A Hybrid Power Load Forecasting Framework with Attention-Based Network and Multi-Scale Decomposition DOI
Jiaming Zhu, Dezhi Liu, Lili Niu

et al.

Published: Jan. 1, 2024

Language: Английский

CNNs-Transformer based day-ahead probabilistic load forecasting for weekends with limited data availability DOI
Zhirui Tian, Weican Liu, Wenqian Jiang

et al.

Energy, Journal Year: 2024, Volume and Issue: 293, P. 130666 - 130666

Published: Feb. 10, 2024

Language: Английский

Citations

35
Optimising post-disaster waste collection by a deep learning-enhanced differential evolution approach DOI Creative Commons
Maziar Yazdani, Kamyar Kabirifar, Milad Haghani

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 132, P. 107932 - 107932

Published: Jan. 31, 2024

In the aftermath of natural disasters, efficient waste collection becomes a crucial challenge, owing to dynamic and unpredictable nature generation, coupled with resource constraints. This paper presents an innovative hybrid methodology that synergizes Long Short-Term Memory (LSTM) machine learning Differential Evolution (DE) optimisation augment efforts post-disaster. The approach leverages real-time data forecast generation high accuracy, facilitating development adaptable strategies. Our is designed dynamically update plans in response evolving scenarios, ensuring timely effective decision-making. Field tests conducted earthquake-prone city have demonstrated superior performance this method managing under fluctuating conditions. Moreover, in-depth sensitivity analysis helps identifying key areas for improvement. Significantly outperforming traditional models, offers substantial time savings equips disaster teams robust tool addressing challenges collection.

Language: Английский

Citations

14
A coupled framework for power load forecasting with Gaussian implicit spatio temporal block and attention mechanisms network DOI
Dezhi Liu, Xuan Lin,

Hanyang Liu

et al.

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110263 - 110263

Published: March 20, 2025

Language: Английский

Citations

1
Comparison of energy consumption prediction models for air conditioning at different time scales for large public buildings DOI
Jianwen Liu,

Zhihong Zhai,

Yuxiang Zhang

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 96, P. 110423 - 110423

Published: Aug. 14, 2024

Language: Английский

Citations

4
Mitigating concept drift challenges in evolving smart grids: An adaptive ensemble LSTM for enhanced load forecasting DOI
Abdul Azeem, Idris Ismail,

Syed Sheeraz Mohani

et al.

Energy Reports, Journal Year: 2025, Volume and Issue: 13, P. 1369 - 1383

Published: Jan. 15, 2025

Language: Английский

Citations

0
Online decoupling feature framework for optimal probabilistic load forecasting in concept drift environments DOI
Chaojin Cao, Yaoyao He, Xiaodong Yang

et al.

Applied Energy, Journal Year: 2025, Volume and Issue: 392, P. 125952 - 125952

Published: April 25, 2025

Language: Английский

Citations

0
HybridDep: An elastic hybrid resources allocation strategy for I/O‐intensive applications DOI Creative Commons
Pengmiao Li, Yuchao Zhang,

Sang Y. Yun

et al.

IET Communications, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 1, 2025

Abstract Along with the rapid development of B5G/6G, number applications grows rapidly and data amount explodes exponentially, putting a massive burden on resource‐limited edge servers. To fully utilize limited resources, virtualization technology is introduced to provide elastic deployment for in But I/O‐intensive applications, allocating resources not as easy compute‐intensive ones, because required I/O unknown due request uncertainty. Many existing researches try solve this multi‐application problem by peaks clipping valleys filling, resource utilization. However, fact, times most hybrid deployed are similar each other, which invalidates those traditional solutions. address challenge, actual analysed complementary peak valley periods time space dimensions found. Based finding, an strategy HybridDep proposed, multiple applications. Validated simulation experiments using real datasets traces, algorithm can reduce about 3.2% cost than compared algorithm.

Language: Английский

Citations

0
Short-Term Electricity-Load Forecasting by deep learning: A comprehensive survey DOI
Qi Dong, Rubing Huang, Chenhui Cui

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 154, P. 110980 - 110980

Published: May 6, 2025

Language: Английский

Citations

0
Transfer-AE: A novel Autoencoder-based Impact Detection Model for Structural Digital Twin DOI
Chengjia Han, Zixin Wang, Yuguang Fu

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 166, P. 112174 - 112174

Published: Aug. 30, 2024

Language: Английский

Citations

3
Multivariable correlation feature network construction and health condition assessment for unlabeled single-sample data DOI

Dan Xu,

Xiaoqi Xiao,

Jianguo Zhang

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108220 - 108220

Published: March 22, 2024

Language: Английский

Citations

2