A novel method for long-term power demand prediction using enhanced data decomposition and neural network with integrated uncertainty analysis: A Cuba case study DOI
Manuel Soto Calvo, Han Soo Lee, Sylvester William Chisale

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 372, P. 123864 - 123864

Published: July 9, 2024

Language: Английский

Enhancing accuracy in point-interval load forecasting: A new strategy based on data augmentation, customized deep learning, and weighted linear error correction DOI
Weican Liu, Zhirui Tian, Yuyan Qiu

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126686 - 126686

Published: Feb. 1, 2025

Language: Английский

Citations

4
Enhanced load forecasting for distributed multi-energy system: A stacking ensemble learning method with deep reinforcement learning and model fusion DOI

Xiaoxiao Ren,

Xin Tian, Kai Wang

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135031 - 135031

Published: Feb. 1, 2025

Language: Английский

Citations

4
AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings DOI Creative Commons

Dalia Mohammed Talat Ebrahim Ali,

Violeta Motuzienė, Rasa Džiugaitė-Tumėnienė

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(17), P. 4277 - 4277

Published: Aug. 27, 2024

Despite the tightening of energy performance standards for buildings in various countries and increased use efficient renewable technologies, it is clear that sector needs to change more rapidly meet Net Zero Emissions (NZE) scenario by 2050. One problems have been analyzed intensively recent years operation much than they were designed to. This problem, known as gap, found many often attributed poor management building systems. The application Artificial Intelligence (AI) Building Energy Management Systems (BEMS) has untapped potential address this problem lead sustainable buildings. paper reviews different AI-based models proposed applications with intention reduce consumption. It compares evaluated reviewed papers presenting accuracy error rates model identifies where greatest savings could be achieved, what extent. review showed offices (up 37%) when employ AI HVAC control optimization. In residential educational buildings, lower intelligence existing BEMS results smaller 23% 21%, respectively).

Language: Английский

Citations

15
A deep learning integrated framework for predicting stock index price and fluctuation via singular spectrum analysis and particle swarm optimization DOI
Chia‐Hung Wang, Jinchen Yuan,

Yingping Zeng

et al.

Applied Intelligence, Journal Year: 2024, Volume and Issue: 54(2), P. 1770 - 1797

Published: Jan. 1, 2024

Language: Английский

Citations

13
An attention-based multi-input LSTM with sliding window-based two-stage decomposition for wind speed forecasting DOI
Dongchuan Yang, Mingzhu Li, Ju’e Guo

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 375, P. 124057 - 124057

Published: Aug. 9, 2024

Language: Английский

Citations

13
Multi-energy load forecasting via hierarchical multi-task learning and spatiotemporal attention DOI
Cairong Song,

Haidong Yang,

Jianyang Cai

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 373, P. 123788 - 123788

Published: July 14, 2024

Language: Английский

Citations

10
Integrated energy short-term multivariate load forecasting based on PatchTST secondary decoupling reconstruction for progressive layered extraction multi-task learning network DOI
Zhijian Qu,

Yan Meng,

Xinxing Hou

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: 269, P. 126446 - 126446

Published: Jan. 7, 2025

Language: Английский

Citations

1
Application of artificial neural networks in predicting the performance of ice thermal energy storage systems DOI Creative Commons

O.Y. Odufuwa,

Lagouge K. Tartibu, K. Kusakana

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 95, P. 112547 - 112547

Published: June 14, 2024

Language: Английский

Citations

6
A federated and transfer learning based approach for households load forecasting DOI
Gurjot Singh, Jatin Bedi

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 299, P. 111967 - 111967

Published: May 24, 2024

Language: Английский

Citations

4
Analysis of aggregated load consumption forecasting in short, medium and long term horizons using Dynamic Mode Decomposition DOI Creative Commons
Marc Carrillo Muñoz, Mònica Aragüés‐Peñalba, Antonio E. Saldaña-González

et al.

Energy Reports, Journal Year: 2024, Volume and Issue: 12, P. 1000 - 1013

Published: July 10, 2024

Language: Английский

Citations

4