Cited by Energy, environmental, economic, and social assessment of photovoltaic potential on expressway slopes: A case in Fujian Province, China

Machine Learning Applications in Building Energy Systems: Review and Prospects DOI

D. Li,

Zhenzhen Qi,

Yiming Zhou

et al.

Buildings, Journal Year: 2025, Volume and Issue: 15(4), P. 648 - 648

Published: Feb. 19, 2025

Building energy systems (BESs) are essential for modern infrastructure but face significant challenges in equipment diagnosis, consumption prediction, and operational control. The complexity of BESs, coupled with the increasing integration renewable sources, presents difficulties fault detection, accurate forecasting, dynamic system optimisation. Traditional control strategies struggle low efficiency, slow response times, limited adaptability, making it difficult to ensure reliable operation optimal management. To address these issues, researchers have increasingly turned machine learning (ML) techniques, which offer promising solutions improving scheduling, real-time BESs. This review provides a comprehensive analysis ML techniques applied According results literature review, supervised methods, such as support vector machines random forest, demonstrate high classification accuracy detection require extensive labelled datasets. Unsupervised approaches, including principal component clustering algorithms, robust identification capabilities without data may complex nonlinear patterns. Deep particularly convolutional neural networks long short-term memory models, exhibit superior forecasting Reinforcement further enhances management by dynamically adjusting parameters maximise efficiency cost savings. Despite advancements, remain terms availability, computational costs, model interpretability. Future research should focus on hybrid integrating explainable AI enhancing adaptability evolving demands. also highlights transformative potential BESs outlines future directions sustainable intelligent building

Language: Английский

Citations

A new paradigm based on Wasserstein Generative Adversarial Network and time-series graph for integrated energy system forecasting DOI

Zhirui Tian, Mei Gai

Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 326, P. 119484 - 119484

Published: Jan. 13, 2025

Language: Английский

Citations

Smart energy management for revenue optimization and grid independence in an Indian RDS DOI

T. Yuvaraj,

M. Thirumalai,

M. Dharmalingam

et al.

Energy Conversion and Management X, Journal Year: 2025, Volume and Issue: unknown, P. 100955 - 100955

Published: March 1, 2025

Language: Английский

Citations

Extreme outage prediction in power systems using a new deep generative Informer model DOI

Razieh Rastgoo, Nima Amjady, Syed Islam

et al.

International Journal of Electrical Power & Energy Systems, Journal Year: 2025, Volume and Issue: 167, P. 110627 - 110627

Published: March 23, 2025

Language: Английский

Citations

Statistical Foundations of Generative AI for Optimal Control Problems in Power Systems: Comprehensive Review and Future Directions DOI

Elinor Ginzburg-Ganz,

Eden Dina Horodi,

Omar Shadafny

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(10), P. 2461 - 2461

Published: May 11, 2025

With the rapid advancement of deep learning, generative artificial intelligence (Gen-AI) has emerged as a powerful tool, unlocking new prospects in power systems sector. Despite evident success these methods and growth this field community, there is still pressing need for deeper understanding how different evaluation metrics relate to underlying statistical structure models. Another related important question what tools can be used quantify uncertainties, which are inherent problems, stem not only from physical system but also nature model itself. This paper attempts address challenges provides comprehensive review existing models applied various tasks. We analyze align with properties explore their strengths limitations. examine sources uncertainty, distinguishing between uncertainties learning model, those arising measurement errors, other sources. Our general aim promote better they being support fascinating growing trend.

Language: Английский

Citations

A review of research on traction load models and modeling methods for electrified railways DOI

Yulong Che, Xiaoru Wang, Leijiao Ge

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2025, Volume and Issue: 219, P. 115869 - 115869

Published: May 24, 2025

Language: Английский

Citations

A new transfer evolutionary multi-task optimization algorithm for bi-level optimal configuration of distributed generations and energy storage systems considering uncertainties DOI

Chen Wang, Shangbin Jiao, Youmin Zhang

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 113, P. 115556 - 115556

Published: Feb. 5, 2025

Language: Английский

Citations

Optimizing Photovoltaic-Storage Building Energy Systems: A Comparative Study of Rule-Based and Reinforcement Learning Control for Grid Stability and Self-Consumption DOI

Xin Liu, Zhonghua Gou

Journal of Physics Conference Series, Journal Year: 2025, Volume and Issue: 3001(1), P. 012030 - 012030

Published: April 1, 2025

Abstract With the advancement of energy transition, adoption photovoltaic systems in residential buildings has been increasing. However, their intermittent and unstable nature poses challenges to grid stability. Integrating storage batteries into building emerged as a key solution enhance reliability. Despite this, optimizing battery charging discharging strategies achieve self-sufficiency, peak load shaving, supply-demand balance remains challenge. This study introduces two control strategies: Rule Based Control (RBC) approach Reinforcement Learning model using Proximal Policy Optimization (PPO). These dynamically coordinate PV generation, user demand operations reduce dependency minimize fluctuations. Firstly, physics-informed machine learning was developed accurately predict flows under varying states, enabling informed decision-making on feedback or consumption. Results from experiments with real data indicate that combined use physics-based models can building-grid usage an accuracy up 92%. Furthermore, compares effectiveness RBC PPO refining strategies. Performance evaluations case demonstrate both (28% 94%) (27% 86%) significantly self-consumption outperforming traditional methods (15% 38%). In terms operational strategies, exhibits superior performance over stabilizing enhancing controllability. research offers new insights for interactions supports deployment integrated PV-storage applications.

Language: Английский

Citations

Energy, environmental, economic, and social assessment of photovoltaic potential on expressway slopes: A case in Fujian Province, China DOI

Shuifa Lin,

Jianyi Lin, Rui Jing

et al.

Energy Reports, Journal Year: 2024, Volume and Issue: 12, P. 4374 - 4389

Published: Oct. 18, 2024

Language: Английский

Citations