- Titel
- Systematic Review of Deep Learning and Machine Learning for Building Energy
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-884320
- Quellenangabe
- Frontiers in energy research
Erscheinungsjahr: 2022
Jahrgang: 10
Seiten: 1-19
E-ISSN: 2296-598X
Artikelnummer: 786027 - Erstveröffentlichung
- 2022
- Abstract (EN)
- The building energy (BE) management plays an essential role in urban sustainability and smart cities. Recently, the novel data science and data-driven technologies have shown significant progress in analyzing the energy consumption and energy demand datasets for a smarter energy management. The machine learning (ML) and deep learning (DL) methods and applications, in particular, have been promising for the advancement of accurate and high-performance energy models. The present study provides a comprehensive review of ML- and DL-based techniques applied for handling BE systems, and it further evaluates the performance of these techniques. Through a systematic review and a comprehensive taxonomy, the advances of ML and DL-based techniques are carefully investigated, and the promising models are introduced. According to the results obtained for energy demand forecasting, the hybrid and ensemble methods are located in the high-robustness range, SVM-based methods are located in good robustness limitation, ANN-based methods are located in medium-robustness limitation, and linear regression models are located in low-robustness limitations. On the other hand, for energy consumption forecasting, DL-based, hybrid, and ensemble-based models provided the highest robustness score. ANN, SVM, and single ML models provided good and medium robustness, and LR-based models provided a lower robustness score. In addition, for energy load forecasting, LR-based models provided the lower robustness score. The hybrid and ensemble-based models provided a higher robustness score. The DL-based and SVM-based techniques provided a good robustness score, and ANNbased techniques provided a medium robustness score.
- Andere Ausgabe
- Link zum Artikel, der zuerst in der Zeitschrift „Frontiers in energy research” erschienen ist.
DOI: 10.3389/fenrg.2022.786027 - Freie Schlagwörter (DE)
- maschinelles Lernen, Energieverbrauch, intelligentes Netz, Internet der Dinge, Datenwissenschaft
- Freie Schlagwörter (EN)
- machine learning, energy consumption, smart grid, internet of things, data science
- Klassifikation (DDC)
- 333.7
- Verlag
- Frontiers Media, Lausanne
- Förder- / Projektangaben
- European Commission (EC)
H2020 | MSCA-COFUND-FP
Slovak Academic and Scientific PROgramme for experienced researchers
(SASPRO 2)
ID: 945478 - Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-884320
- Veröffentlichungsdatum Qucosa
- 02.02.2024
- Dokumenttyp
- Artikel
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0