• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.419-430
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• 2020

This paper investigated methods to improve the forecasting accuracy of the electricity consumption prediction model. Currently, the demand for electricity has continuously been rising more than ever. Since the industrial sector uses more electricity than any other sectors, the importance of a more precise forecasting model for manufacturing sites has been highlighted to lower the excess energy production. We propose a double encoder-decoder model, which uses two separate encoders and one decoder, in order to adapt both long-term and short-term data for better forecasts. We evaluated our proposed model on our electricity power consumption dataset, which was collected in a manufacturing site of Sehong from January 1st, 2019 to June 30th, 2019 with 1 minute time interval. From the experiment, the double encoder-decoder model marked about 10% reduction in mean absolute error percentage compared to a conventional encoder-decoder model. This result indicates that the proposed model forecasts electricity consumption more accurately on manufacturing sites compared to an encoder-decoder model.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1468-1471
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• 1999

This paper presents a method of the regional long-term distribution planning considering economic indicator with the assumption that energy demands proportionally increases with the economic indicators. For the practical distribution planning, it is necessary to regional load forecasting, distribution substation planning, distribution feeder planning. Accordingly, in this paper, after performing regional load forecasting considering economic indicator, it is performed distribution substation planning and distribution feeder planning in order by using this result. For accurate distribution planning, it is very important to scrutinize the correlation among the regional electric power demands, economic indicator and other characteristics because distribution planning results may vary depending on many different factors such as electric power demands, gross products, social trend and so on. In this paper, various steps microscopically and macro scopically are used for the regional long-term distribution planning in order to increase the accuracy and practical use of the results

• Journal of Korea Multimedia Society
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• v.18 no.7
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• pp.886-894
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• 2015

In this paper, we conduct sensitivity analysis of parameters used for inverse modeling in order to estimate the PM₁₀ emissions from the 16 areas in East Asia accurately. Parameters used in sensitivity analysis are R, the observational error covariance matrix, and B, a priori (background) error covariance matrix. In previous studies, it was used with the predetermined parameter empirically. Such a method, however, has difficulties in estimating an accurate emissions. Therefore, an automatically determining method for the most suitable value of R and B with an error measurement criteria and posteriori emissions accuracy is required. We determined the parameters through a sensitivity analysis, and improved the accuracy of posteriori emissions estimation. Inverse modeling methods used in the emissions estimation are pseudo inverse, NNLS (Nonnegative Least Square), and BA(Bayesian Approach). Pseudo inverse has a small error, but has negative values of emissions. In order to resolve the problem, NNLS is used. It has a unrealistic emissions, too. The problems are resolved with BA(Bayesian Approach). We showed the effectiveness and the accuracy of three methods through case studies.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.591-598
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• 2018

Short term load forecasts complexly affected by socioeconomic factors and weather variables have non-linear characteristics. Thus far, researchers have improved load forecast technologies through diverse techniques such as artificial neural networks, fuzzy theories, and statistical methods in order to enhance the accuracy of load forecasts. Short term load forecast errors for special days are relatively much higher than that of weekdays. The errors are mainly caused by the irregularity of social activities and insufficient similar past data required for constructing load forecast models. In this study, the load characteristics of Lunar New Year's Day holidays well known for the highest error occurrence holiday period are analyzed to propose a load forecast technique for Lunar New Year's Day holidays. To solve the insufficient input data problem, the similarity of the load patterns of past Lunar New Year's Day holidays having similar patterns was judged by Euclid distance. Lunar New Year's Day holidays periods for 2011-2012 were forecasted by the proposed method which shows that the proposed algorithm yields better results than the comprehensive analysis method or the knowledge-based method.

• Journal of the Korean Society for Marine Environment & Energy
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• v.1 no.2
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• pp.52-59
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• 1998

Many concerns are placed on preservation of coastal environment from the spilled oil contaminant in the coastal area. And the use of computer simulation model to combat with oil spill has come to play mote important role in forecasting the oil spill trajectory so as to protect coastal area and minimize the damage from oil contaminants. The main concerns of this study is how the movements of spilled oil are affected by currents including tidal, oceanic, and wind-driven currents. Especially, in the present paper, the oil spill trajectory can be predicted by a real-time system that allows prediction of circulation and wind field. The harmonic methods are adopted to simulate the tidal currents as well as it can be possible to achieve the wind-field data and oceanic current data from the established database. System performance is illustrated by the simulation of oil spill in the south-eastern coastal area of Korea. Simulation results are compared with the observed one.

• Journal of Advanced Navigation Technology
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• v.22 no.3
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• pp.233-239
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• 2018

Since solar power generation is intermittent depending on weather conditions, it is necessary to predict the accurate generation amount of solar power to improve the efficiency and economical efficiency of solar power generation. This study proposes a short - term deep learning prediction model of solar power generation using meteorological data from Mokpo meteorological agency and generation data of Yeongam solar power plant. The meteorological agency forecasts weather factors such as temperature, precipitation, wind direction, wind speed, humidity, and cloudiness for three days. However, sunshine and solar radiation, the most important meteorological factors for forecasting solar power generation, are not predicted. The proposed model predicts solar radiation and solar radiation using forecast meteorological factors. The power generation was also forecasted by adding the forecasted solar and solar factors to the meteorological factors. The forecasted power generation of the proposed model is that the average RMSE and MAE of DNN are 0.177 and 0.095, and RNN is 0.116 and 0.067. Also, LSTM is the best result of 0.100 and 0.054. It is expected that this study will lead to better prediction results by combining various input.

• Korean Chemical Engineering Research
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• v.50 no.3
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• pp.479-491
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• 2012

In this paper we performed evaluation of the economics of a district heating system (DHS) consisting of energy suppliers and consumers, heat generation and storage facilities and power transmission lines in the capital region, as well as identification of optimal operating conditions. The optimization problem is formulated as a mixed integer linear programming (MILP) problem where the objective is to minimize the overall operating cost of DHS while satisfying heat demand during 1 week and operating limits on DHS facilities. This paper also propose a new forecasting model of the system marginal price (SMP) using past data on power supply and demand as well as past cost data. In the optimization, both the forecasted SMP and actual SMP are used and the results are analyzed. The salient feature of the proposed approach is that it exhibits excellent predicting performance to give improved energy efficiency in the integrated DHS.

• Journal of the Korean Solar Energy Society
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• v.39 no.6
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• pp.113-125
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• 2019

With the opening of the small power brokerage business market in December 2018, the small power trading market has started in Korea. Operators must submit the day-ahead estimates of power output and receive incentives based on its accuracy. Therefore, the accuracy of power generation forecasts is directly affects profits of the operators. The forecasting process for power generation can be divided into two procedure. The first is to forecast solar irradiation and the second is to transform forecasted solar irradiation into power generation. There are two methods for transformation. One is to simulate with physical model, and another is to use regression model. In this study, we found the best-fit regression model by analyzing hourly data of PV output and solar irradiation data during three years for 242 PV plants in Korea. The best model was not a linear model, but a sigmoidal model and specifically a Gompertz model. The combined linear regression and Gompertz curve was proposed because a the curve has non-zero y-intercept. As the result, R² and RMSE between observed data and the curve was significantly reduced.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.13 no.1
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• pp.7-13
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• 2017

In order to use and manage the building energy efficiently, it is necessary to minimize building energy consumptions, and establish operation plans of various equipment. The maximum heating and cooling load calculation is an essential way in various equipment selections, and the annual building load calculation is used in forecasting & evaluating the LCC required for operation plan. In this study, noting that the annual building load changes depending on outside temperature around year, we propose a predicting method of annual building load. By using the $4^{th}$ polynomial function that have two double radix and a feature the $f(x)=a^4$ in x = 0 condition, we can calculate annual building load very easily only with the two result (maximum heating and cooling load) and a minimum parameters.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.146-146
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• 2018

Basin evapotranspiration is the result of water balance and energy balance, which is affected by climate and underlying surface characteristics, the process is complex, and spatial and temporal variability is large, the evapotranspiration estimation of river basin is an important but difficult problem in the field of hydrology, over the years, many scholars devoted to the basin actual evapotranspiration estimation and achieved excellent results. We discuss Budyko coupled water-energy balance theory and evaporation paradox, then use the Fu's equation to estimate actual evapotranspiration yearly in different areas with different dryness. The result shows that Fu's equation has high precision for estimating evapotranspiration yearly in our selected study area, and the estimation result has higher precision in the area with high dryness. Then, we propose an improved formula which can be used to estimate actual evapotranspiration monthly. Furthermore, we found that the parameter in the formula reflects general conditions of underlying surface and it is affected by several factors, at last, we tried to propose the calculation formula. The study indicates that Fu's equation provides a reliable method for evapotranspiration estimation in dry regions as well as semi-humid and semi-arid regions, which has great significance for forecasting river basin water resources and inquiring into ecological water requirement.