Evidence map›Paper›PMID 40560986›Full record

ArticlePloS one2025

Wind resource assessment Offshore Fujian using 30-year wind estimates.

Wenfei Xue, Rongfu Li, Weidong Ji, Endi Zhai, Hongying Yang, Qiaozhen Ning, Xiaokai Hu, Guanghong Liao

Abstract read
In one paragraph

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

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

The trial behind it

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Wenfei XueGoldwind Science & Technology Co., Ltd, Urumqi, China.
Rongfu LiGoldwind Science & Technology Co., Ltd, Urumqi, China.
Weidong JiGoldwind Science & Technology Co., Ltd, Urumqi, China.
Endi ZhaiGoldwind Science & Technology Co., Ltd, Urumqi, China.
Hongying YangGoldwind Science & Technology Co., Ltd, Urumqi, China.
Qiaozhen NingGoldwind Science & Technology Co., Ltd, Urumqi, China.
Xiaokai HuNARI Technology Co. Ltd, Nanjing, China.
Guanghong LiaoKey Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing, China.ORCID 0000-0002-4646-7808

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

China has set ambitious goals for the development of offshore wind energy to meet the increasing energy needs of coastal provinces. The initial phase of offshore wind energy development involves evaluating the wind resource and identifying the most promising locations for wind farms. It is crucial to assess the characteristics and potential of wind energy beforehand. This study conducts a comprehensive assessment of offshore wind resource near Fujian China. Wind measurement devices were deployed at XiaPu and PingTan to collect wind profile data and meteorological conditions for one year. Various wind characteristics, including average wind speed, frequency of wind direction, wind shears and turbulence intensity were analyzed. An adaptive Measure‑Correlate‑Predict methodology was utilized to estimate wind conditions over 30‑years span. Measured Wind energy density values range from 3082.63 and 11753.52 kWh/m2/year. The peak daily average wind speeds are prevailing between 12 a.m. and 11 p.m with lower turbulence intensity and higher wind shear exponent, such condition is suitable for development of wind power. The variation in the wind shear exponent, and wind speed changes with the seasons. The 90th percentile of turbulence intensity was found to be below the standard set for IEC Class A + The extreme wind speed associated with a 50-year return period was 38.0m/s at a height of 100m, leading to the recommendation of wind turbine class II. However, taking into account the ambient turbulence intensity, it might be advisable to upgrade the turbine class to IEC Class A+.

Indexed as

Energy-Generating ResourcesWindChinaRenewable EnergySeasons

Identifiers

PMID40560986
PMCPMC12193848

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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.