Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks-Reference-Cited by-同舟云学术

Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks

Published:2021-06-28 Issue: Volume: Page:
ISSN:2050-0505
Container-title:Energy Science & Engineering
language:en
Short-container-title:Energy Sci Eng

Author:

Petkovic Milena¹^ORCID,Koch Thorsten¹²^ORCID,Zittel Janina¹^ORCID

Affiliation:

1. Department for Applied Algorithmic Intelligence Methods Zuse Institute Berlin Berlin Germany

2. Chair of Software and Algorithms for Discrete Optimization Technische Universität Berlin Berlin Germany

Publisher

Wiley

Subject

General Energy,Safety, Risk, Reliability and Quality

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ese3.932

Reference54 articles.

1. International Energy Agency.Germany 2020 Energy policy review. Technical report International Energy Agency;2020.

2. Bundesnetzagentur.Monitoring report 2019. Technical report BUNDESKARTELLAMT;2019.

3. Working Group on Energy Balances.Evaluation tables on the energy balance for the federal republic of germany 1990 to 2019. Technical report Arbeitsgemeinschaft Energiebilanzen;2019.

4. Predicting residential energy consumption using CNN-LSTM neural networks

5. Deep Learning for Load Forecasting: Sequence to Sequence Recurrent Neural Networks With Attention

Cited by 8 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. FMM-VMD-Transformer: A hybrid deep learning model for predicting natural gas consumption;Digital Engineering;2024-09

2. Application of forecasting strategies and techniques to natural gas consumption: A comprehensive review and comparative study;Engineering Applications of Artificial Intelligence;2024-03

3. Deep Learning Based Leak Detection of Oil Transmission;2023 3rd International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA);2023-12-13

4. Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey;Energies;2023-07-14

5. Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM;Resources Policy;2023-01