Data-driven insights for improved heating and cooling predictions: Impact of input parameters on multivariate deep learning algorithms using geothermal borehole field data-Reference-Cited by-同舟云学术

Data-driven insights for improved heating and cooling predictions: Impact of input parameters on multivariate deep learning algorithms using geothermal borehole field data

Published:2024-05 Issue: Volume:245 Page:122870
ISSN:1359-4311
Container-title:Applied Thermal Engineering
language:en
Short-container-title:Applied Thermal Engineering

Author:

Ahmed Naveed^ORCID,Assadi Mohsen^ORCID,Zhang Qian^ORCID,Śliwa Tomasz

Funder

University of Stavanger

Publisher

Elsevier BV

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4. A.A. Ahmed, M. Assadi, A. Kalantar, A. Sapińska-Śliwa, T. Sliwa, N. Ahmed, et al., Evaluating the techno-economic impact of decarbonizing buildings by using borehole heat exchangers in comparison to fuel-based systems, Energy Sustain. Dev. 2023;76. 10.1016/j.esd.2023.101262.

5. Review and prospect of data-driven techniques for load forecasting in integrated energy systems;Zhu;Appl. Energy,2022

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