Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Roberto Chiosa"'
Publikováno v:
Energies, Vol 14, Iss 1, p 237 (2021)
Recently, the spread of smart metering infrastructures has enabled the easier collection of building-related data. It has been proven that a proper analysis of such data can bring significant benefits for the characterization of building performance
Externí odkaz:
https://doaj.org/article/d1efd446be694defb1538c38fac36627
Autor:
Zhelun Chen, Zheng O’Neill, Jin Wen, Ojas Pradhan, Tao Yang, Xing Lu, Guanjing Lin, Shohei Miyata, Seungjae Lee, Chou Shen, Roberto Chiosa, Marco Savino Piscitelli, Alfonso Capozzoli, Franz Hengel, Alexander Kührer, Marco Pritoni, Wei Liu, John Clauß, Yimin Chen, Terry Herr
Publikováno v:
Applied Energy. 339:121030
Publikováno v:
Energy and Buildings. 270:112302
Publikováno v:
Energies; Volume 14; Issue 1; Pages: 237
Energies, Vol 14, Iss 237, p 237 (2021)
Energies, Vol 14, Iss 237, p 237 (2021)
Recently, the spread of smart metering infrastructures has enabled the easier collection of building-related data. It has been proven that a proper analysis of such data can bring significant benefits for the characterization of building performance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb2612fcfe22d0312411b65327cc956a
http://hdl.handle.net/11583/2876722
http://hdl.handle.net/11583/2876722
Autor:
Yutian Lei, Roberto Chiosa, Yongjun Sun, Cheng Fan, Alfonso Capozzoli, Marco Savino Piscitelli
Publikováno v:
Energy. 240:122775
Data-driven methods have gained increasing popularity due to their high-convenience and high-accuracy in practice. Considering the wide discrepancies in data availability across different buildings, transfer learning can be applied to improve the fea