Effectiveness of Forecasters Based on Neural Networks for Energy Management in Zero Energy Buildings DOI
Iván A. Hernández-Robles,

Xiomara González Ramírez,

J. A. Álvarez-Jaime

et al.

Published: Jan. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Language: Английский

Flexible coupling and grid-responsive scheduling assessments of distributed energy resources within existing zero energy houses DOI
Xiaoyi Zhang, Fu Xiao, Yanxue Li

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 87, P. 109047 - 109047

Published: March 16, 2024

Language: Английский

Citations

12
Recent advances in data mining and machine learning for enhanced building energy management DOI Creative Commons

Xinlei Zhou,

Han Du,

Shan Xue

et al.

Energy, Journal Year: 2024, Volume and Issue: 307, P. 132636 - 132636

Published: July 29, 2024

Due to the recent advancements in Internet of Things and data science techniques, a wide range studies have investigated use mining (DM) machine learning (ML) algorithms enhance building energy management (BEM). However, different classes DM ML feature mechanisms capabilities, resulting their distinct roles performance BEM. Appropriate integration categories BEM is essential promote application provide guidance for new topic areas. This study presents literature review techniques key areas BEM, including evaluation, usage prediction, demand flexibility optimization. The categorizes into three main categories, supervised DM, unsupervised reinforcement (RL). Unsupervised are primarily used assessment, while mainly employed benchmarking prediction. RL has been utilized optimal control improve efficiency, flexibility, indoor thermal comfort. strengths, shortcomings, these methods terms applications discussed, along with some suggestions future research this field.

Language: Английский

Citations

10
Chance-constrained stochastic framework for building thermal control under forecast uncertainties DOI
Parastoo Mohebi,

Shuhao Li,

Zhe Wang

et al.

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115385 - 115385

Published: Jan. 1, 2025

Language: Английский

Citations

0
Optimizing the hyper-parameters of deep reinforcement learning for building control DOI

Shuhao Li,

Shu Su,

Xu Lin

et al.

Building Simulation, Journal Year: 2025, Volume and Issue: unknown

Published: March 4, 2025

Language: Английский

Citations

0
Effectiveness of forecasters based on neural networks for energy management in zero energy buildings DOI
Iván A. Hernández-Robles,

Xiomara González-Ramírez,

J. A. Álvarez-Jaime

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: 316, P. 114372 - 114372

Published: June 1, 2024

Language: Английский

Citations

2
Review on Advanced Storage Control Applied to Optimized Operation of Energy Systems for Buildings and Districts: Insights and Perspectives DOI Creative Commons
Maria Ferrara, Matteo Bilardo, Dragos‐Ioan Bogatu

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(14), P. 3371 - 3371

Published: July 9, 2024

In the context of increasing energy demands and integration renewable sources, this review focuses on recent advancements in storage control strategies from 2016 to present, evaluating both experimental simulation studies at component, system, building, district scales. Out 426 papers screened, 147 were assessed for eligibility, with 56 included final review. As a first outcome, work proposes novel classification taxonomy update advanced systems, aiming bridge gap between theoretical research practical implementation. Furthermore, study emphasizes case studies, moving beyond numerical analyses provide insights. It investigates how literature is enhancing building flexibility resilience, highlighting application algorithms artificial intelligence methods their impact financial savings. By exploring correlation resulting benefits, provides comprehensive analysis current state future perspectives smart grids buildings.

Language: Английский

Citations

1
Effectiveness of Forecasters Based on Neural Networks for Energy Management in Zero Energy Buildings DOI
Iván A. Hernández-Robles,

Xiomara González Ramírez,

J. A. Álvarez-Jaime

et al.

Published: Jan. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Language: Английский

Citations

0