• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.266-268
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• 2003

This is based on the AGENT-LUC model framework. Luangprabang Province has the largest percentage of shifting cultivation area in Laos PDR. The model simulates the spatial and temporal patterns of the shifting cultivation in the study area, using a GIS database while the total area of shifting cultivation is controlled by supply and demand balance of food. The model simulation period is from 1990 to 1999, at a spatial resolution of 500m. The results are evaluated using statistical data and remote sensing images. Through the validation, it is concluded that the trends simulated agrees to that of statistical data and the spatial and temporal patterns are also replicated satisfactorily.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1069-1078
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• 2018

Demand response (DR) programs give opportunity to consumers to manage their electricity bills. Besides, distribution system operator (DSO) is interested in using DR programs to obtain technical and economic benefits for distribution network. Since small consumers have difficulties to individually take part in the electricity market, an entity named demand response provider (DRP) has been recently defined to aggregate the DR of small consumers. However, implementing DR programs face challenges to fairly allocate benefits and payments between DRP and DSO. This paper presents a procedure for modeling the interaction between DRP and DSO based on a bilevel programming model. Both DSO and DRP behave from their own viewpoint with different objective functions. On the one hand, DRP bids the potential of DR programs, which are load shifting and load curtailment, to maximize its expected profit and on the other hand, DSO purchases electric power from either the electricity market or DRP to supply its consumers by minimizing its overall cost. In the proposed bilevel programming approach, the upper level problem represents the DRP decisions, while the lower level problem represents the DSO behavior. The obtained bilevel programming problem (BPP) is converted into a single level optimizing problem using its Karush-Kuhn-Tucker (KKT) optimality conditions. Furthermore, point estimate method (PEM) is employed to model the uncertainties of the power demands and the electricity market prices. The efficiency of the presented model is verified through the case studies and analysis of the obtained results.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.11-19
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• 2018

With the development of smart grid technology, it has become a hotspot to increase benefits of both residential users and electric power companies through demand response technology and interactive technology. In this paper, the game theory is introduced to the interaction between residential users and an electric company, making a mutually beneficial situation for the two. This paper solves the problem of electricity pricing and load shifting in the interactive behavior by building the game-theoretic process, proposing the interaction strategy and doing the optimization. In the simulation results, the residential users decrease their cost by 11% mainly through shifting the thermal loads and the electric company improves its benefits by 5.6% though electricity pricing. Simulation analysis verifies the validity of the proposed method and shows great revenue for the economy of both sides.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.905-907
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• 1993

Demand for electricity is increasing annually. Especially, the daytime demand grawth shows higher than any other time period. So the big difference between maximum and minimum electrical demand becomes another important problem to be solved. The Battery Energy Storage System is chosen as one of the solutions among the sevral methods. The purpose of utilization of Battery Energy Storage System is to improve the daily load factor. Also, Battery Energy Storage System may be used for the load levelling or the load shifting as well as the spinning reserve. Up to now, only the pumped hydro power plant system has been operated on the commercial basis, but this system has so many constraints such as site, environmental effects, construction period, ect. Being considered current electrical power situation the development of electric storage system is in need latly. Among the various electric storage systems, Battery Energy System is chosen with the top priority because it has sevral merits to cover such as the short construction period, the demand site installation, and the food environmental characteristics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.85-92
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• 2018

Reductions of the electricity charge are achieved by demand management of the load. The demand management method of the load using ESS involves peak shifting, which shifts from a high demand time to low demand time. By shifting the load, the peak load can be lowered and the energy charge can be saved. Electricity charges consist of the energy charge and the basic charge per contracted capacity. The energy charge and peak load are minimized by Linear Programming (LP) and Quadratic Programming (QP), respectively. On the other hand, each optimization method has its advantages and disadvantages. First, the LP cannot separate the efficiency of the ESS. To solve these problems, the charge and discharge efficiency of the ESS was separated by Mixed Integer Linear Programming (MILP). Nevertheless, both methods have the disadvantages that they must assume the reduction ratio of peak load. Therefore, QP was used to solve this problem. The next step was to optimize the formula combination of QP and LP to minimize the electricity charge. On the other hand, these two methods have disadvantages in that the charge and discharge efficiency of the ESS cannot be separated. This paper proposes an optimization method according to the situation by analyzing quantitatively the advantages and disadvantages of each optimization method.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1843-1850
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• 2014

In this paper, we investigate a group building based power consumption scheduling to minimize the electricity cost. We consider the demand shift to reduce the peak load and suggest the compensation function reflecting the relationship between the change of the building demand and the occupants' comfort. Using that, the electricity cost minimization problem satisfied the convexity is formulated, and the optimal power consumption scheduling algorithm is proposed based on the iterative method. Extensive simulations show that the proposed algorithm achieves the group management gain compared to the individual building operation by increasing the degree of freedom for the operation.

• 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.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.2
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• pp.15-26
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• 2012

For stable and sustainable crop production, understanding the effects of climate changes on agricultural water resources is necessary to minimize the negative effects which might occur due to shifting weather conditions. Although various studies have been carried out in Korea concerning changes in evapotranspiration and irrigation water requirement, the findings are still difficult to utilize fordesigning the demand and unit duty of water, which are the design criteria of irrigation systems. In this study, the impact analysis of climate changes on the paddy water demand and unit duty of water was analyzed based on the high resolution climate change scenarios (specifically under the A1B scenario) provided by the Korea Meteorological Administration. The result of the study indicated that average changes in the paddy water demand in eight irrigation districts were estimated as -2.4 % (2025s), -0.2 % (2055s), and 3.2 % (2085s). The unit duty of water was estimated to increase on an average within 2 % during paddy transplanting season and within 5 % during growing season after transplanting. This result could be utilized for irrigation system design, agricultural water resource development, and rice paddy cultivation policy-making in South Korea.

• Journal of the Korea Society of Computer and Information
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• v.22 no.10
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• pp.63-71
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• 2017

In this paper, we develop the system for audio volume equalization of video on demand(VoD) content and propose the solution for it. In recent years, there has been a steady increase in the number of VoD users in addition to linear channels. However, viewers ought to sit in an uncomfortable way, adjusting the volume intermittently while they are broadcasted. Sudden changes of volume occur between the broadcasting channels, the programs from the co-channel, or the linear channels and the VoDs. Especially, upsurged dissatisfaction from the televiewers has been found due to the unequalized volume when shifting between the linear channel and the VoD. In order to solve this problem, multilateral efforts were put forth, such as a system for keeping the volume at a certain level in digital broadcasting program has been legislated domestically. It leads success in equalizing linear channel volume. On contrary, too little notice has been taken for distorted volume problem of video on demand(VoD) content. In this paper, we developed and applied the volume equalization system into VoD content to achieve uniformization, a similar condition with linear channel(-24LKFS). This suggestion helped uneven current of volume which was in the stage -16 ~ -20LKFS to stable condition by lowering into the stage of -24LKFS. It also brought 20% increase in perspective of volume quality satisfaction level.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.