Predictive optimization using long short-term memory for solar PV and EV integration in relatively cold climate energy systems with a regional case study DOI Creative Commons
Tao Hai,

Ali B.M. Ali,

Diwakar Agarwal

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 12, 2025

The global shift toward sustainable energy and electric mobility addresses environmental concerns related to fossil fuels. While these alternatives are increasingly utilized in residential commercial sectors, integrating renewable building systems presents significant challenges. This is particularly evident cold regions where unpredictable resource availability complicates reliability. study emphasizes the need for innovative approaches address complexities ensure consistent performance dynamic conditions. research explores dynamics within a community located relatively climate region (Tabriz). begins by estimating requirements of individual buildings, including additional demand generated vehicles. It then evaluates potential solar generation from photovoltaic systems. Finally, machine learning-based approach (i.e., LSTM, Long Short-Term Memory) employed optimize management supply across community. demonstrates that heating demands substantially higher than cooling needs, with providing sufficient (~ 32.1%) coverage during warmer months but requiring grid support colder seasons. prediction EV charging patterns using LSTM models achieved over 93% accuracy, enabling improved forecasting load management. These findings highlight optimizing use, reducing dependency, enhancing efficiency through effective production-demand

Language: Английский

Pythagorean fuzzy-based integration of ANP with TOPSIS -VIKOR-SAW techniques for hospital service quality evaluation DOI

Yograj Singh,

Dinesh C. S. Bisht

OPSEARCH, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 9, 2025

Language: Английский

Citations

0
Flood vulnerability assessment in the Ili River Basin based on the comprehensive symmetric Kullback–Leibler distance DOI Creative Commons
Jinghui Liu, Yanmin Li,

Xinyue Yuan

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 3, 2025

In vulnerability assessments, accurately determining the indicator weights is essential to ensure results' precision and reliability. This paper proposes an optimized comprehensive symmetric Kullback–Leibler (K–L) distance weighting method, in which K–L for each calculated using a grid-based approach, normalized serves as weight indicator. ArcGIS software was employed assess Ili River Basin flood case study. The results reveal following: (1) method facilitated variable processing disaster where it offered scientific adaptable approach indexing vulnerability, thus improving both evaluation accuracy practicality. (2) spatial distribution of levels uneven, with higher observed northwestern, southwestern, southeastern regions, lower eastern northeastern areas. Yining County, City, certain southern regions Cocodala City were particularly vulnerable due multiple influencing factors, including population, economy, society. These areas require focused attention preventive measures.

Language: Английский

Citations

0
A grey-CoCoSo-based approach for service quality evaluation of health-care units DOI
Santonab Chakraborty, Rakesh D. Raut,

T. M. Rofin

et al.

International Journal of Pharmaceutical and Healthcare Marketing, Journal Year: 2025, Volume and Issue: unknown

Published: March 25, 2025

Purpose Like all other service industries, evaluation of quality in health-care units is a complex decision-making task involving multiple stakeholder groups with varying interest, conflicting qualitative criteria and competing units. The past researchers have already attempted to solve this problem while integrating different uncertainty models various multi-criteria (MCDM) tools. This paper aims propose application an MCDM method for evaluating uncertain environment. Design/methodology/approach presents integrated approach combining grey numbers combined compromise solution (G-CoCoSo) appraising six Urban Primary Health Centers (UPHCs) Kolkata, India, based on the opinions three (health-care recipients, medical officers administrators) against subjective (tangibles, responsiveness, service, assurance, empathy hygiene). A sensitivity analysis also performed investigate effect values λ ranking performance G-CoCoSo method. Findings Based collective judgments expressed numbers, “tangibles” identified as most important criterion, followed by “responsiveness”. On hand, “assurance” criterion has least importance. singles out H3 best UPHC, H1 . contrary, H5 appears worst performing UPHC. results prove that insensitive changing Similarly, comparative study state-of-the-art methods validates its accuracy. Originality/value To authors’ knowledge, used first time demonstrating satisfactory results. It would assist both professionals patients identifying relative strengths weaknesses each UPHCs under consideration.

Language: Английский

Citations

0
Predictive optimization using long short-term memory for solar PV and EV integration in relatively cold climate energy systems with a regional case study DOI Creative Commons
Tao Hai,

Ali B.M. Ali,

Diwakar Agarwal

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 12, 2025

The global shift toward sustainable energy and electric mobility addresses environmental concerns related to fossil fuels. While these alternatives are increasingly utilized in residential commercial sectors, integrating renewable building systems presents significant challenges. This is particularly evident cold regions where unpredictable resource availability complicates reliability. study emphasizes the need for innovative approaches address complexities ensure consistent performance dynamic conditions. research explores dynamics within a community located relatively climate region (Tabriz). begins by estimating requirements of individual buildings, including additional demand generated vehicles. It then evaluates potential solar generation from photovoltaic systems. Finally, machine learning-based approach (i.e., LSTM, Long Short-Term Memory) employed optimize management supply across community. demonstrates that heating demands substantially higher than cooling needs, with providing sufficient (~ 32.1%) coverage during warmer months but requiring grid support colder seasons. prediction EV charging patterns using LSTM models achieved over 93% accuracy, enabling improved forecasting load management. These findings highlight optimizing use, reducing dependency, enhancing efficiency through effective production-demand

Language: Английский

Citations

0