• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.184-186
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• 2000

This paper proposes the introduction of TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. TAR model is a piecewise linear autoregressive model. In the scatter diagram of daily peak load versus daily maximum or minimum temperature, we can find out that the load-temperature relationship has a negative slope in lower regime and a positive slope in upper regime due to the heating and cooling load, respectively. In this paper, daily peak load was forecasted by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.329-330
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• 2015

From the results of previous my paper [10] in 2015 year of economic and electrical power storage research conference in Naju, this paper describes an application of autoregressive and moving average (ARMA) model to forecast hourly average wind speed (HAWS) in Jeju island. The models are used to build up short-term forecast of hourly average wind speed by the weighted sum of previous wind speed values.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1567-1573
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• 2017

The k-nearest neighbor (k-NN) algorithm is one of the most widely used benchmark algorithm in classification. Nowadays it has been further applied to predict time series. However, one of the main concerns of the algorithm applied on short-term electricity load forecasting is high computational burden. In the paper, we propose an approach of dimension reduction that follows the principles of highlighting the temperature effect on electricity load data series. The results show the proposed approach is able to reduce the dimension of the data around 30%. Moreover, with temperature effect highlighting, the approach will contribute to finding similar days accurately, and then raise forecasting accuracy slightly.

• Journal of Energy Engineering
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• v.6 no.1
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• pp.19-25
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• 1997

This paper presents the short-term operation scheduling on cogeneration system connected with auxiliary equipment by using the possibility fuzzy theory. The possibility fuzzy theory is a method to obtain the possibility boundary of the solution from the fuzzification of coefficients. Simulation is carried out to obtain the boundary of heat production in each time interval. Simulation results show the flexible operation boundary to establish effectively operation scheduling.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.914-916
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• 1997

This paper presents the development of an Artificial Neural Networks(ANN) for Short-Term Load Forecasting(STLF). First, used historical load data is divided into 5 patterns for the each seasonal data using Kohonen networks. Second, classified data is used as inputs of Back-propagation networks for next day hourly load forecasting. The proposed method was tested with KEPCO hourly record (1994-95) and we obtained desirable results.

• Geomechanics and Engineering
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• v.9 no.2
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• pp.195-205
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• 2015

Barricade failures generally occur at the early times of paste backfill when it is fresh in the stopes. The backfill strength increases and need for barricading pressure decreases as a result of the hydration reactions. In this study, paste backfill barricades of Cayeli copper mine were investigated to design cemented mineral processing plant tailings as barricade body concrete. Paste backfill in sub-level caving stopes of the mine needs to be barricaded for only four or five days. Therefore, short term strength and workability tests were applied on several cemented tailings material designs. Barricade failure mechanisms, important points of barricade designing and details of the new concrete material are explained in this work. According to the results obtained with this experimental study, the tailings were assessed to be used in concrete applied as temporary supports such as cemented paste backfill barricades.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.6
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• pp.259-266
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• 2005

Short-term load forecasting algorithm using neural networks and the sensitivity of temperatures in the summer season is proposed. In recent 10 years, many researchers have focused on artificial neural network approach for the load forecasting. In order to improve the accuracy of the load forecasting, input parameters of neural networks are investigated for three training cases of previous 7-days, 14-days, and 30-days. As the result of the investigation, the training case of previous 7-days is selected in the proposed algorithm. Test results show that the proposed algorithm improves the accuracy of the load forecasting.

• Journal of the Ergonomics Society of Korea
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• v.9 no.1
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• pp.15-20
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• 1990

In order to design man-machine system, communication system and other tasks that require information, we need to understand the characteristics of hyman short-term memory (STM). Thus, the purpose of this thesis is to investigate the influences of information stress and retention time on human performances and their relation- ships for STM of visual invormation. Eight subjects performed the computer monitering with STM task. The results showed that performance on serial recall from STM get wores and response time (and completion time) on information transmission by recall from STM increase as information stress and retention time increase. Also, there existed inverse proportional relationship between recall performance and response time (and completion time).

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1001-1006
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• 2017

In this study the NARX was proposed as a novel approach to forecast electric load more accurately. The NARX model is a recurrent dynamic network. ISO-NewEngland dataset was employed to evaluate and validate the proposed approach. Obtained results were compared with NAR network and some other popular statistical methods. This study showed that the proposed approach can be applied to forecast electric load and NARX has high potential to be utilized in modeling dynamic systems effectively.