Probabilistic load forecasting for integrated energy systems using attentive quantile regression temporal convolutional network-Reference-Cited by-同舟云学术

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Probabilistic load forecasting for integrated energy systems using attentive quantile regression temporal convolutional network

Published:2024-07 Issue: Volume:14 Page:100165
ISSN:2666-7924
Container-title:Advances in Applied Energy
language:en
Short-container-title:Advances in Applied Energy

Author:

Guo Han,Huang Bin^ORCID,Wang Jianhui

Publisher

Elsevier BV

Reference43 articles.

1. Electric/thermal hybrid energy storage planning for park-level integrated energy systems with second-life battery utilization;Guo;Adv Appl Energy,2021

2. High technical and temporal resolution integrated energy system modelling of industrial decarbonisation;Manuel;Adv Appl Energy,2022

3. Dynamic energy flow analysis of integrated gas and electricity systems using the holomorphic embedding method;Zhang;Appl Energy,2022

4. Crystal ball lessons in predictive analytics;Hong;EnergyBiz Mag,2015

5. Short-term load forecasting with deep residual networks;Chen;IEEE Trans Smart Grid,2018

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. Analyzing variability in urban energy poverty: A stochastic modeling and Monte Carlo simulation approach;Energy;2024-09

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