Global offshore wind turbine dataset

Zhang, Ting; Tian, Bo; Sengupta, Dhritiraj; Zhang, Lei; Si, Youbin

doi:10.1038/s41597-021-00982-z

Cited by 46 publications

(31 citation statements)

References 22 publications

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“…Hence, a foundation of an OWT has to be differentiated from a foundation with a turbine on top. The earlier mentioned studies by Wong et al (2019), Xu et al (2020) and Zhang et al (2021) do not differentiate between these deployment stages. By classifying turbine foundations and readily deployed turbines both as operational OWTs, the number of OWTs in these data sets suffers from temporal overestimation, which can be seen in Fig.…”

Section: Discussionmentioning

confidence: 86%

“…They deployed their algorithm on the Google Earth Engine and detected oil rigs in the Gulf of Mexico and offshore wind turbines in the exclusive economic zones of the UK and China. Zhang et al (2021) processed the Sentinel-1 archive on a global scale and provided OWT locations by applying a morphological approach in combination with multiple thresholds to remove false positives. In addition to the spatial locations, the estimated first appearance of an OWT between 2014 and 2019 is provided in their study.…”

Section: Related Researchmentioning

confidence: 99%

“…The data sets reviewed are the DeepOWT data set , OWT locations from the Open Street Map (OSM) database and the global offshore wind turbine data set (GOWT) presented by Zhang et al (2021), in its original version (v1.3) and the current version after the latest update (v2021). All data sets, except GOWT v1.3, were compared with the ground truth datasets describing the status at the end of the second quarter of 2021.…”

Section: Declaration Of Competing Interestmentioning

confidence: 99%

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Global dynamics of the offshore wind energy sector monitored with Sentinel-1: Turbine count, installed capacity and site specifications

Hoeser¹,

Kuenzer²

2022

Preprint

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With the promotion of renewable energy production and a planned phaseout of fossil fuels until 2040, the offshore wind energy sector has started to expand and will continue to increase its capacity in the upcoming decades. This study presents how the installed capacity can be derived from radar imagery provided by the Sentinel-1 mission for all offshore wind turbines on the entire Earth. By further combining freely available Earth observation and GIS data, commonly reported attributes of the offshore wind energy sector are compiled. All attributes are investigated to provide an in-depth overview of the developments of the offshore wind energy sector over the last five years. Between 2016 and 2021, the installed capacity worldwide grew from 13.5 GW to 40.6 GW. This corresponds to an increase of 27.1 GW or 200\%. In total 8,885 offshore wind turbines (OWTs) were installed until June 2021 with an additional 852 under construction. The European Union (15.2 GW), China (14.1 GW) and the United Kingdom (10.7 GW) are the three major contributors to the offshore wind energy sector. China has seen the largest growth in the last five years of 13 GW, followed by the EU with 8 GW and the UK with 5.8 GW. The provided in-depth analysis at the end of this study describes the offshore wind energy sector to be in a transition phase between decades of maturity and massive growth at a time when carbon-neutral energy production is massively supported. Overall the proposed methods for independent offshore wind turbine capacity estimation and spatiotemporal investigation of the offshore wind energy sector can be used by all stakeholders involved in the upcoming challenge of integrated planning and implementation of offshore wind energy projects.

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Section: Discussionmentioning

confidence: 86%

Section: Related Researchmentioning

confidence: 99%

Section: Declaration Of Competing Interestmentioning

confidence: 99%

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Global dynamics of the offshore wind energy sector monitored with Sentinel-1: Turbine count, installed capacity and site specifications

Hoeser¹,

Kuenzer²

2022

Preprint

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“…Zhang et al . presented the global offshore wind turbine dataset 15 . There is a platform called OpenStreetMap that is used to recreate new versions of wind and solar installation datasets 16 .…”

Section: Background and Summarymentioning

confidence: 99%

Solar and wind power data from the Chinese State Grid Renewable Energy Generation Forecasting Competition

Chen

2022

Sci Data

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Accurate solar and wind generation forecasting along with high renewable energy penetration in power grids throughout the world are crucial to the days-ahead power scheduling of energy systems. It is difficult to precisely forecast on-site power generation due to the intermittency and fluctuation characteristics of solar and wind energy. Solar and wind generation data from on-site sources are beneficial for the development of data-driven forecasting models. In this paper, an open dataset consisting of data collected from on-site renewable energy stations, including six wind farms and eight solar stations in China, is provided. Over two years (2019–2020), power generation and weather-related data were collected at 15-minute intervals. The dataset was used in the Renewable Energy Generation Forecasting Competition hosted by the Chinese State Grid in 2021. The process of data collection, data processing, and potential applications are described. The use of this dataset is promising for the development of data-driven forecasting models for renewable energy generation and the optimization of electricity demand response (DR) programs for the power grid.

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“…Since the deployment of OWFs is a global issue, the monitoring has to be globally too. Thus, Earth observation imagery is the most promising data source to provide a global, independent, constant and automatic OWF monitoring product in time [45]. To process a global dataset frequently, efficient processing is indispensable, and at the same time, the model's spatial transferability must be guaranteed to avoid high false positive or false negative rates.…”

Section: Offshore Windfarm Detection With Synteomentioning

confidence: 99%

SyntEO: Synthetic Data Set Generation for Earth Observation and Deep Learning -- Demonstrated for Offshore Wind Farm Detection

Hoeser¹,

Kuenzer²

2021

Preprint

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With the emergence of deep learning in the last years, new opportunities arose in Earth observation research. Nevertheless, they also brought with them new challenges. The data-hungry training processes of deep learning models demand large, resource expensive, annotated datasets and partly replaced knowledge-driven approaches, so that model behaviour and the final prediction process became a black box. The proposed SyntEO approach enables Earth observation researchers to automatically generate large deep learning ready datasets and thus free up otherwise occupied resources. SyntEO does this by including expert knowledge in the data generation process in a highly structured manner. In this way, fully controllable experiment environments are set up, which support insights in the model training. Thus, SyntEO makes the learning process approachable and model behaviour interpretable, an important cornerstone for explainable machine learning. We demonstrate the SyntEO approach by predicting offshore wind farms in Sentinel-1 images on two of the worlds largest offshore wind energy production sites. The largest generated dataset has 90,000 training examples. A basic convolutional neural network for object detection, that is only trained on this synthetic data, confidently detects offshore wind farms by minimising false detections in challenging environments. In addition, four sequential datasets are generated, demonstrating how the SyntEO approach can precisely define the dataset structure and influence the training process. SyntEO is thus a hybrid approach that creates an interface between expert knowledge and data-driven image analysis.

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Global offshore wind turbine dataset

Cited by 46 publications

References 22 publications

Global dynamics of the offshore wind energy sector monitored with Sentinel-1: Turbine count, installed capacity and site specifications

Global dynamics of the offshore wind energy sector monitored with Sentinel-1: Turbine count, installed capacity and site specifications

Solar and wind power data from the Chinese State Grid Renewable Energy Generation Forecasting Competition

SyntEO: Synthetic Data Set Generation for Earth Observation and Deep Learning -- Demonstrated for Offshore Wind Farm Detection

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