Digital Twinning and ANN-based Forecasting Model for Building Energy Consumption-Reference-Cited by-同舟云学术

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Digital Twinning and ANN-based Forecasting Model for Building Energy Consumption

Published:2023-02-20 Issue: Volume: Page:
ISSN:
Container-title:2023 Advances in Science and Engineering Technology International Conferences (ASET)
language:
Short-container-title:

Author:

Al-Mufti Omar Ahmed¹,Al-Isawi Omar Adil²,Amirah Lutfi Hatem³,Ghenai Chaouki³

Affiliation:

1. Khalifa University,Mechanical Engineering Department,Abu Dhabi,United Arab Emirates

2. Khalifa University,Electrical and Computer Engineering Department,Abu Dhabi,United Arab Emirates

3. University of Sharjah,Sustainable and Renewable Energy Engineering Department,Sharjah,United Arab Emirates

Funder

University of Sharjah

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10180403/10180404/10180899.pdf?arnumber=10180899

Reference21 articles.

1. Artificial Neural Network PV Performance Prediction and Electric Power System Simulation of a Ship-tracking CubeSat

2. Artificial neural networks for power output forecasting from bifacial solar PV system with enhanced building roof surface Albedo

3. Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study

4. Short-term building electrical load forecasting using adaptive neuro-fuzzy inference system (ANFIS)

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predictive digital twin technologies for achieving net zero carbon emissions: a critical review and future research agenda;Smart and Sustainable Built Environment;2024-08-02

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