• Journal of Digital Convergence
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• v.18 no.12
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• pp.267-278
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• 2020

ESS is an essential requirement for resolving power shortages and power demand management and promoting renewable energy at a time when the energy paradigm changes. In this paper, we propose a cost-effective ESS Peak-Shaving operation plan through load and solar power generation forecast. For the ESS operation plan, electric load and solar power generation were predicted through RMS, which is a statistical measure, and a target load reduction guideline for one hour was set through the predicted electric load and solar power generation amount. The load and solar power generation amount from May 6th to 10th, 2019 was predicted by simulation of load and photovoltaic power generation using real data of the target customer for one year, and an hourly guideline was set. The average error rate for predicting load was 7.12%, and the average error rate for predicting solar power generation amount was 10.57%. Through the ESS operation plan, it was confirmed that the hourly guide-line suggested in this paper contributed to the peak-shaving maximization of customers.Through the results of this paper, it is expected that future energy problems can be reduced by minimizing environmental problems caused by fossil energy in connection with solar power and utilizing new and renewable energy to the maximum.

• The Journal of Society for e-Business Studies
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• v.24 no.4
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• pp.119-133
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• 2019

Recently, the importance prediction of photovoltaic power (PV) is considered as an essential function for scheduling adjustments, deciding on storage size, and overall planning for stable operation of PV facility systems. In particular, since most of PV power is generated in peak time, PV power prediction in a peak time is required for the PV system operators that enable to maximize revenue and sustainable electricity quantity. Moreover, Prediction of the PV power output in peak time without meteorological information such as solar radiation, cloudiness, the temperature is considered a challenging problem because it has limitations that the PV power was predicted by using predicted uncertain meteorological information in a wide range of areas in previous studies. Therefore, this paper proposes the LSTM (Long-Short Term Memory) based the PV power prediction model only using the meteorological, seasonal, and the before the obtained PV power before peak time. In this paper, the experiment results based on the proposed model using the real-world data shows the superior performance, which showed a positive impact on improving the PV power in a peak time forecast performance targeted in this study.

• Journal of Astronomy and Space Sciences
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• v.21 no.4
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• pp.429-440
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• 2004

Recently, real time data acquisition become important for space weather forecast and research. In this work, we have developed the data acquisition tools and their applications for space weather monitoring. We have developed programs to download the space weather data using IDL as well as programs to interactively display the image and data using ION (IDL on the Net). By using these tools, we have constructed the mirror site of Active Region Monitor (ARM) which summarizes several different solar activities, and developed ION programs to display TEC(Total Electron Contents) maps from GPS data at the passage of Korean satellites. At present, the KAO ARM mirror site (http://sun.kao.re.kr/arm) is successfully updated in every thirty minutes. The TEC maps from GPS data are expected to be used for monitoring the space environment of Korean satellites.

• Journal of Korea Water Resources Association
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• v.44 no.9
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• pp.721-730
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• 2011

The objective of this study is to assess the availability of streamflow simulation using Radar-AWS Rain rate (RAR) data which is produced by KMA on real-time. Chuncheon dam upstream basin is selected as study area and total area is 4859.73 $km^2$. Mean Areal Precipitation (MAP) using AWS and RAR are calculated on 5 subbasin. The correlationship of hourly MAPs between AWS and RAR is weak on ungauged subbasins but that is relatively high on gauged ones. We evaluated the simulated discharge using the MAPs derived from two data types during flood season from 2006 to 2009. The simulated discharges using AWS on Chuncheon dam (gauged basin) are well fitted with measured ones. In some cases, however, discharges using AWS on Hwacheon dam and Pyeonghwa dam with some ungauged subbasins are overestimated on the other hand, ones using RAR in the same case are well fitted with measured ones. The hourly RAR data is useful for the real-time river forecast on the ungauged basin in view of the results.

• The Journal of Bigdata
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• v.3 no.2
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• pp.59-70
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• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.4
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• pp.307-326
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• 2021

Real-time sea level observations from tide gauges include missing and erroneous values. Classification as abnormal values can be done for the latter by the quality control procedure. Although the 3𝜎 (three standard deviations) rule has been applied in general to eliminate them, it is difficult to apply it to the sea-level data where extreme values can exist due to weather events, etc., or where erroneous values can exist even within the 3𝜎 range. An artificial intelligence model set designed in this study consists of non-annotated recurrent neural networks and ensemble techniques that do not require pre-labeling of the abnormal values. The developed model can identify an erroneous value less than 20 minutes of tide gauge recording an abnormal sea level. The validated model well separates normal and abnormal values during normal times and weather events. It was also confirmed that abnormal values can be detected even in the period of years when the sea level data have not been used for training. The artificial neural network algorithm utilized in this study is not limited to the coastal sea level, and hence it can be extended to the detection model of erroneous values in various oceanic and atmospheric data.

• Journal of Astronomy and Space Sciences
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• v.22 no.4
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• pp.431-440
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• 2005

Local K-index is an indicator representing local geomagnetic activity in every 3 hour. For estimation of the local K-index, a reasonable determination of solar quiet curve (undisturbed daily variation of geomagnetic field) is quiet essential. To derive the solar quiet curve, the FMI method, which is one of representative algorithms, uses horizontal components (H and D) of 3 days magnetometer data from the previous day to the next day for a specific day. However, this method is not applicable to real time forecast since it always requires the next day data. In this study, we have devised a new method to estimate local K-index in near real-time by modifying the FMI method. The new method selects a recent quiet day whose $K_p$ indices, reported by NOAA/SEC are all lower than 3, and replace the previous day and the next day data by the recent quiet day data. We estimated 2,672 local K indices from Gyeongzu magnetometer in 2003, and then compared the indices with those from the conventional FMI method. We also compared the K indices with those from Kakioka observatory. As a result, we found that (1) K indices from the new method are nearly consistent with those of the conventional FMI method with a very high correlation (R=0.96); (2) onr local K indices also have a relatively high correlation (R=0.81) with those from Kakioka station. Our results show that the new method can be used for near real-time estimation of local K indices from Gyeongzu magnetometer.

• Journal of the Korean earth science society
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• v.32 no.4
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• pp.360-372
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• 2011

In order to clarify the advection and dispersion characteristics of volcanic tephra to be emitted from the Mt. Baekdu, several numerical experiments were carried out using three-dimensional atmospheric dynamic model, Weather and Research Forecast (WRF) and Laglangian particles dispersion model FLEXPART. Four different temporally averaged meteorological values including wind speed and direction were used, and their averaged intervals of meteorological values are 1 month, 10 days, and 3days, respectively. Real time simulation without temporal averaging is also established in this study. As averaging time of meteorological elements is longer, wind along the principle direction is stronger. On the other hands, the tangential direction wind tends to be clearer when the time become shorten. Similar tendency was shown in the distribution of volcanic tephra because the dispersion of particles floating in the atmosphere is strongly associated with wind pattern. Wind transporting the volcanic tephra is divided clearly into upper and lower region and almost ash arriving the Korean Peninsula is released under 2 km high above the ground. Since setting up the temporal averaging of meteorological values is one of the critical factors to determine the density of tephra in the air and their surface deposition, reasonable time for averaging meteorological values should be established before the numerical dispersion assessment of volcanic tephra.

• 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.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.