• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.4
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• pp.183-190
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• 2018

Predicting accurate electricity prices is an important task in the electricity trading market. To address the electricity price forecasting problem, various approaches have been proposed so far and it is known that linear regression-based approaches are the best. However, the use of such linear regression-based methods is limited due to low accuracy and performance. In traditional linear regression methods, it is not practical to find a nonlinear regression model that explains the training data well. If the training data is complex (i.e., small-sized individual data and large-sized features), it is difficult to find the polynomial function with n terms as the model that fits to the training data. On the other hand, as a linear regression model approximating a nonlinear regression model is used, the accuracy of the model drops considerably because it does not accurately reflect the characteristics of the training data. To cope with this problem, we propose a new electricity price forecasting method that divides the entire dataset to multiple split datasets and find the best linear regression models, each of which is the optimal model in each dataset. Meanwhile, to improve the performance of the proposed method, we modify the proposed localized linear regression method in the map and reduce way that is a framework for parallel processing data stored in a Hadoop distributed file system. Our experimental results show that the proposed model outperforms the existing linear regression model. Specifically, the accuracy of the proposed method is improved by 45% and the performance is faster 5 times than the existing linear regression-based model.

• KIEAE Journal
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• v.11 no.5
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• pp.91-96
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• 2011

This study is to analysis the characteristics of electric power consumption of apartments complex in Korea. This study shows the pattern of electric power consumption and correlation of each apartment complex's completion year monthly and timely. With this result, we are able to predict the demand pattern of electricity in a house and make the schedule by demand pattern. It is expected this data is used as reference of electric consumption of Daejeon area to operate the simulation tools to predict the building energy. The yearly data of 10 apartment complexes of 2010 are analyzed. The results of this study are followed. The averaged amount of electricity consumption in winter is higher as summer because of the high capacity of heating equipment. All of the house has electric base load from 0.26kWh to 0.5kWh. The average of the electricity consumption of month is shown as 310.2kWh. A week is seperated, as 4 part such as week, weekend, Saturday and Sunday. During week, the average of timely electricity consumption is shown as 0.426kWh. The Saturday consumption is 0.437kWh. The Sunday is 0.445kWh. The peak electricity consumption in summer and winter is measured. The peak consumption on summer season is 1.389kW on 22th August 64% higher than winter season 0.887kW on 3rd January.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.703-720
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• 2019

The influence of temperature on electricity demand is increasing due to extreme weather and climate change, and the climate impacts involves nonlinearity, asymmetry and complexity. Considering changes in government energy policy and the development of the fourth industrial revolution, it is important to assess the climate effect more accurately for stable management of electricity supply and demand. This study aims to analyze the effect of temperature change on electricity demand using the partial linear model. The main results obtained using the time-unit high frequency data for meteorological variables and electricity consumption are as follows. Estimation results show that the relationship between temperature change and electricity demand involves complexity, nonlinearity and asymmetry, which reflects the nonlinear effect of extreme weather. The prediction accuracy of in-sample and out-of-sample electricity forecasting using the partial linear model evidences better predictive accuracy than the conventional model based on the heating and cooling degree days. Diebold-Mariano test confirms significance of the predictive accuracy of the partial linear model.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.9
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• pp.521-528
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• 2004

The demise of the native franchise markets and the emergence of competitive markets in electricity generation service is substantially altering the way that operation and planning activity is conducted and is making it increasingly difficult for market participants such as generation firms to prospect the future electricity markets. Traditional generation expansion planning (GEP) problems which centrally determine the least-cost capacity addition plan that meets forecasted demand within pre-specified reliability criteria over a planning horizon (typically 10 to 20 years) is becoming no more valid in competitive market environments. Therefore, it requires to develop a new methodology for generation investments, which is applicable to the changed electric industry business environments and is able to address the post-privatization situation where individual generation firms seek to maximize their return on generation investments against uncertain market revenues. This paper formulates a new generation expansion planning problem and solve it in a market-oriented manner.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1205-1210
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• 2007

Several mechanisms are introduced for the procurement of capacity adequacy. In the competitive electricity market, however, it is a GENCO that makes generation investment decision. A GENCO will invest a new generator when it can get more profit than cost. There requires a model to evaluate profit with respect to a new generation investment. In the view of long-term investment, evaluation of a profit of a generator in the electricity market is quite different from that of short-term operation. In this paper, a new profit-evaluation model is proposed for the long-term generation investment. It can treat the probabilistic characteristics of generators, ie, forced-outage-rates, which affect profit of generators.

• KDI Journal of Economic Policy
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• v.41 no.4
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• pp.67-99
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• 2019

This paper, focusing on the renewable portfolio standard (RPS), evaluates alternative renewable energy policies. We propose a tractable equilibrium model which provides a structural representation of Korea's electricity market, including its energy settlement system and renewable energy certificate (REC) transactions. Arbitrage conditions are used to define the core value of REC prices to identify relevant competitive equilibrium conditions. The model considers R&D investments and learning effects that may affect the development of renewable energy technologies. The model is parameterized to represent the baseline scenario under the currently scheduled RPS reinforcement for a 20% share of renewable generation, and then simulated for alternative scenarios. The result shows that the reinforcement of the RPS leads to higher welfare compared to weakening it as well as repealing it, though there remains room to enhance welfare. It turns out that subsidies are welfare-inferior to the RPS due to financial burdens and that reducing nuclear power generation from the baseline yields lower welfare by worsening environmental externalities.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1701-1710
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• 2012

Recently, it has been found that it is important to use a smart grid to reduce greenhouse-gas emissions worldwide. A smart grid is a digitally enabled electrical grid that gathers, distributes, and acts on information regarding the behavior of all participants (suppliers and consumers) to improve the efficiency, importance, reliability, economics, and sustainability of electricity services. The smart grid technology uses two-way communication, where users can monitor and limit the electricity consumption of their home appliances in real time. Likewise, power companies can monitor and limit the electricity consumption of home appliances for stabilization of the electricity supply. However, if information regarding the measured electricity consumption of a user is leaked, serious privacy issues may arise, as such information may be used as a source of data mining of the electricity consumption patterns or life cycles of home residents. In this paper, we propose a data transaction protocol for privacy protection in a smart grid. In addition, a power company cannot decrypt an encrypted home appliance ID without the user's password.

• Journal of Korean Society of Water and Wastewater
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• v.31 no.2
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• pp.177-186
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• 2017

This study concerned the analysis on the efficiency of the conversion of water tank type supply system to direct water supply system to examine the feasibility of the conversion, as well as the calculation of optimal conversion range that enables the supply of safe, high-quality water at stable pressure in accordance with the standards of water supply facility. The results of this research showed that when converting water supply system from water tank type supply system to direct water supply system, more nodal points could be properly converted and more reduction of electricity usage was expected in case water pressure rather than residence time was fixed. This means that higher efficacy can be obtained by fixing water pressure when converting water supply system. However, since the number of the locations that received on-spot inspection was small and the electricity usage measured was not exclusively by water supply facility, it is difficult to judge that such reduction of electricity usage accurately represents reduced electricity usage by water supply facility alone. therefore, after having secured on-spot information about a larger number of locations in apartment complexes that have converted water supply system, and utilizing information about electricity usage exclusively by water supply facility, the proposed method of this research could be applied to accurately deducing expected reduction of electricity usage by water supply facilities of various other apartment complexes. It is also considered possible to deduce an effective operation method of water supply system by finding out an area that shows low pressure or low residual chlorine concentration in the optimal conversion range of water supply, followed by estimating the proper location of pumping station or the proper chlorine dosage at the power purification plant that supply water to the target area.

• Journal of the Korean Solar Energy Society
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• v.26 no.1
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• pp.13-19
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• 2006

The efficiency of building-integrated photovoltaic(BIPV) system is mainly determined by solar radiation and the temperature of PV modules. The performance of BIPV systems is reported to be different from that of conventional PV systems installed in the open-air. This paper presents the relationship of solar radiation and electricity generation from a 2kWp roof-integrated PV system that is applied as building elements on an experimental house, and the energy saving effect of the BIPV system for a typical house. For the performance evaluation of the BIPV system, it produced a regression equation with measured data for winter days. The regression equation showed that a comparison of the measured electricity generation and the predicted electricity generation of the BIPV system were meaningful. It showed that an annual electricity generation of the system appeared to cover around 52% of an annual electricity consumption of a typical domestic house with the floor area of $96m^2$.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.81-91
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• 2024

Electrical energy is a key source of energy for modern civilization, and changes in electricity generation and consumption are closely related to industry and life in general. In this study, we identified the correlation between electricity generation and nighttime light values in South Korea and used it to predict monthly electricity generation trends in North Korea. The results of the study showed a low Pearson correlation coefficient of 0.34 between nighttime light and electricity generation in Seoul, but a high Pearson correlation coefficient of 0.79 between weighting for Seoul case nighttime light values and electricity generation using monthly average temperature. Using nighttime light values weighting for Seoul case by the average monthly temperature in Pyongyang to predict the monthly power generation trend in North Korea, we found that the month-on-month power generation increase in December 2022 was about 60% higher than the month-on-month power generation increase in December 2020 and 2021. The results of this study are expected to help predict monthly electricity generation trends in regions where monthly electricity generation data does not exist, making it difficult to identify timely industry trends.