• 전기학회논문지
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• 제58권12호
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• pp.2349-2353
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• 2009

Due to high oil price and global warming of the earth, investments for renewable energy have been increased a lot continuously. Specially, wind power has been received a great attention in the world. In order to construct a new wind farm, forecasting of wind power generation is essential for a feasibility test. This paper investigates wind velocity measurement data of Gosan weather station which located in Hankyung of Jeju island. This paper presents results of estimation of wind power generation using digital weather forecast provided from Korea meteorological administration, and the accuracy of the wind power forecasting by comparison between forecasted data and actual wind power data.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2006년도 춘계학술대회
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• pp.323-324
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• 2006

In the present paper a forecasting system of wind power generation for Walryong Site, Jejudo is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model, KIER forecaster is constructed based on statistical models and is trained with wind speed data observed at Gosan Weather Station nearby Walryong Si to. Due to short period of measurements at Walryong Site for training statistical model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict technique. Three-hour advanced forecast ins shows good agreement with the measurement at Walryong site with the correlation factor 0.88 and MAE(mean absolute error) 15% under.

• KEPCO Journal on Electric Power and Energy
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• 제6권4호
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• pp.433-438
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• 2020

Accurate forecasting of wind power is important for grid operation. Wind power has intermittent and nonlinear characteristics, which increases the uncertainty in wind power generation. In order to accurately predict wind power generation with high uncertainty, it is necessary to analyze the factors affecting wind power generation. In this paper, 6 factors out of 11 are selected for more accurate wind power generation forecast. These are wind speed, sine value of wind direction, cosine value of wind direction, local pressure, ground temperature, and history data of wind power generated.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권4호
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• pp.305-312
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• 2014

This paper discusses real-time peak shaving algorithms for a large-scale battery energy storage system (BESS). Although several transmission and distribution functions could be implemented for diverse purposes in BESS applications, this paper focuses on a real-time peak shaving algorithm for an energy time shift, considering wind power generation. In a high wind penetration environment, the effective load levels obtained by subtracting the wind generation from the load time series at each long-term cycle time unit are needed for efficient peak shaving. However, errors can exist in the forecast load and wind generation levels, and the real-time peak shaving operation might require a method for wind generation that includes comparatively large forecasting errors. To effectively deal with the errors of wind generation forecasting, this paper proposes a real-time peak shaving algorithm for threshold value-based peak shaving that considers fuzzy wind power generation.

• 한국태양에너지학회 논문집
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• 제26권2호
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• pp.27-34
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• 2006

In this paper, a forecasting model of wind speed at Walryong Site, Jeju Island is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model is constructed based on neural network and is trained with wind speed data observed at Cosan Weather Station located near by Walryong Site. Due to short period of measurements at Walryong Site for training statistical model Gosan Weather Station's long-term data are substituted and then transplanted to Walryong Site by using Measure-Correlate-Predict technique. One to three-hour advance forecasting of wind speed show good agreements with the monitoring data of Walryong site with the correlation factors 0.96 and 0.88, respectively.

• 신재생에너지
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• 제2권2호
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• pp.37-43
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• 2006

In this paper, the first forecasting system of wind power generation, KIER Forecaster is presented. KIER Forecaster has been constructed based on statistical models and was trained with wind speed data observed at Gosan Weather Station nearby Walryong Site. Due to short period of measurements at Walryong Site for training the model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict(MCP) technique. The results of One to Three-hour advanced forecasting models are consistent with the measurement at Walryong site. In particular, the multiple regression model by classification of wind speed pattern, which has been developed in this work, shows the best performance comparing with neural network and auto-regressive models.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권2호
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• pp.108-112
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• 2012

A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly wind energy is unlimited in potential. However, due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. To cope with this, many works have been done for wind speed and power forecasting. It is reported that, compared with physical persistent models, statistical techniques and computational methods are more useful for short-term forecasting of wind power. Among them, support vector regression (SVR) has much attention in the literature. This paper proposes an SVR based wind speed forecasting. To improve the forecasting accuracy, a fuzzy clustering is adopted in the process of SVR modeling. An illustrative example is also given by using real-world wind farm dataset. According to the experimental results, it is shown that the proposed method provides better forecasts of wind power.

• ETRI Journal
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• 제37권2호
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• pp.283-294
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• 2015

A model that precisely forecasts how much wind power is generated is critical for making decisions on power generation and infrastructure updates. Existing studies have estimated wind power from wind speed using forecasting models such as ANFIS, SMO, k-NN, and ANN. This study applies a projected clustering technique to identify wind power patterns of wind turbines; profiles the resulting characteristics; and defines hourly and daily power patterns using wind power data collected over a year-long period. A wind power pattern prediction stage uses a time interval feature that is essential for producing representative patterns through a projected clustering technique along with the existing temperature and wind direction from the classifier input. During this stage, this feature is applied to the wind speed, which is the most significant input of a forecasting model. As the test results show, nine hourly power patterns and seven daily power patterns are produced with respect to the Korean wind turbines used in this study. As a result of forecasting the hourly and daily power patterns using the temperature, wind direction, and time interval features for the wind speed, the ANFIS and SMO models show an excellent performance.

• 한국통신학회논문지
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• 제42권5호
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• pp.1085-1092
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• 2017

본 논문은 풍력발전예측의 정확도 개선을 위하여 바람의 특성을 반영한 풍력발전량예측 방법을 제안한다. 제안한 방법은 크게 바람의 특성을 추출하는 부분과 발전량을 예측하는 부분으로 구성된다. 바람의 특성을 추출하는 부분은 발전량, 풍향과 풍속의 상관분석을 이용한다. 풍향과 풍속의 상관관계를 근거로 K-means 방법으로 클러스터링하여 특징 벡터를 추출한다. 예측하는 부분은 임의의 실수값을 예측 할 수 있도록 SVM을 일반화 한 SVR을 이용하여 기계학습을 한다. 기계학습은 바람의 특성을 반영한 제안한 방법과 바람의 특성을 반영하지 않은 기존방법을 비교 실험하였다. 또한, 제안한 방법의 정확도와 타당성을 검증하기 위하여 장소가 상이한 제주도 풍력발전단지 3지역에서 수집된 데이터를 사용하였다. 실험결과, 제안한 방법의 오차가 일반적인 풍력발전예측 오차보다 개선되었다.

• 한국환경과학회지
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• 제19권10호
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• pp.1229-1235
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• 2010

Three meteor-statistical forecasting models - the transfer function model, the time-series autoregressive model and the neural networks model - were tested to develop a daily forecasting model for Jejudo, where the need and demand for wind power forecasting has increased. All the meteorological observation sites in Jejudo have been classified into 6 groups using a cluster analysis. Four pairs of observation sites among them, all having strong wind speed correlation within the same meteorological group, were chosen for a model test. In the development of the wind speed forecasting model for Jejudo, it was confirmed that not only the use a wind dataset at the objective site itself, but the introduction of another wind dataset at the nearest site having a strong wind speed correlation within the same group, would enhance the goodness to fit of the forecasting. A transfer function model and a neural network model were also confirmed to offer reliable predictions, with the similar goodness to fit level.