Treating Common Problems Observed During Smart Building Control Real-Life Testing: Sharing Practical Experience

Author:

Karatzinis Georgios,Michailidis Iakovos,Dimara Asimina,Stefanopoulou Aliki,Vasilopoulos Vasileios Georgios,Krinidis Stelios,Anagnostopoulos Christos-Nikolaos,Kosmatopoulos Elias,Ioannidis Dimosthenis,Tzovaras Dimitrios

Publisher

Springer Nature Switzerland

Reference23 articles.

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2. Aguilar, J., Garces-Jimenez, A., R-Moreno, M., García, R.: A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings. Renew. Sustain. Energy Rev. 151, 111530 (2021)

3. Chaudhuri, T., Soh, Y.C., Li, H., Xie, L.: A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings. Appl. Energy 248, 44–53 (2019)

4. Chen, Y., Guo, M., Chen, Z., Chen, Z., Ji, Y.: Physical energy and data-driven models in building energy prediction: a review. Energy Rep. 8, 2656–2671 (2022)

5. Commission, E.: ‘fit for 55’: delivering the eu’s 2030 climate target on the way to climate neutrality. Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions (2021)

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