Investigation of Data Size Variability in Wind Speed Prediction Using AI Algorithms.

M A Ehsan, Amir Shahirinia, Nian Zhang, Timothy Oladunni

Abstract read

In one paragraph

Article in Cybernetics and systems, 2021. 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.

Care Needs and Symptoms Burden of Breast Cancer Patients in Jordan: A Cross-Sectional Study.International journal of environmental research and public health · 2022
Article

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

4 authors.

M A EhsanDepartment of Electrical and Computer Engineering, University of the District of Columbia, Washington, DC, USA.

Amir ShahiriniaDepartment of Electrical and Computer Engineering, University of the District of Columbia, Washington, DC, USA.

Nian ZhangDepartment of Electrical and Computer Engineering, University of the District of Columbia, Washington, DC, USA.

Timothy OladunniDepartment of Computer Science & Information Technology, University of the District of Columbia, Washington, DC, USA.

Funding

NIA MSTEM: Advancing Diversity in Aging Research through Undergraduate Education at the University of the District of ColumbiaR25AG067896 · NIA · UNIVERSITY OF THE DISTRICT OF COLUMBIA · PI THOMPSON, LARA A · 2020 to 2024

$1.5M

NIA NIH HHS R25 AG067896

6 · The paper itself

Abstract

Electricity generation from burning fossil fuel is one of the major contributors to global warming. Renewable energy sources are a viable alternative to produce electrical energy and to reduce the emission from power industry. They have unlocked opportunities for consumers to produce electricity locally and use it on-site that reduces dependency on centralized generation. Despite the widespread availability, one of the major challenges is to understand their characteristics in a more informative way. Wind energy is highly dependent on the intermittent wind speed profile. This paper proposes the prediction of wind speed that simplifies wind farm planning and feasibility study. Twelve artificial intelligence algorithms were used for wind speed prediction from collected meteorological parameters. The model performances were compared to determine the wind speed prediction accuracy and model comparison for different sizes of data set. The results show, the most effective algorithm varies based on the data size.

Indexed as

Convolutional neural networksdeep learninglong short-term memory (LSTM)wind speed prediction

Identifiers

PMID38500540

DOI10.1080/01969722.2020.1827796

PMCPMC10947156

What OpenQuestion holds

Textmetadata

LicenceTDM

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.