• Journal of Korean Society for Atmospheric Environment
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• v.19 no.6
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• pp.745-756
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• 2003

An analysis of wind environments using computational fluid dynamics and an evaluation of wind resources using measurement data obtained from meteorological observation sites at Homi-Cape, Pohang have been carrid out for siting a wind farm. It was shown that a numerical simulation using computational fluid dynamics would provide reliable wind resource map in complex terrain with land-sea breeze condition. As a result of this investigation, Homi-Cape wind farm with 11.25 ㎿ capacity has been designed for maximum power generation and 25.7 GWh electricity production is predicted.

• Journal of Environmental Science International
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• v.18 no.8
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• pp.877-883
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• 2009

This study has analyzed the scale, location, resource potential and feasibility of offshore wind farm scientifically and systematically based on the national wind map and GIS. For long-term wind power development, this study pursues siting strategy building, selection of target area and deciding development priority as well as the presenting a basis for assessment that are necessary for policy decision making by making theme layers under GIS environment. According to the analysis after organizing technological development by stages, even if only the most suitable sites are developed among the area of offshore wind farm candidates that can be developed under the current technological standard, it has been evaluated as being able to develop about 3 times of the wind power dissemination target until 2012. It is expected that about 5% of territorial water area can be developed in a short-term future while the southern offshore area possessing relatively favorable wind resource than the western offshore has been identified as the most feasible site. While about 23% of territorial water area has been classified as potential area for offshore wind farm development in a long-term future, even Jeju Island and offshore of Ulsan possessing excellent wind resource have been analyzed as feasible sites. The feasibility assessment of offshore wind lam development established by this study is expected to assist national strategy building for accomplishing the wind power dissemination target.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.184.1-184.1
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• 2010

In order to research the way to evaluate wind resource without actual Met Mast data, this paper has been carried out on the southeastern region of Jeju island, Korea. Although wind turbine has been an economical alternative energy resource, misjudging the prediction of lifetime or payback period occurs because of the inaccurate assessment of wind resource and the location of wind turbine. Using WindPRO(Ver. 2.7), a software for wind farm design developed by EMD from Denmark, wind resources for the southeastern region of Jeju island was analyzed, and the performance of WindPRO prediction was evaluated in detail. Met Mast data in Su-san 5.5Km far from Samdal wind farm, AWS in Sung-san 4.5km far from Samdal wind farm, and Korea Wind Map data had been collected for this work.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.183-192
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• 2023

The remotely sensed data, such as satellite imagery and aerial photos, can be used to extract and detect some objects in the image through image interpretation and processing techniques. Significantly, the possibility for utilizing digital map updating and land monitoring has been increased through automatic object detection since spatial resolution of remotely sensed data has improved and technologies about deep learning have been developed. In this paper, we tried to extract plastic greenhouses into aerial orthophotos by using fully convolutional densely connected convolutional network (FC-DenseNet), one of the representative deep learning models for semantic segmentation. Then, a quantitative analysis of extraction results had performed. Using the farm map of the Ministry of Agriculture, Food and Rural Affairsin Korea, training data was generated by labeling plastic greenhouses into Damyang and Miryang areas. And then, FC-DenseNet was trained through a training dataset. To apply the deep learning model in the remotely sensed imagery, instance norm, which can maintain the spectral characteristics of bands, was used as normalization. In addition, optimal weights for each band were determined by adding attention modules in the deep learning model. In the experiments, it was found that a deep learning model can extract plastic greenhouses. These results can be applied to digital map updating of Farm-map and landcover maps.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.664-667
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• 2004

This paper is aimed at analyzing the soil erosion hazard zone in farm land. RUSLE was used for an analysis of soil erosion amount, and for the spatial data of basin, soil erosion amount was calculated by extracting the respect topography space related factors of RUSLE using DEM, Landuse, Soil map as base map. As a result of analysis on the calculated soil erosion amount according to land use type, it was analyzed that the most soil erosion occurred in orchard area, i.e., 40.08ton/ha/yr at average. It was classified into 5 classes depending on the calculated soil erosion amount. of which Class V was decided as soil erosion hazard zone, and for this area, 72.5ha or so, $2.4\%$ of the entire farm land was assessed as erosion hazard zone.

• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.286-290
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• 2011

This paper introduces the optimum macro-siting of a potential site for an offshore wind farm around Jeju Island using the RDAPS sea wind model. The statistical model was developed by analyzing the sea wind data from RDAPS model, and the meso-scale digital wind map was prepared. To develop the high resolution spatial calibration model, Artificial Neural Network(ANN) models were used to construct the wind and bathymetric maps. Accuracy and consistency of wind/bathymetric spatial calibration models were obtained using analysis of variance. The optimization problem was defined to maximize the energy density satisfying the criteria of maximum water depth and maximum distance from the coastline. The candidate site was selected through Genetic Algorithm(GA). From the results, it is possible to predict roughly a candidate site location for the installation of the offshore wind jam, and to evaluate the wind resources of the proposed site.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.460-462
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• 2009

This study has analyzed the scale, location, resource potential and feasibility of offshore wind farm scientifically and systematically based on the national wind map and GIS (Geographic Information System). For long-term wind power development, this study pursues siting strategy building, selection of target area and deciding development priority as well as the presenting a basis for assessment that are necessary for policy decision making by making theme layers under GIS environment. According to the analysis after organizing technological development by stages, even if only the most suitable sites are developed among the area of offshore wind farm candidates that can be developed under the current technological standard, it has been evaluated as being able to develop about 3 times of the wind power dissemination target until 2012. It is expected that about 5% of territorial water area can be developed in a short-term future while the southern offshore area possessing relatively favorable wind resource than the western offshore has been identified as the most feasible site. While about 23% of territorial water area has been classified as potential area for offshore wind farm development in a long-term future, even Jeju Island and offshore of Ulsan possessing excellent wind resource have been analyzed as feasible sites. The feasibility assessment of offshore wind farm development established by this study is expected to assist national strategy building for accomplishing the wind power dissemination target.

• Wind and Structures
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• v.13 no.4
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• pp.375-383
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• 2010

A three-dimensional flow simulation was performed to investigate the wind flow around wind-power generation facilities on mountainous area of complex terrain. A digital map of eastern mountainous area of Korea including a wind farm was used to model actual complex terrain. Rotating wind turbines in the wind farm were also modeled in the computational domain with detailed geometry of blade by using the frozen rotor method. Wind direction and speed to be used as a boundary condition were taken from local meteorological reports. The numerical results showed not only details of flow distribution in the wind farm but also the variation in the performance of the wind turbines due to the installed location of the turbines on complex terrain. The wake effect of the upstream turbine on the performance of the downstream one was also examined. The methodology presented in this study may be used in selecting future wind farm site and wind turbine locations in the selected site for possible maximum power generation.

• Journal of Information Technology Services
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• v.11 no.4
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• pp.123-134
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• 2012

Recently, The Bali Road Map was approved, as it demands that developing countries should also have the responsibility of greenhouse gas reduction from 2013. This suggests that the greenhouse gas and environment should be controlled across industry sectors. Accordingly, this study was conducted to identify the application and effects of the IT convergence technology to the smart farm and realize the low-carbon green industry in Korea. The smart farm technologies within and outside of Korea were comparatively analyzed for the low-carbon green industry policy. The study subjects were determined to propose the necessity of the study efficiently. First, the studies on the smart farm for low-carbon green industry policy were examined. Second, the suitable IT technology for the smart farm as well as the effect and the improvement plan of the IT technology-based smart farm system were examined. This study now aims to promote the low-carbon green industry policy and IT convergence technology and job creation. These will be achieved by providing the plan for linking the system simulator organization with the low-carbon green industry policy.

• Journal of Wind Energy
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• v.9 no.4
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• pp.32-39
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• 2018

In this study, the annual power production of a wind farm according to obstacles and wind data was predicted for the Garyeok-do wind farm in the Saemangeum area. The Saemangeum Garyeok-do wind farm was built in December 2014 by the Korea Rural Community Corporation. Currently, two 1.5 MW wind turbines manufactured by Hyundai Heavy Industries are installed and operated. Automatic weather station data from 2015 to 2017 was used as wind data to predict the annual power production of the wind farm for three consecutive years. For prediction, a commercial computational fluid dynamics tool known to be suitable for wind energy prediction in complex terrain was used. Predictions were made for three cases with or without considering obstacles and wind direction errors. The study found that by considering both obstacles and wind direction errors, prediction errors could be substantially reduced. The prediction errors were within 2.5 % or less for all three years.