• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.405-421
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• 2019

Predicting how much energy to use is an important issue in society. However, it is more difficult to capture the usage characteristics of residential buildings than other buildings. This paper provides time series analysis methods for electricity consumption in a residential building. Temperature is closely related to electricity demand. An error correction model, which is a method of adjusting the error with time, is applied when a cointegration relation is established between variables. Therefore, we analyze data via ECMs with consideration of the temperature effect.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.937-944
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• 2022

Electric vehicles (EVs) have been growing to reduce energy consumption and greenhouse gas (GHG) emissions in the transportation sector. The increasing number of EVs requires adequate recharging infrastructure, and at the same time, adopts low- or zero-emission electricity production because the GHG emissions are highly dependent on primary sources of electricity production. Although previous research has studied solar photovoltaic (PV) -integrated EV charging stations, it is challenging to optimize spatial areas between where the charging stations are required and where the renewable energy sources (i.e., solar photovoltaic (PV)) are accessible. Therefore, the primary objective of this research is to support decisions of siting EV charging stations using a spatial data clustering method integrated with Geographic Information System (GIS). This research explores spatial relationships of PV power outputs (i.e., supply) and traffic flow (i.e., demand) and tests a community in the state of Indiana, USA for optimal sitting of EV charging stations. Under the assumption that EV charging stations should be placed where the potential electricity production and traffic flow are high to match supply and demand, this research identified three areas for installing EV charging stations powered by rooftop PV in the study area. The proposed strategies will drive the transition of existing energy infrastructure into decentralized power systems. This research will ultimately contribute to enhancing economic efficiency and environmental sustainability by enabling significant reductions in electricity distribution loss and GHG emissions driven by transportation energy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2701-2717
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• 2023

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposed method was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.1
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• pp.1211-1217
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• 2001

This paper presnts a controller for the heat capacity of thermal storage systems using off-peak electricity which is composed of an identifier using neural networks and a storage time adjuster in order to store exactly the required thermal energy without loss. Since thermal storage systems have nonlinear characteristics and large time constant, even if we predict the heating load accurately, it is very difficult to store exactly the required thermal energy. Thus, in the neural network for the identifier, the adaptive learning rate for high learning speed and bit inputs based on state changes of thermal storage power source are used. Also a hardware for the controller using a microprocessor is developed. The performance of the proposed controller is shown by experiment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.11
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• pp.1536-1541
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• 2018

For effective electricity consumption in urban railway station such as peak load shaving, it is important to know each electrical load pattern by various usage. The total electricity consumption in the urban railway substation is already measured in Korea, but the electricity consumption for each usage is not measured. The author proposed the deep learning method to estimate the electrical load pattern for each usage in the urban railway substation with public data such as weather data. GRU (gated recurrent unit), a variation on the LSTM (long short-term memory), was used, which aims to solve the vanishing gradient problem of standard a RNN (recursive neural networks). The optimal model was found and the estimation results with that were assessed.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.310-312
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• 2000

This paper presents database for electricity demand-side management. Demand-Side Management(DSM) refers to programs that influence the usage of energy for improved economic efficiency and reduced environmental impact DSM can be looked upon as a tool for energy utilities to find resources on the demand side instead of on the supply side, or as a more general tool for society to better use and distribute scarce resources. In this paper, we construct the database for electricity demand-side management and apply it to residential and commercial sector.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.398-399
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• 2006

The procurement of generation and transmission/Distribution capacity in vertically-integrated electric industry is sufficient by facility construction in suitable time. However, the introduction of competitive electricity market increase the efficiency of availability for facilities and fuels. As a result, long-term capacity procurement is required for stable demand-supply balance since it is expected to maintain their generation capacity at a minimum for profit maximization. In this paper, a new long-term capacity procurement mechanism is proposed, which is able to assure supplemental contribution in competitive electricity market.

• Proceedings of the KIEE Conference
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• summer
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• pp.710-712
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• 2004

Generation companies(Genco) submit the supply functions as a bidding function to a bid market in a competitive electricity market. The profits of Gencos vary in accordance with the bid functions, so the selection of a bidding function plays a key role in increasing their profits. This paper presents an analysis of the selection of the supply function from the viewpoint of Nash equilibrium(NE). Four types of bidding function parameters are used for analizing the electricity market. The competition of selecting bidding parameters is modeled as subgame and overall game in this research. The NEs in both game are computed by using analytic method and payoff matrix method. It is verified in case studies for the NE of overall game to satisfy the equilibrium condition.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.432-434
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• 2002

Recently, electricity demand of remote islands in Korea has been rapidly increased. It's mainly due to increase of income level resulted from economic development. Electricity demand patterns and characteristics in remote islands are different from those of mainland in point of time of peak load, demographic and industrial characteristics of islands, and so on. The optimal power supply in remote islands has a important relationship with accurate analysis of island's load characteristics, the adoption of relevant load forecasting technique, and optimal power facilities reflecting local's electricity demand characteristics. This paper shows the recent load pattern and characteristics, load forecasting using probability distribution, and the perpetration of relevant power facilities in remote islands.

• Proceedings of the KIEE Conference
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• 2001.07c
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• pp.1496-1499
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• 2001

Optical and electrical properties of the $In_2Se_3$ single crystals grown by use of the Bridgman technique were examined in the transition temperature range between $\alpha$-phase and $\beta$-phase. $In_2Se_3$ single crystal has the rhombohedral structure and lattice constants are a=4.025 $\AA$, c=28.771 $\AA$ in C-axis. The transition temperatures of the stoichiometric $In_2Se_3$ single crystal is $10^{-2}{\Omega}cm^{-1}$ according to the specimens. However it varies rapidly in the transition region.