• Journal of Hydrogen and New Energy
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• v.33 no.5
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• pp.591-597
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• 2022

Recently, technologies for efficient power grid operation have become important due to climate change. For this reason, predicting power demand using deep learning is being considered, and it is necessary to understand the influence of characteristics of each region, industrial structure, and climate. This study analyzed the power demand of New Jersey in US, with a high urbanization rate and a large service industry, and West Virginia in US, a low urbanization rate and a large coal, energy, and chemical industries. Using recurrent neural network algorithm, the power demand from January 2020 to August 2022 was learned, and the daily and weekly power demand was predicted. In addition, the power grid operation based on the power demand forecast was discussed. Unlike previous studies that have focused on the deep learning algorithm itself, this study analyzes the regional power demand characteristics and deep learning algorithm application, and power grid operation strategy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1019-1023
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• 2018

In this paper sensitivity analysis of temperature on special day electricity demand of land and Jeju Island is performed. The basic electricity demand per 3 hours is defined as electricity demand that reflects the GDP effect without the temperature influence. The temperature sensitivity per 3 hours is calculated through the relationship between special day electricity demand normalized to basic electricity demand and temperature. In the future, forecast error will be improved if the temperature sensitivity per 3 hours is applied to the special day load forecasting.

• Journal of Energy Engineering
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• v.23 no.3
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• pp.7-12
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• 2014

This paper suggest mandatory target predestinator of natural gas wholesale and retail provider will set appropriate target. To analysis natural gas energy saving trend forecast, reduce natural gas forecast and using technology and forecast analysis for equipment is draw based on result of developing tool that more detailed gas field. Also this paper calculate effect on energy saving through various scenarios, efficiency consideration of gas equipment and subsidy condition.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.104.1-104
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• 2001

This paper presents a technique to development load control and management system in order to limits a maximum load demand and saves electric energy consumption. The computer programming proper load forecasting algorithm associated with programmable logic control and digital power meter through inform of multidrop network RS 485 over the twisted pair, over all are contained in this system. The digital power meter can measure a load data such as V, I, pf, P, Q, kWh, kVarh, etc., to be collected in statistics data convey to data base system on microcomputer and then analyzed a moving linear regression of load to forecast load demand Eventually, the result by forecasting are used for compost of load management and shedding for demand monitoring, Cycling on/off load control, Timer control, and Direct control. In this case can effectively reduce the electric energy consumption cost for 10% ...

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.29-39
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• 2019

In this study, we introduce a model that satisfies energy efficiency and economical efficiency by introducing and demonstrating cogeneration generators in industrial complexes using various actual data collected at the site. The proposed model is composed of three scenarios, ie, full - time operation, scenario operated according to demand, and a fusion type. In this study, the power generation profit and surplus thermal energy are measured according to the operation of the generator, and the thermal energy is traded according to the demand of the customer to calculate the profit and loss including the heat and evaluate the economic efficiency. As a result of the study, it is relatively profitable to reduce the generation of the generator under the condition that the electricity rate is low and the gas rate is high, while the basic charge is not increased. On the contrary, if the electricity rate is high and the gas rate is low, The more you start up, the more profit you can see. These results show that even a cogeneration power plant with a low economic efficiency due to a low "spark spread" has sufficient economic value if it can sell more than a certain amount of heat energy from a nearby customer and adjust the applied power through peak management.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.2
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• pp.74-78
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• 2015

With the improvement of living standards and economic development, electricity consumption continues to grow. The electricity is a special energy which is hard to store, so its supply must be consistent with the demand. The objective of electricity demand forecasting is to make best use of electricity energy and provide balance between supply and demand. Hence, it is very important work to forecast electricity demand with higher precision. So, various forecasting methods have been developed. They can be divided into five broad categories such as time series models, regression based model, artificial intelligence techniques and fuzzy logic method without considering special-day effects. Electricity demand patterns on holidays can be often idiosyncratic and cause significant forecasting errors. Such effects are known as special-day effects and are recognized as an important issue in determining electricity demand data. In this research, we developed the power demand forecasting method using ELM(Extreme Learning Machine) for special day, particularly, lunar new year and Chuseok holiday.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.11
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• pp.1495-1504
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• 2013

Renewable energy integration and increased system complexities make system operator maintain supply and demand balance harder than before. To keep the grid frequency in a stable range, an appropriate spinning reserve margin should be procured with consideration of ever-changing system situation, such as demand, wind power output and generator failure. This paper propose a novel concept of dynamic reserve, which arrange different spinning reserve margin depending on time. To investigate the effectiveness of the proposed dynamic reserve, we developed a new short-term reliability criterion that estimates the probability of a spinning reserve shortage events, thus indicating grid frequency stability. Uncertainties of demand forecast error, wind generation forecast error and generator failure have been modeled in probabilistic terms, and the proposed spinning reserve has been applied to generation scheduling. This approach has been tested on the modified IEEE 118-bus system with a wind farm. The results show that the required spinning reserve margin changes depending on the system situation of demand, wind generation and generator failure. Moreover the proposed approach could be utilized even in case of system configuration change, such as wind generation extension.

• IE interfaces
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• v.24 no.4
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• pp.351-361
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• 2011

Forecasting of electricity demand have difficulty in adapting to abrupt weather changes along with a radical shift in major regional and global climates. This has lead to increasing attention to research on the immediate and accurate forecasting model. Technically, this implies that a model requires only a few input variables all of which are easily obtainable, and its predictive performance is comparable with other competing models. To meet the ends, this paper presents an energy demand forecasting model that uses the variable selection or extraction methods of data mining to select only relevant input variables, and employs support vector regression method for accurate prediction. Also, it proposes a novel performance measure for time-series prediction, shift index, followed by description on preprocessing procedure. A comparative evaluation of the proposed method with other representative data mining models such as an auto-regression model, an artificial neural network model, an ordinary support vector regression model was carried out for obtaining the forecast of monthly electricity demand from 2000 to 2008 based on data provided by Korea Energy Economics Institute. Among the models tested, the proposed method was shown promising results than others.

• Journal of Energy Engineering
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• v.17 no.1
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• pp.31-37
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• 2008

At present, the target amount of demand-side management and investment cost of EE (Energy Efficiency) program, which consists of high-efficiency appliances, has been estimated simply by the diffusion function based on the real historical data in the past or last year. In the internal and external condition, the penetration amount of each appliance has been estimated by Bass diffusion model which is expressed by time and three coefficients. And enough acquisition of real historical data is necessary for reasonable estimation of coefficients. In energy efficiency, to estimate the target amount of demand-side management, the penetration amount of each appliance should be primarily forecasted by Bass diffusion model in Korea. On going programs, however, lightings, inverters, vending machine and motors have a insufficient real historical data which is a essential condition to forecast the penetration amount using a Bass diffusion model due to the short period of program progress. In other words, the forecast of penetration amount may not be exact, so that it is necessary for the method of forecast to apply improvement of method. In this paper, the penetration amount of high-efficiency appliances is forecasted by Bass, virtual Bass, Logistic and Lawrence & Lawton diffusion models to analyze the diffusion progress. And also, by statistic standards, each penetration is compared with historical data for model suitability by characteristic of each appliance. Based on the these result, in the forecast of penetration amount by diffusion model, the reason for error occurrence caused by simple application of diffusion model and preferences of each diffusion model far a characteristic of data are analyzed.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.3
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• pp.95-99
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• 2013

The maximum power analysis simulator took advantage of the facilities and power consumption reduction simulator test scenario development and testing of improvement in the scenario. As a maximum demand power controller, Maximum power analysis simulator performs control and disperasion of maximum demand power by calculating base power, load forecast, and present power which are based on signal of watt-hour meter to keep the electricity under the target. In addition, various algorithms to select appropriate control methode on each of the light installations through the peak demand power is configured to management. The simulation shows the success of control power for the specified target controlled by five sequential lighting installations.