• Journal of Integrative Natural Science
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• v.12 no.1
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• pp.20-23
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• 2019

Demand response is usually operated through using the power rates and incentives. Demand management based on power charges is the most rational and efficient demand management method, and such methods include rolling base charges with peak time, sliding scaling charges depending on time, sliding scaling charges depending on seasons, and nighttime power charges. Search for other methods to stimulate resources on demand by actively deriving the demand reaction of loads to increase the energy efficiency of loads. In this paper, ESS algorithm for saving energy based on predicting the amount of solar power generation that can be used for buildings with small loads not under electrical grid.

• Membrane and Water Treatment
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• v.9 no.6
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• pp.455-462
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• 2018

The measurement and monitoring of the biochemical oxygen demand (BOD) play an important role in the planning and operation of wastewater treatment plants. The most basic method for determining biochemical oxygen demand is direct measurement. However, this method is both expensive and takes a long time. A five-day period is required to determine the biochemical oxygen demand. This study has been carried out in a wastewater treatment plant in Turkey (Hurma WWTP) in order to estimate the biochemical oxygen demand a shorter time and with a lower cost. Estimation was performed using artificial neural network (ANN) method. There are three different methods in the training of artificial neural networks, respectively, multi-layered (ML-ANN), teaching learning based algorithm (TLBO-ANN) and artificial bee colony algorithm (ABC-ANN). The input flow (Q), wastewater temperature (t), pH, chemical oxygen demand (COD), suspended sediment (SS), total phosphorus (tP), total nitrogen (tN), and electrical conductivity of wastewater (EC) are used as the input parameters to estimate the BOD. The root mean squared error (RMSE) and the mean absolute error (MAE) values were used in evaluating performance criteria for each model. As a result of the general evaluation, the ML-ANN method provided the best estimation results both training and test series with 0.8924 and 0.8442 determination coefficient, respectively.

• Journal of the Korea Concrete Institute
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• v.20 no.2
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• pp.193-202
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• 2008

Seismic design for bridge columns of the current Korea Highway Bridge Design Specifications which adopt full ductility design concept results in reinforcement congestion problems in construction site. It is due to large amount of confining steel is required even for small ductility demand which is a normal case in low and moderate seismicity regions like Korean peninsular. Therefore a new seismic design method based on limited ductility concept was proposed, which is called ductility demand based design method. It uses the new confining steel design equation considering ductility demand and aspect ratio of the column as well as material strength. The purpose of this study is to verify safety of the ductility demand based design method by the confining steel design equation. Eighty nine circular column test results are selected and investigated in terms of ductility factor and its safety. The safety factor for the circular column test results ranges between 1.11 and 3.98, and the average is 1.90. In this paper, the basic concept and detailed design procedure of the ductility demand based design method are also introduced as well as the investigation of the safety with respect to the major variables in confining steel design.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.447-449
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• 2022

This study was based on Android, one of the representative mobile platforms with the characteristics of connecting to the network anytime, anywhere and flexible mobility. In addition, using a demand prediction algorithm that can know the data of defective products based on AI, we will study the real-time monitoring UI design method based on Android studio with demand prediction data and company time series data.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.193-203
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• 2016

This paper forecasts electricity demand as a critical element of a demand management system in Smart Grid environment. We present a prediction method of using a combination of predictive values by time series clustering. Periodogram-based normalized clustering, predictive analysis clustering and dynamic time warping (DTW) clustering are proposed for time series clustering methods. Double Seasonal Holt-Winters (DSHW), Trigonometric, Box-Cox transform, ARMA errors, Trend and Seasonal components (TBATS), Fractional ARIMA (FARIMA) are used for demand forecasting based on clustering. Results show that the time series clustering method provides a better performances than the method using total amount of electricity demand in terms of the Mean Absolute Percentage Error (MAPE).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2354-2358
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• 2009

Demand response (DR) can be used to improve the efficiency of electricity markets and increase the reliability of power systems. As more utilities attempt to reduce the purchasing costs by implementing DR programs strategically, there is an increasing need for studies of how to allocate the reduced purchasing costs among DR program participants. The rebates or incentives can be given to DR program participants in proportion to the participants' contributions to the reduced purchasing costs. This paper presents Shapley Value-based method to determine the DR program participants' contributions to the reduced purchasing costs. A numerical example is presented to validate the effectiveness of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.2
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• pp.74-78
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• 2015

With the improvement of living standards and economic development, electricity consumption continues to grow. The electricity is a special energy which is hard to store, so its supply must be consistent with the demand. The objective of electricity demand forecasting is to make best use of electricity energy and provide balance between supply and demand. Hence, it is very important work to forecast electricity demand with higher precision. So, various forecasting methods have been developed. They can be divided into five broad categories such as time series models, regression based model, artificial intelligence techniques and fuzzy logic method without considering special-day effects. Electricity demand patterns on holidays can be often idiosyncratic and cause significant forecasting errors. Such effects are known as special-day effects and are recognized as an important issue in determining electricity demand data. In this research, we developed the power demand forecasting method using ELM(Extreme Learning Machine) for special day, particularly, lunar new year and Chuseok holiday.

• Earthquakes and Structures
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• v.12 no.3
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• pp.321-332
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• 2017

The reverse fault is a dangerous geological hazard faced by buried steel pipelines. Permanent ground deformation along the fault trace will induce large compressive strain leading to buckling failure of the pipe. A hybrid pipe-shell element based numerical model programed by INP code supported by ABAQUS solver was proposed in this study to explore the strain performance of buried X80 steel pipeline under reverse fault displacement. Accuracy of the numerical model was validated by previous full scale experimental results. Based on this model, parametric analysis was conducted to study the effects of four main kinds of parameters, e.g., pipe parameters, fault parameters, load parameter and soil property parameters, on the strain demand. Based on 2340 peak strain results of various combinations of design parameters, a semi-empirical model for strain demand prediction of X80 pipeline at reverse fault crossings was proposed. In general, reverse faults encountered by pipelines are involved in 3D oblique reverse faults, which can be considered as a combination of reverse fault and strike-slip fault. So a compressive strain demand estimation procedure for X80 pipeline crossing oblique-reverse faults was proposed by combining the presented semi-empirical model and the previous one for compression strike-slip fault (Liu 2016). Accuracy and efficiency of this proposed method was validated by fifteen design cases faced by the Second West to East Gas pipeline. The proposed method can be directly applied to the strain based design of X80 steel pipeline crossing oblique-reverse faults, with much higher efficiency than common numerical models.

• The Journal of Industrial Distribution & Business
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• v.7 no.3
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• pp.27-36
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• 2016

Purpose - Considering the importance of housing needs to real estate market, domestic studies on real estate prices from the perspective of demand are basically based on macro-data, but relatively few are associated with micro-data of urban real estate demand. We try to find a reliable relation of elasticity of demand and commercial housing market. Research design, data, and methodology - In this paper, we have derived housing demand theoretic method and have utilized micro-data of residential family housing survey of downtown area in Kunming City in October, 2015 to estimate income elasticity and price elasticity of housing demand respectively and make a comparative analysis. Results - The results indicate that income elasticity and price elasticity of families with owner-occupied housing are both larger than those of families with rental housing. Income elasticity of housing demand of urban residential families in Kunming is far below the foreign average and eastern coastal cities level, however, the corresponding price elasticity is far higher. Conclusions - We suggest that housing affordability of urban families in western China are constrained by the level of economic development, and the current housing price level has exceeded the economic affordability and psychological expectation of ordinary residents. Furthermore, noticing the great rigidity of housing demand, the expansion space of housing market for improvement and for commodity is limited.

• Korean Journal of Clinical Laboratory Science
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• v.50 no.4
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• pp.511-524
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• 2018

The purpose of this study is to provide policy recommendations for manpower planning by forecasting the supply and demand of Medical Technologists. Supply was estimated using an in-and-out movement method with a demographic method based on a baseline projection model. Demand was projected according to a demand-based method using the number of clinico-pathologic examinations taken for Medical Technologists. Over- or undersupply of Medical Technologists will depend on the productivity scenario and assumptions and ultimately on governmental policy direction. In other words, whether the production of Medical Technologists is higher or lower than the current level depends on the government policy to consider insurance finances. In this study, we assessed 'productivity scenario 3' based on the productivity as of 2012, when the government's policy direction was not considered. Based on the demand scenario using the ARIMA model, the supply of Medical Technologists is expected to be excessive. This oversupply accounts for less than 10% of the total and therefore should not be a big problem. However, given that the employment rate of Medical Technologists is 60%, it is necessary to consider policies to utilize the unemployed. These measures should expand the employment opportunities for the unemployed. To this end, it is necessary to strengthen the functions of laboratories in the public health center, to increase the quota of Medical Technologists, to assure their status, to establish a permanent inspection system for outpatient patients, and to expand the export of Medical Technologists overseas.