Evidence map›Paper›PMID 38404842›Full record

ArticleHeliyon2024

Early energy performance analysis of smart buildings by consolidated artificial neural network paradigms.

Guoqing Guo, Peng Liu, Yuchen Zheng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Guoqing GuoXi'an Jiaotong-liverpool University, Xi'an, Shannxi, 215123, China.
Peng LiuXi'an Jiaotong-liverpool University, Xi'an, Shannxi, 215123, China.
Yuchen ZhengChenyu Technology (Wuhan) Co., LTD, Wuhan, Hubei, 430074, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The assessment of energy performance in smart buildings has emerged as a prominent area of research driven by the increasing energy consumption trends worldwide. Analyzing the attributes of buildings using optimized machine learning models has been a highly effective approach for estimating the cooling load (C

Indexed as

Artificial intelligenceEnergy performancePredictionSmart buildingsThermal loads

Identifiers

PMID38404842
PMCPMC10884448

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.