• Journal of Information Processing Systems
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• v.19 no.2
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.1
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• pp.42-50
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• 2008

Unlike a cellular network, a mobile ad hoc network(MANET) is constructed only by mobile nodes without access point. Mobile nodes in MANET operate with scarce resources and restricted battery. If battery of intermediate node is exhausted, overall network might be diverged. Therefore, power-aware is really important. An on-demand multipath routing protocol which is proposed to compensate for shortcoming of on-demand single path routing protocol can reduce mute discovery overhead because route discovery starts only when all routes are disconnected. AOMDV(Ad hoc On-demand Multipath Distance Vector) which is on-demand multipath routing protocol based on AODV, reduces 40% of route discovery frequency. However, AOMDV have none of power-aware. So AOMDV have problem that route discovery for power exhaustion is not reduced at all. This paper proposes new power-aware path selection algorithm for AOMDV and scheme that broadcast REER packets when mobile node's battery can be gone. Performance comparison of proposed algorithm with AOMDV using ns-2 simulator shows that route discovery of proposed algorithm is reduced maximally 36.57% than AOMDV's.

• The Journal of Bigdata
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• v.4 no.1
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• pp.63-72
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• 2019

For power energy, optimal generation and distribution plans based on accurate demand forecasts are necessary because it is not recoverable after they have been delivered to users through power generation and transmission processes. Failure to predict power demand can cause various social and economic problems, such as a massive power outage in September 2011. In previous studies on forecasting power demand, ARIMA, neural network models, and other methods were developed. However, limitations such as the use of the national average ambient air temperature and the application of uniform criteria to distinguish seasonality are causing distortion of data or performance degradation of the predictive model. In order to improve the performance of the power demand prediction model, we divided Korea into five major regions, and the power demand prediction model of the linear regression model and the neural network model were developed, reflecting seasonal characteristics through regional characteristics and migration period learning techniques. With the proposed approach, it seems possible to forecast the future demand in short term as well as in long term. Also, it is possible to consider various events and exceptional cases during a certain period.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.11a
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• pp.345-347
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• 2005

The electric demands increase, financial need for new power plant constructions and environmental problem have led to search for more efficient energy production and load management. To minimize the construction of power plants and reduce total power consumption include installation of demand controller to industrial applications. Accordingly to maximize the load control by the diffusion of demand controller, govermental economic supports as well as the analysis of energy saving effects. This paper presents the cost-effectiveness analysis for DSM program evaluation and case study to analyze demand controller DSM program.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.619-622
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• 2001

In this paper, an intelligent demand control system was introduced. This system is composed of demand controller, RTU, Power Line Modem and HMI program. The developed demand controller was capable of synchronizing with watthour meter recommended by KEPCO. To control distant loads, network function using powerline communication is implemented in RTU, with HMI program, the untrained users are able to operate system easily. Additionally using the Powerline, the cost and time of installation is saved. The system performance was proved In a several experiments.

• Proceedings of the KIEE Conference
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• summer
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• pp.663-665
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• 2004

There are some demand response program which is Direct Load Control and so on in Korea. These are used to manage lack of power stability or shift peak time for shading load. It is very important not only using stability power system but controling and scheduling power system on the whole. Interruptible loads are essential resources to solve lack of energy and limit of constructing generator On recently days, Demand Response Program's reliability is recognized as ancillary or reserve service in many country. This paper presents a necessity to apply demand resource to our ancillary program. For this reason, it is introduce overseas ancillary program using load resource.

• Journal of the Korean Solar Energy Society
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• v.32 no.3
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• pp.19-25
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• 2012

About supply and demand to see that you need to match, the limitations of wind power capacity is low demand and the commitment of the general generator will exist between the minimum generation. if the turbine's output can be controlled, The limitation of wind power capacity will be adopted based on instant power generation. Namely, The minimum limits of wind power generation based load operation by calculating the amount that is higher than if the output should be restricted to highest operation. in this paper, we committed to the demand for low enough that the combination of the general generator of wind power capacity to accommodate the operation of determining whether the limit is intended to. For this, power system analysis program PSS/E was used, Jeju system by implementing the model simulations were performed.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.235-236
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• 2008

Recently, the power load is growing larger and because of the environmental limitation of generation, the expansion of generation facilities are becoming more difficult. For that reason the importance of the demand-side resources come to be higher. One method of the demand-side resource, the DLC Program, has executed, and moreover, the loads which are available to be controlled are increasing. It should be considered of some kinds of power system components such as DLCs, because the fact that using the demand resources will be an important part of the power system. This paper considers the power factor of the load-bus which is shedded in the direct load control program. and then analyze the power system using flow sensitivity and voltage sensitivity. In this paper, we assumed two scenarios through the rank of the load power factor at each bus and to compare and evaluate each case, we used Power World for the simulation.

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.76.1-76.1
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• 2011

With the growing penetration of distributed generation including from renewable sources, smart grid power system is needed to address the reliability problem. One important feature of smart grid is demand response. In order to design a demand response program, it is indispensable to understand how consumer reacts upon the change of electricity price. In this paper, we construct an econometrics model to estimate the hourly price elasticity of demand. This panel model utilizes the hourly load data obtained from KEPCO for the period from year 2005 to 2009. The hourly price elasticity of demand is found to be statistically significant for all the sample under investigation. The samples used for this analysis is from the past historical data under the price structure of three different time zones for each season. The result of the analysis of this time of use pricing structure would allow the policy maker design an appropriate incentive program. This study is important in the sense that it provides a basic research information for designing future demand response programs.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.11
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• pp.39-49
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• 2011

Smart Grid has two main objectives on both supply and demand aspects which are to distribute the renewable energy sources on supply side and to develop realtime price responses on demand side. Renewable energy does not consume fossil fuels, therefore it improves the eco-friendliness and saves the cost of power system operation at the same time. Demand response increases the flexibility of the power system by mitigating the fluctuation from renewable energies, and reduces the capacity investment cost by shedding the peak load to off-peak periods. Currently Smart Grid technologies mainly focus on energy monitoring and display services but it has been proved that enabling technologies can induce the higher demand responses through many pilot projects in USA. On this context, this paper provides a price responsive algorithm for HEMS (home energy management system) on the real time pricing environment. This paper identifies the demand response as a core function of HEMS and classifies the demand into 3 categories of fixed, transferable, and realtime responsive loads which are coordinated and operated for the utility maximization or cost minimization with the optimal usage combination of three kinds of demand.