Synconn_build: A python based synthetic dataset generator for testing and validating control-oriented neural networks for building dynamics prediction-Reference-Cited by-同舟云学术

Synconn_build: A python based synthetic dataset generator for testing and validating control-oriented neural networks for building dynamics prediction

Published:2023-12 Issue: Volume:11 Page:102464
ISSN:2215-0161
Container-title:MethodsX
language:en
Short-container-title:MethodsX

Author:

Chaudhary Gaurav^ORCID,Johra Hicham,Georges Laurent,Austbø Bjørn

Funder

Norges Teknisk-Naturvitenskapelige Universitet

Publisher

Elsevier BV

Subject

Medical Laboratory Technology,Clinical Biochemistry

Reference35 articles.

1. 2020 Global Status Report for Buildings and Construction: Towards a zero-emissions, efficient and resilient buildings and construction sector - executive summary 2020.

2. Does renewable energy electricity and economic complexity index help to achieve carbon neutrality target of top exporting countries?;Li;J. Environ. Manag.,2021

3. Neukomm M., Nubbe V., Fares R. Grid-Interactive Efficient Buildings. United States: 2019. doi:10.2172/1508212.

4. What is stopping low-carbon buildings? A global review of enablers and barriers;Mata;Energy Res. Soc. Sci.,2021

5. All you need to know about model predictive control for buildings;Drgoňa;Annu. Rev. Control.,2020

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