2. Diaz-Blanco, Ignacio 1
  3. Enguita-Gonzalez, Jose M. 1
  4. Menéndez, Jorge
  5. Cuadrado-Vega, Abel A. 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Actes de conférence:
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2024

Année de publication: 2024

Pages: 339-344

Congreso: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (32th. 2024. Bruges, Bélgica)

Type: Communication dans un congrès

DOI: 10.14428/ESANN/2024.ES2024-130 GOOGLE SCHOLAR lock_openAccès ouvert editor

Résumé

Efforts to reduce energy consumption in buildings are crucial for climate change concerns. In this sense, energy monitoring increases energy awareness and mitigates energy wastes. This study integrates machine learning models, advanced visualisations, and interactive tools to create an insightful energy monitoring dashboard. Novel contributions include a 2D map of daily energy demand profiles combining spatial encodings based on t-SNE, fluid aggregation, and filter operations via a datacube framework, as well as visual encoding powered by morphing projections. This approach facilitates the decisions of end users regarding the optimisation of energy in residential facilities.

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Information sur le financement

This work is part of Grant PID2020-115401GB-I00 funded by MCIN/AEI/ 10.13039/501100011033.

Financeurs