Hybrid Approach in Thermal Demand Forecasting of a Building

Author:

Marković AnđelaORCID,Batić MarkoORCID,Stanković KatarinaORCID

Publisher

Springer Nature Switzerland

Reference5 articles.

1. Kosarac, A., et al.: Thermal behavior modeling based on BP neural network in Keras framework for motorized machine tool spindles. Materials 15(21), Article no. 21 (2022). https://doi.org/10.3390/ma15217782

2. Trinity College Dublin (Dublin University). D3.4 - ITES-MES prototype at small scale [Deliverable]. In: Novel Building Integration Designs for Increased Efficiencies in Advanced Climatically Tunable Renewable Energy Systems (IDEAS Project No. 815271, Work Package WP3: Heat Pump and Underfloor Heating/Cooling). Horizon 2020 Program: H2020-LC-SC3-2018–2019–2020. Stage: Final (2020)

3. Bastida, H., Ugalde-Loo, C.E., Abeysekera, M., Qadrdan, M., Wu, J.: Thermal dynamic modelling and temperature controller design for a house. Energy Procedia 158, 2800–2805 (2019). https://doi.org/10.1016/j.egypro.2019.02.041

4. Jarmuda, T.: Thermal model of an intelligent house. https://core.ac.uk/display/295559358?utm_source=pdf&utm_medium=banner&utm_campaign=pdf-decoration-v1

5. Refaeilzadeh, P., Tang, L., Liu, H.: Cross-validation. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, pp. 532–538. Springer, Cham (2009). https://doi.org/10.1007/978-0-387-39940-9_565

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