• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.292-294
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• 1998

It is very important for electric utility to expand generating facilities and transmission equipments in accordance with the increase of electricity demand. Regional electricity demand forecasting is among the most important step for long-term investment and power supply planning. The main objectives of this paper are to develop the methodologies for forecasting regional load demand. The Model consists of four models, regional economy, regional electricity energy demand, areal electricity energy demand. and areal peak load demand. This paper mainly suggests regional electricity energy demand model and areal peak load demand. A case study is also presented.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.376-378
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• 2000

The current worldwide electricity market introduced competition, which is breaking up the monopoly structure and also enforcing phased structural reform in South Korea. The change of the electricity charge from cost base to price base due to the introduction of the electricity market competition causes consumer to choose a variety of charge schemes and a portion of loads to be affected by this change. Therefore, in order to find a mathematical model of the sensitively-responding-to-price loads and reflect this to the DSM demand management, the price-sensitive load model is needed. Thus, this paper first proposes the composite price-sensitive load model that is expressed as a function of price, presents the methodology to estimate price-sensitive load model at each bus by bus load compositions.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.38-50
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• 2018

Spatio-temporal load forecasting (STLF) is a foundation for building the prediction-based power map, which could be a useful tool for the visualization and tendency assessment of urban energy application. Constructing one point-forecasting model for each electricity cell in the geographic space is possible; however, it is unadvisable and insufficient, considering the aggregation features of electricity cells and uncertainties in input variables. This paper presents a new STLF method, with a data-driven framework consisting of 3 subroutines: multi-level clustering of cells considering their aggregation features, load regression for each category of cells based on SLS-SVRNs (sparse least squares support vector regression networks), and interval forecasting of spatio-temporal load with sampled blind number. Take some area in Pudong, Shanghai as the region of study. Results of multi-level clustering show that electricity cells in the same category are clustered in geographic space to some extent, which reveals the spatial aggregation feature of cells. For cellular load regression, a comparison has been made with 3 other forecasting methods, indicating the higher accuracy of the proposed method in point-forecasting of spatio-temporal load. Furthermore, results of interval load forecasting demonstrate that the proposed prediction-interval construction method can effectively convey the uncertainties in input variables.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.3
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• pp.187-197
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• 2004

This paper presents a window-based load management system (LMS) developed as a decision-making tool in the competitive electricity market The developed LMS can help the users to monitor system load patterns, analyze their past energy consumption and schedule for the future energy consumption. The LMS can also provide the effective information on real-time energy/cost monitoring, consumed energy/cost analysis, demand schedule and cost-savings. Therefore. this LMS can be used to plan the optimal demand schedule and consumption strategy.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.591-593
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• 2007

This paper is to cluster the AMR (Automatic Meter Reading) data. The load survey system has been applied to record the power consumption of sampling the contract assortment in KEPRI AMR. The effect of the contract assortment change to the customer power consumption is determined by executing the clustering on the load survey results. We can supply the power to customer according to usage to the analysis cluster. The Korea a class of the electricity supply type is less than other country. Because of the Korea electricity markets exists one electricity provider. Need to further divide of electricity supply type for more efficient supply. We are found pattern that is different from supplied type to customer. Out experiment use the Clementine which data mining tools.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.386-388
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• 2001

The change of the electricity charge from cost base to price base due to the introduction of the electricity market competition causes consumer to choose a variety of charge schemes and a portion of loads to be affected by this change. Besides, it is required the index that consolidate the price volatility experienced on the power exchange with gaming and strategic bidding by suppliers to increase profits. Therefore, in order to find a mathematical model of the sensitively-responding-to-price loads, the price-sensitive load model is needed. And the development of state-of-the-art technologies affects the electricity price, so the diffusion of high-efficient end-uses and these price affect load patterns. This paper shows the analysis on learning curves algorithms which is used to investigate the correlation of the end-uses' price and load patterns.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.349-360
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• 2013

Forecasting the daily peak load for electricity demand is an important issue for future power plants and power management. We first introduce several time series models to predict the peak load for electricity demand and then compare the performance of models under the RMSE(root mean squared error) and MAPE(mean absolute percentage error) criteria.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.11 no.2
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• pp.244-249
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• 1999

This study is to examine the emission rate of $CO_2$gas as the environmental load in office buildings. After the investigation of monthly consumption of each energy source(electricity and natural gas), it is analyzed that the $CO_2$emission rate of 34 office buildings surveyed is 22.4kg-$c/m^2$.year, which consists of 17.5kg-$c/m^2$.year by consuming electricity, and 4.9kg-$c/m^2$.year by consuming natural gas. And the $CO_2$emission rate of each load in those buildings consists of 68% emitted by general electricity, 16% by cooling load and 16% by heating load. It is also proposed that the $CO_2$emission rate of cooling and heating load is profoundly pertinent to the variation of outdoor temperature.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1821-1830
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• 2018

With the wide application of intelligent household appliances, the optimization of electricity behavior has become an important component of home-based intelligent electricity. In this study, a multi-objective optimization model in an intelligent electricity environment is proposed based on economy and comfort. Firstly, the domestic consumer's load characteristics are analyzed, and the operating constraints of interruptible and transferable electrical appliances are defined. Then, constraints such as household electrical load, electricity habits, the correlation minimization electricity expenditure model of household appliances, and the comfort model of electricity use are integrated into multi-objective optimization. Finally, a continuous search multi-objective particle swarm algorithm is proposed to solve the optimization problem. The analysis of the corresponding example shows that the multi-objective optimization model can effectively reduce electricity costs and improve electricity use comfort.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.17-18
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• 1991

In electric Industry, the improvement of load factor by flattening load has been considered to be more important than any other tasks and has received wide concern and interest. Especially while annual peak load had occurred early evening in winter during past decades, but we found the trend has changed so that annual peak load occurred during the daytime in summer since 1981. In this paper we introduce various method for the load management.