• Title, Summary, Keyword: Wind Power Generation Forecasting

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A Study on Estimation of Wind Power Generation using Weather Data in Jeju Island (기상관측자료를 이용한 제주도 풍력단지의 풍력발전량 예측에 관한 연구)

  • Ryu, Goo-Hyun;Kim, Ki-Su;Kim, Jae-Chul;Song, Kyung-Bin
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.58 no.12
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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.

Development of the Wind Power Forecasting System, KIER Forecaster (풍력발전 예보시스템 KIER Forecaster의 개발)

  • Kim, Hyun-Goo;Jang, Mun-Seok;Kyong, Nam-Ho;Lee, Yung-Seop
    • 한국신재생에너지학회:학술대회논문집
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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.

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Real-Time Peak Shaving Algorithm Using Fuzzy Wind Power Generation Curves for Large-Scale Battery Energy Storage Systems

  • Son, Subin;Song, Hwachang
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.14 no.4
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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.

A Study on Development of a Forecasting Model of Wind Power Generation for Walryong Site (월령단지 풍력발전 예보모형 개발에 관한 연구)

  • Kim, Hyun-Goo;Lee, Yeong-Seup;Jang, Mun-Seok;Kyong, Nam-Ho
    • Journal of the Korean Solar Energy Society
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    • v.26 no.2
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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.

Development of the Wind Power Forecasting System, KIER Forecaster (풍력발전 예보시스템 KIER Forecaster의 개발)

  • Kim Hyun-Goo;Lee Yung-Seop;Jang Mun-Seok;Kyong Nam-Ho
    • New & Renewable Energy
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    • v.2 no.2
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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.

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A Clustering Approach to Wind Power Prediction based on Support Vector Regression

  • Kim, Seong-Jun;Seo, In-Yong
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.12 no.2
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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.

Wind Power Pattern Forecasting Based on Projected Clustering and Classification Methods

  • Lee, Heon Gyu;Piao, Minghao;Shin, Yong Ho
    • ETRI Journal
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    • v.37 no.2
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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.

Forecasting of Short-term Wind Power Generation Based on SVR Using Characteristics of Wind Direction and Wind Speed (풍향과 풍속의 특징을 이용한 SVR기반 단기풍력발전량 예측)

  • Kim, Yeong-ju;Jeong, Min-a;Son, Nam-rye
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.42 no.5
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    • pp.1085-1092
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    • 2017
  • In this paper, we propose a wind forecasting method that reflects wind characteristics to improve the accuracy of wind power prediction. The proposed method consists of extracting wind characteristics and predicting power generation. The part that extracts the characteristics of the wind uses correlation analysis of power generation amount, wind direction and wind speed. Based on the correlation between the wind direction and the wind speed, the feature vector is extracted by clustering using the K-means method. In the prediction part, machine learning is performed using the SVR that generalizes the SVM so that an arbitrary real value can be predicted. Machine learning was compared with the proposed method which reflects the characteristics of wind and the conventional method which does not reflect wind characteristics. To verify the accuracy and feasibility of the proposed method, we used the data collected from three different locations of Jeju Island wind farm. Experimental results show that the error of the proposed method is better than that of general wind power generation.

Cluster Analysis and Meteor-Statistical Model Test to Develop a Daily Forecasting Model for Jejudo Wind Power Generation (제주도 일단위 풍력발전예보 모형개발을 위한 군집분석 및 기상통계모형 실험)

  • Kim, Hyun-Goo;Lee, Yung-Seop;Jang, Moon-Seok
    • Journal of Environmental Science International
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    • v.19 no.10
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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.

A Study on the Wind Data Analysis and Wind Speed Forecasting in Jeju Area (제주지역 바람자료 분석 및 풍속 예측에 관한 연구)

  • Park, Yun-Ho;Kim, Kyung-Bo;Her, Soo-Young;Lee, Young-Mi;Huh, Jong-Chul
    • Journal of the Korean Solar Energy Society
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    • v.30 no.6
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    • pp.66-72
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    • 2010
  • In this study, we analyzed the characteristics of wind speed and wind direction at different locations in Jeju area using past 10 years observed data and used them in our wind power forecasting model. Generally the strongest hourly wind speeds were observed during daytime(13KST~15KST) whilst the strongest monthly wind speeds were measured during January and February. The analysis with regards to the available wind speeds for power generation gave percentages of 83%, 67%, 65% and 59% of wind speeds over 4m/s for the locations Gosan, Sungsan, Jeju site and Seogwipo site, respectively. Consequently the most favorable periods for power generation in Jeju area are in the winter season and generally during daytime. The predicted wind speed from the forecast model was in average lower(0.7m/s) than the observed wind speed and the correlation coefficient was decreasing with longer prediction times(0.84 for 1h, 0.77 for 12h, 0.72 for 24h and 0.67 for 48h). For the 12hour prediction horizon prediction errors were about 22~23%, increased gradually up to 25~29% for 48 hours predictions.