Annex 81 Articles

[1]
P. Pauwels and G. Fierro, ‘A Reference Architecture for Data-Driven Smart Buildings Using Brick and LBD Ontologies’, CLIMA, May 2022, doi: 10.34641/clima.2022.425.
[2]
L. Chamari, E. Petrova, and P. Pauwels, ‘A web-based approach to BMS, BIM and IoT integration: a case study’, CLIMA 2022 conference, May 2022, doi: 10.34641/clima.2022.228.
[3]
M. Lumbreras, G. Diarce, K. Martin-Escudero, R. Garay-Martinez, and B. Arregi, ‘Advanced Heat-Load Prediction Models in Buildings Combining Supervised & Unsupervised Learning’. Rochester, NY, Aug. 10, 2022. doi: 10.2139/ssrn.4186449.
[4]
T. Schranz, Q. Alfalouji, T. Hirsch, and G. Schweiger, ‘An Open IoT Platform: Lessons Learned from a District Energy System’, in 2022 Second International Conference on Sustainable Mobility Applications, Renewables and Technology (SMART), Cassino, Italy: IEEE, Nov. 2022, pp. 1–9. doi: 10.1109/SMART55236.2022.9990228.
[5]
Z. Ma, A. Clausen, Y. Lin, and B. N. Jørgensen, ‘An overview of digitalization for the building-to-grid ecosystem’, Energy Inform, vol. 4, no. S2, p. 36, Sep. 2021, doi: 10.1186/s42162-021-00156-6.
[6]
G. Fierro, A. Saha, T. Shapinsky, M. Steen, and H. Eslinger, ‘Application-driven creation of building metadata models with semantic sufficiency’, in Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Boston Massachusetts: ACM, Nov. 2022, pp. 228–237. doi: 10.1145/3563357.3564083.
[7]
D. Jähnig, F. Hengel, K. Eder, S. Moosberger, and D. Ruepp, ‘Betriebsoptimierung durch die Erstellung eines Digitalen Zwillings’, Nachhaltige technologien, vol. 117, no. 1, 2023. [Online]. Available: https://www.aee.at/zeitschrift-erneuerbare-energie/117-nt-01-2023-datengesteuerte-intelligente-gebaeude/1450-betriebsoptimierung-durch-die-erstellung-eines-digitalen-zwillings
[8]
Gori, Virginia, Chen, Guokai, Jatkar, Harshavardhan, Ruyssevelt, Paul, and Rovas, Dimitrios, ‘Case Studies zu datengesteuerten intelligenten Gebäuden’, Nachhaltige technologien, vol. 117, no. 1, 2023. Accessed: Jun. 01, 2023. [Online]. Available: https://www.aee.at/zeitschrift-erneuerbare-energie/117-nt-01-2023-datengesteuerte-intelligente-gebaeude/1451-case-studies-zu-datengesteuerten-intelligenten-gebaeuden
[9]
T. Schranz, T. Mach, and G. Schweiger, ‘Das Internet der Dinge für die Energiewende’, Nachhaltige technologien, vol. 117, no. 1, 2023. [Online]. Available: https://www.aee.at/zeitschrift-erneuerbare-energie/117-nt-01-2023-datengesteuerte-intelligente-gebaeude/1453-das-internet-der-dinge-fuer-die-energiewende
[10]
M. Lumbreras et al., ‘Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters’, Energy, vol. 239, p. 122318, Jan. 2022, doi: 10.1016/j.energy.2021.122318.
[11]
Ruyssevelt, Paul, Rovas, Dimitrios, Gori, Virginia, Chen, Guokai, and Jatkar, Harshavardhan, ‘Data Driven Smart Building Case Studies’, Nov. 2022, doi: 10.5281/zenodo.7326672.
[12]
M. Lumbreras-Mugaguren, R. Garay-Martinez, A. Garrido-Marijuan, and O. Eguiarte-Fernandez, ‘Data Driven Supervision of District Heating Systems’, in Handbook of Low Temperature District Heating, R. Garay-Martinez and A. Garrido-Marijuan, Eds., in Green Energy and Technology. Cham: Springer International Publishing, 2022, pp. 165–178. doi: 10.1007/978-3-031-10410-7_7.
[13]
X. Yu, L. Georges, and L. Imsland, ‘Data pre-processing and optimization techniques for stochastic and deterministic low-order grey-box models of residential buildings’, Energy and Buildings, vol. 236, p. 110775, Apr. 2021, doi: 10.1016/j.enbuild.2021.110775.
[14]
O. Eguiarte, A. Garrido-Marijuan, R. Garay-Martinez, M. Raud, and I. Hagu, ‘Data-driven assessment for the supervision of District Heating Networks’, Energy Reports, vol. 8, pp. 34–40, Dec. 2022, doi: 10.1016/j.egyr.2022.10.212.
[15]
V. Gori, G. Chen, H. Jatkar, and D. Rovas, ‘Data-driven smart buildings: narratives of drivers and barriers from real-world implementation’, CIBSE Technical Symposium, 2023.
[16]
M. Finamore, ‘Double skin suitable for Mediterranean climate in school-gym buildings’, E3S Web Conf., vol. 111, p. 03001, 2019, doi: 10.1051/e3sconf/201911103001.
[17]
Z. G. Ma, K. Christensen, M. Vaerbak, N. Fatras, D. A. Howard, and B. N. Jørgensen, ‘Ecosystem based Opportunity Identification and Feasibility Evaluation for Demand Side Management Solutions’, in 2023 IEEE Conference on Power Electronics and Renewable Energy (CPERE), Luxor, Egypt: IEEE, Feb. 2023, pp. 1–6. doi: 10.1109/CPERE56564.2023.10119582.
[18]
X. Yu, K. S. Skeie, M. D. Knudsen, Z. Ren, L. Imsland, and L. Georges, ‘Influence of data pre-processing and sensor dynamics on grey-box models for space-heating: Analysis using field measurements’, Building and Environment, vol. 212, p. 108832, Mar. 2022, doi: 10.1016/j.buildenv.2022.108832.
[19]
J. R. Santiago, M. Krause, and L. Wett, ‘Intelligente Gebäudesteuerung im Zentrum für Umweltbewusstes Bauen’, Nachhaltige technologien, vol. 117, no. 1, 2023. [Online]. Available: https://www.aee.at/zeitschrift-erneuerbare-energie/117-nt-01-2023-datengesteuerte-intelligente-gebaeude/1454-intelligente-gebaeudesteuerung-im-zentrum-fuer-umweltbewusstes-bauen
[20]
J. Kurzidim and M. Schöny, ‘Künstliche Intelligenz für Vorhersage und Regelung im Energiemanagement’, Nachhaltige technologien, vol. 117, no. 1, 2023. [Online]. Available: https://www.aee.at/zeitschrift-erneuerbare-energie/117-nt-01-2023-datengesteuerte-intelligente-gebaeude/1452-kuenstliche-intelligenz-fuer-vorhersage-und-regelung-im-energiemanagement
[21]
T. Weiß and A. Riffnaller-Schiefer, ‘Revolutionieren digitale Gebäudezwillinge den Gebäudebetrieb oder bleiben sie ein ungenutzes Potenzial?’, Nachhaltige technologien, vol. 117, no. 1, 2023. [Online]. Available: https://www.aee.at/zeitschrift-erneuerbare-energie/117-nt-01-2023-datengesteuerte-intelligente-gebaeude/1449-revolutionieren-digitale-gebaeudezwillinge-den-gebaeudebetrieb-oder-bleiben-sie-ein-ungenutzes-potenzial
[22]
M. Eguizabal, R. Garay-Martinez, and I. Flores-Abascal, ‘Simplified model for the short-term forecasting of heat loads in buildings’, Energy Reports, vol. 8, pp. 79–85, Dec. 2022, doi: 10.1016/j.egyr.2022.10.224.
[23]
I. Sartori et al., ‘Sub-hourly measurement datasets from 6 real buildings: Energy use and indoor climate’, Data in Brief, vol. 48, p. 109149, Jun. 2023, doi: 10.1016/j.dib.2023.109149.
[24]
Z. Ma, ‘Survey data on university students’ experience of energy control, indoor comfort, and energy flexibility in campus buildings’, Energy Inform, vol. 5, no. S4, p. 50, Dec. 2022, doi: 10.1186/s42162-022-00239-y.
[25]
M. Lumbreras, G. Diarce, K. Martin, R. Garay-Martinez, and B. Arregi, ‘Unsupervised recognition and prediction of daily patterns in heating loads in buildings’, Journal of Building Engineering, vol. 65, p. 105732, Apr. 2023, doi: 10.1016/j.jobe.2022.105732.
[26]
X. Jin, Ch. Fu, H. Kazmi, A. Balint, A. Canaydin, M. Quintana, F. Biljecki, F. Xiao, and C. Miller, ‘The Building Data Genome Directory -- An open, comprehensive data sharing platform for building performance research', doi: 10.48550/arXiv.2307.00793.
[27]
Z. Chen, Z. O’Neill, J. Wen, O. Pradhan, T. Yang, X. Lu, G. Lin, S. Miyata, S. Lee, Ch. Shen, R. Chiosa, M.S. Piscitelli, A. Capozzoli, F. Hengel, A. Kührer, M. Pritoni, W. Liu, J. Clauß, Y. Chen, and T. Herr, ‘A review of data-driven fault detection and diagnostics for building HVAC systems', Applied Energy, vol. 339, p. 121030, 2023, doi: 10.1016/j.apenergy.2023.121030