• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.6
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• pp.54-59
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• 2003

This paper presents hourly system marginal price forecasting of the Korea electric power system using a fuzzy linear regression analysis method. The proposed method is tested by forecasting hourly system marginal price for a week of spring in 2002. The percent average of forecasting error for the proposed method is from 3.14% to 6.10% in the weekdays, from 7.04% to 8.22% in the weekends, and comparable with a artificial neural networks method.

• International Journal of Advanced Culture Technology
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• v.2 no.2
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• pp.11-14
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• 2014

In the smart grid, it will be possible to communicate with the consumers for the purposes of monitoring and controlling their power consumption without disturbing their business or comfort. This will bring easier administration capabilities for the utilities. On the other hand, consumers will require more advanced home automation tools which can be implemented by using advanced sensor technologies. For instance, consumers may need to adapt their consumption according to the dynamically varying electricity prices which necessitates home automation tools. This paper tries to combine neural network and nonlinear autoregressive with exogenous variable (NARX) class for next week electric load forecasting. The suitability of the proposed approach is illustrated through an application to electric load consumption data. The suggested system provides a useful and suitable tool especially for the load forecasting.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1481-1483
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• 1999

Power distribution system planning for maximum customer satisfaction and system efficiency requires accurate forecast of future demand in service area. Spatial load forecasting method provides a more accurate estimation of both magnitudes and location of future electrical load. This method considers the causes of load growth due to addition of customers and per capita consumption among customers by land use (residential, commercial and industrial). So the land-use study and it's preference for small area is quite important. This paper proposes land-use preference estimation method based on fuzzy logic. Fuzzy logic is applied to computing preference scores for each land-use and by these scores the customer growth is allocated in service area. An simulation example is used to illustrate the proposed method.

• Proceedings of the Korean System Dynamics Society
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• 2006.04a
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• pp.101-122
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• 2006

A series of system dynamics model was developed for forecasting demand and supply of human resource in the electricity industry. To forecast demand of human resource in the electric power industry, BLS (Bureau of Labor Statistics) methodology was used. To forecast supply of human resource in the electric power industry, forecasting on the population of our country and the number of students in the department of electrical engineering were performed. After performing computer simulation with developed system dynamics model, it is discovered that the shortage of human resource in the electric power industry will be 3,000 persons per year from 2006 to 2015, and more than a double of current budget is required to overcome this shortage of human resource.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.1-6
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• 2009

The traditional demand prediction was based on the technique wherein electric power corporations made monthly or seasonal estimation of electric power consumption for each area and subscription type for the next one or two years to consider both seasonally generated and local consumed amounts. Note, however, that techniques such as pricing, power generation plan, or sales strategy establishment were used by corporations without considering the production, comparison, and analysis techniques of the predicted consumption to enable efficient power consumption on the actual demand side. In this paper, to calculate the predicted value of electric power consumption on a short-term basis (15 minutes) according to the amount of electric power actually consumed for 15 minutes on the demand side, we performed comparison and analysis by applying a 15-minute interval prediction technique to the average and that to the time series analysis to show how they were made and what we obtained from the simulations.

• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.35-42
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• 2021

In order to produce electric vehicle demand forecasting information, which is an important element of the plan to expand charging facilities for electric vehicles, a model for predicting electric vehicle demand was proposed using Exponential Smoothing. In order to establish input data for the model, the monthly power demand of cities and counties was applied as independent variables, monthly electric vehicle charging stations, monthly electric vehicle charging stations, and monthly electric vehicle registration data. To verify the accuracy of the electric vehicle power demand prediction model, we compare the results of the statistical methods Exponential Smoothing (ETS) and ARIMA models with error rates of 12% and 21%, confirming that the ETS presented in this paper is 9% more accurate as electric vehicle power demand prediction models. It is expected that it will be used in terms of operation and management from planning to install charging stations for electric vehicles using this model in the future.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.108-112
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• 2012

A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly wind energy is unlimited in potential. However, due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. To cope with this, many works have been done for wind speed and power forecasting. It is reported that, compared with physical persistent models, statistical techniques and computational methods are more useful for short-term forecasting of wind power. Among them, support vector regression (SVR) has much attention in the literature. This paper proposes an SVR based wind speed forecasting. To improve the forecasting accuracy, a fuzzy clustering is adopted in the process of SVR modeling. An illustrative example is also given by using real-world wind farm dataset. According to the experimental results, it is shown that the proposed method provides better forecasts of wind power.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.698-700
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• 1996

A knowledge based forecasting system for special days has been developed for the economic and secure operation of electric power system. If-then production rules has been adopted in this system to be used in various environmental conditions. Graphic user interfaces enables a user to access easily to the system. The simulation based on the historical data have shown that the forecasting result was improved remarkably when compared to the results from the conventional statistical methods. The forecasting results can be used for power system operational planning to improve security and economy of the power system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.6
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• pp.17-22
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• 2013

Fuzzy linear regression method has been used for short-term load forecasting of the special day in the previous researches. However, considerable load forecasting errors would be occurring if a special day is located on Saturday or Monday. In this paper, a new load forecasting method for the consecutive holidays is proposed with the consideration of the power demand variation rate. In the proposed method, a exponential smoothing model reflecting temperature is used to short-term load forecasting for Sunday during the consecutive holidays and then the loads of the special day during the consecutive holidays is calculated using the hourly power demand variation rate between the previous similar consecutive holidays. The proposed method is tested with 10 cases of the consecutive holidays from 2009 to 2012. Test results show that the average accuracy of the proposed method is improved about 2.96% by comparison with the fuzzy linear regression method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8576-8584
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• 2015

Load forecasting is needed to make supply and demand plan for a stable supply of electricity. It is also necessary for optimal operational plan of the power system planning. In particular, in order to ensure stable power supply, long-term load forecasting is important. And regional load forecasting is important for tightening supply stability. Regional load forecasting is known to be an essential process for the optimal state composition and maintenance of the electric power system network including transmission lines and substations to meet the load required for the area. Therefore, in this paper we propose a forecasting method using SARIMA during the 12 months (long-term/mid-term) load forecasting by 16 regions of the South Korea.