• 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.

• East Asian Journal of Business Economics (EAJBE)
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• v.3 no.3
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• pp.14-26
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• 2015

A wind speed forecast is a crucial and sophisticated task in a wind farm for planning turbines and corresponds to an estimate of the expected production of one or more wind turbines in the near future. By production is often meant available power for wind farm considered (with units KW or MW depending on both the wind speed and direction. Such forecasts can also be expressed in terms of energy, by integrating power production over each time interval. In this study, we technically focused on mathematical modeling of wind speed and direction forecast based on locally data set gathered from Aghdasiyeh station in Tehran. The methodology is set on using most common techniques derived from literature review. Hence we applied the most sophisticated forecasting methods to embed seasonality, trend, and irregular pattern for wind speed as an angular variables. Through this research, we carried out the most common techniques such as the Box and Jenkins family, VARMA, the component method, the Weibull function and the Fourier series. Finally, the best fit for each forecasting method validated statistically based on white noise properties and the final comparisons using residual standard errors and mean absolute deviation from real data.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Journal of the Korean Solar Energy Society
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• v.36 no.1
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• pp.27-37
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• 2016

A simple but practical Ensemble Prediction System(EPS) for wind power forecasting was developed and evaluated using the measurement of the offshore meteorological tower, HeMOSU-1(Herald of Meteorological and Oceanographic Special Unite-1) installed at the Southwest Offshore in South Korea. The EPS developed by the Korea Institute of Energy Research is based on a simple ensemble mean of two Numerical Weather Prediction(NWP) models, WRF-NMM and WRF-ARW. In addition, the Kalman Filter is applied for real-time quality improvement of wind ensembles. All forecasts with EPS were analyzed in comparison with the HeMOSU-1 measurements at 97 m above sea level during Typhoon Bolaven episode in August 2012. The results indicate that EPS was in the best agreement with the in-situ measurement regarding (peak) wind speed and cut-out speed incidence. The RMSE of wind speed was 1.44 m/s while the incidence time lag of cut-out wind speed was 0 hour, which means that the EPS properly predicted a development and its movement. The duration of cut-out wind speed period by the EPS was also acceptable. This study is anticipated to provide a useful quantitative guide and information for a large-scale offshore wind farm operation in the decision making of wind turbine control especially during a typhoon episode.

• 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.

• Journal of the Korean Solar Energy Society
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• v.30 no.3
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• pp.25-32
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• 2010

A real-time monitoring system was developed for transfer, receive, backup and analysis of wind power data at three wind farm(Hang won, Hankyung and Sung san) in Jeju. For this monitoring system a communication system analysis, a collection of data and transmission module development, data base construction and data analysis and management module was developed, respectively. These modules deal with mechanical, electrical and environmental problem. Especially, time series graphic is supported by the data analysis and management module automatically. The time series graphic make easier to raw data analysis. Also, the real-time monitoring system is connected with wind power forecasting system through internet web for data transfer to wind power forecasting system's data base.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1373-1383
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• 2016

The wind energy industry and wind power generation have increased; consequently, the stable supply of the wind power has become an important issue. It is important to accurately predict the wind power with short-term basis in order to make a reliable planning for the power supply and demand of wind power. In this paper, we first analyzed the speed, power and the directions of the wind. The neural network and the time series models (ARMA, ARMAX, ARMA-GARCH, Holt Winters) for wind power generation forecasting were compared based on mean absolute error (MAE). For one to three hour-ahead forecast, ARMA-GARCH model was outperformed, and the neural network method showed a better performance in the six hour-ahead forecast.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.585-592
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• 2013

As the needs for alternative energy and renewable energy increase, there has been a lot of investment in developing wind energy, which does not cause air pollution nor the greenhouse gas effect. Wind energy is an environment friendly energy that is unlimited in its resources and is possible to be produced wherever the wind blows. However, since wind energy heavily relies on wind that has unreliable characteristics, it may be difficult to have efficient energy transmissions. For this reason, an important factor in wind energy forecasting is the estimation of available wind power. In this study, Gunsan wind farm data was used to compare ARMA model to neural network model to analyze for more accurate prediction of wind power generation. As a result, the neural network model was better than the ARMA model in the accuracy of the wind power predictions.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.989-995
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• 2017

As is known to all that the output of wind power generation has a character of randomness and volatility because of the influence of natural environment conditions. At present, the research of wind power prediction mainly focuses on point forecasting, which can hardly describe its uncertainty, leading to the fact that its application in practice is low. In this paper, a wind power range prediction model based on the multiple output property of BP neural network is built, and the optimization criterion considering the information of predicted intervals is proposed. Then, improved Particle Swarm Optimization (PSO) algorithm is used to optimize the model. The simulation results of a practical example show that the proposed wind power range prediction model can effectively forecast the output power interval, and provide power grid dispatcher with decision.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.715-724
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• 2023

Solar energy forecasting is essential for (1) power system planning, management, and operation, requiring accurate predictions. It is crucial for (2) ensuring a continuous and sustainable power supply to customers and (3) optimizing the operation and control of renewable energy systems and the electricity market. Recently, research has been focusing on developing solar energy forecasting models that can provide daily plans for power usage and production and be verified in the electricity market. In these prediction models, various data, including solar energy generation and climate data, are chosen to be utilized in the forecasting process. The most commonly used climate data (such as temperature, relative humidity, precipitation, solar radiation, and wind speed) significantly influence the fluctuations in solar energy generation based on weather conditions. Therefore, this paper proposes a hybrid forecasting model by combining the strengths of the Prophet model and the GRU model, which exhibits excellent predictive performance. The forecasting periods for solar energy generation are tested in short-term (2 days, 7 days) and medium-term (15 days, 30 days) scenarios. The experimental results demonstrate that the proposed approach outperforms the conventional Prophet model by more than twice in terms of Root Mean Square Error (RMSE) and surpasses the modified GRU model by more than 1.5 times, showcasing superior performance.