Enhanced ensemble learning-based uncertainty and sensitivity analysis of ventilation rate in a novel radiative cooling building.

Majid Mohsenpour, Mohsen Salimi, Atieh Kermani, Majid Amidpour

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In one paragraph

Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Majid MohsenpourDepartment of Energy System Engineering, Faculty of Mechanical Engineering, K.N. Toosi University of Technology, No. 15, Pardis St., Molasadra Ave., Vanak Sq., Tehran, Iran.

Mohsen SalimiRenewable Energy Research Department, Niroo Research Institute (NRI), Tehran, Iran.

Atieh KermaniDepartment of Energy System Engineering, Faculty of Mechanical Engineering, K.N. Toosi University of Technology, No. 15, Pardis St., Molasadra Ave., Vanak Sq., Tehran, Iran.

Majid AmidpourDepartment of Energy System Engineering, Faculty of Mechanical Engineering, K.N. Toosi University of Technology, No. 15, Pardis St., Molasadra Ave., Vanak Sq., Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rising global demand for air conditioning systems, driven by increasing temperatures and urbanization, has led to higher energy consumption and greenhouse gas emissions. HVAC systems, particularly AC, account for nearly half of building energy use, highlighting the need for efficient cooling solutions. Passive cooling, especially radiative cooling, offers potential to reduce cooling loads and improve energy efficiency. However, most studies focus on idealized conditions, neglecting the real-world variability of indoor and outdoor environments. This study proposes a novel machine learning-based ensemble stacking model to predict ventilation rates in passive cooling buildings, addressing the challenges of black-box modeling. The model's performance is improved across key metrics such as R

Indexed as

Enhanced ensemble learningGlobal warmingNatural coolingSensitivity analysisUncertainty analysis

Identifiers

PMID39850439

DOI10.1016/j.heliyon.2024.e41572

PMCPMC11755033

What OpenQuestion holds

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.