• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.139-153
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• 2018

This paper investigates solar power forecasting based on several time series models. First, we consider weather variables that influence forecasting procedures as well as compare forecasting accuracies between time series models such as ARIMAX, Holt-Winters and Artificial Neural Network (ANN) models. The results show that ten models forecasting 24hour data have better performance than single models for 24 hours.

• Journal of Korean Society of Water and Wastewater
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• v.28 no.4
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• pp.377-389
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• 2014

In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and this has led to various studies regarding energy saving and improvement of water supply reliability. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The concepts was demonstrated through application to observed from water plant (A) in the South Korea. Various statistics (e.g. the efficiency coefficient, the correlation coefficient, the root mean square error, and a maximum error rate) were evaluated to investigate model efficiency. The ensemble based model with an cross-validate prediction procedure showed better predictability for water demand forecasting at different temporal resolutions. In particular, the performance of the ensemble model on hourly water demand data showed promising results against other individual prediction schemes.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.127-131
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• 1999

Typhoons were known by contributing to transporting plus heat or kinetic energy from equatorial region to midlatitude region. Due to the strong damage from typhoon, we acknowledged the theoretical study and the importance of accurate forecast about typhoon. In this study, typhoon forecasting system was developed to search the tracks of past typhoons or to display similar track of past typhoon in comparison with the path of current forecasting typhoon. It was programmed using Interactive Data Language(IDL), which was a complete computing environment for the interactive analysis and visualization of data. Typhoon forecasting system was also included satellite image and auxiliary chart. IR, Water Vapor, Visible satellite images helped users analyze an accurate forecast of typhoon. They were further refined the procedures for generating water vapor winds and gave an initial indication of their utility for numerical weather prediction(NWP), in particular for typhoon track forecasting where they could provide important information. They were also available for its utility in typhoon tracer or intensity.

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.162-164
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• 2005

This paper presents an hour-ahead System Marginal Price (SMP) forecasting framework based on a neural network. Recently, the deregulation in power industries has impacted on the power system operational problems. The bidding strategy of market participants in energy market is highly dependent on the short-term price levels. Therefore, short-term SMP forecasting is a very important issue to market participants to maximize their profits. and to market operator who may wish to operate the electricity market in a stable sense. The proposed hybrid neural network is composed of tow parts. First part of this scheme is pattern classification to input data using Kohonen Self-Organizing Map (SOM) and the second part is SMP forecasting using back-propagation neural network that has three layers. This paper compares the forecasting results using classified input data and unclassified input data. The proposed technique is trained, validated and tested with historical date of Korea Power Exchange (KPX) in 2002.

• 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 Korean Society for Atmospheric Environment
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• v.31 no.1
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• pp.1-14
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• 2015

This study investigates the impact of enhanced regional meteorological fields on improvement of wind energy forecasting accuracy in the southwestern coast of the Korean Peninsula. To clarify the effect of detailed surface boundary data and application of analysis nudging technique on simulated meteorological fields, several WRF simulations were carried out. Case_LT, which is a simulation with high resolution terrain height and land use data, shows the most remarkable accuracy improvement along the shoreline mainly due to modified surface characteristics such as albedo, roughness length and thermal inertia. Case_RS with high resolution SST data shows accurate SST distributions compared to observation data, and they led to change in land and sea breeze circulation. Case_GN, grid nudging applied simulation, also shows changed temperature and wind fields. Especially, the application of grid nudging dominantly influences on the change of horizontal wind components in comparison with vertical wind component.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.396-397
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• 2011

As moving away from fossil fuel makes rapid progress, new paradigm has arisen in the power industry area. Developing alternative energy source is progressing actively, the proportion of renewable energy in electricity production is expected to be increased. Because the output of wind farm depends on wind characteristic, minimizing the output fluctuation is a key to keep the power system controllable and stable. Various compensation scheme for stabilizing the output of wind farm has been developed. Considering some requirements such as reaction velocity, controllability, scalability and applicability, energy storage system is one of the effective methods for spreading of renewable energy. In this paper, method of compensating method with forecasting algorithm was simulated, and then the results was analyzed.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.177-182
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• 2022

Technology is progressing with every passing day and the enormous usage of electricity is becoming a necessity. One of the techniques to enjoy the assistances in a smart home is the efficiency to manage the electric energy. When electric energy is managed in an appropriate way, it drastically saves sufficient power even to be spent during hard time as when hit by natural calamities. To accomplish this, prediction of energy consumption plays a very important role. This proposed prediction model Coherent Weighted K-Means Clustering ARIMA (CWKMCA) enhances the weighted k-means clustering technique by adding weights to the cluster points. Forecasting is done using the ARIMA model based on the centroid of the clusters produced. The dataset for this proposed work is taken from the Pecan Project in Texas, USA. The level of accuracy of this model is compared with the traditional ARIMA model and the Weighted K-Means Clustering ARIMA Model. When predicting,errors such as RMSE, MAPE, AIC and AICC are analysed, the results of this suggested work reveal lower values than the ARIMA and Weighted K-Means Clustering ARIMA models. This model also has a greater loglikelihood, demonstrating that this model outperforms the ARIMA model for time series forecasting.

• Journal of Korea Water Resources Association
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• v.48 no.5
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• pp.409-423
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• 2015

In this study the very short-term rainfall forecasting and storm water forecasting using the weather radar data were implemented in an urban stream basin. As forecasting time increasing, the very short-term rainfall forecasting results show that the correlation coefficient was decreased and the root mean square error was increased and then the forecasting model accuracy was decreased. However, as a result of the correlation coefficient up to 60-minute forecasting time is maintained 0.5 or higher was obtained. As a result of storm water forecasting in an urban area, the reduction in peak flow and outflow volume with increasing forecasting time occurs, the peak time was analyzed that relatively matched. In the application of storm water forecasting by radar rainfall forecast, the errors has occurred that we determined some of the external factors. In the future, we believed to be necessary to perform that the continuous algorithm improvement such as simulation of rapid generation and disappearance phenomenon by precipitation echo, the improvement of extreme rainfall forecasting in urban areas, and the rainfall-runoff model parameter optimizations. The results of this study, not only urban stream basin, but also we obtained the observed data, and expand the real-time flood alarm system over the ungaged basins. In addition, it is possible to take advantage of development of as multi-sensor based very short-term rainfall forecasting technology.

• Environmental and Resource Economics Review
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• v.28 no.3
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• pp.437-465
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• 2019

In this study, we propose a new method to forecast long-term energy demand in Korea. Based on Chang et al. (2016), which models the time varying long-run relationship between electricity demand and GDP with a function coefficient panel model, we design several schemes to retain objectivity of the forecasting model. First, we select the bandwidth parameters for the income coefficient based on the out-of-sample forecasting performance. Second, we extend the income coefficient using the functional principal component analysis method. Third, we proposed a method to reflect the elasticity change patterns inherent in Korea. In the empirical analysis part, we forecasts the long-term energy demand in Korea using the proposed method to show that the proposed method generates more stable long term forecasts than the existing methods.