• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1257-1264
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• 2018

This paper proposes Autoregressive Integrated Moving Average (ARIMA)-based forecasting algorithms using meteorological indices to predict seasonal peak load. First of all, this paper observes a seasonal pattern of the peak load that appears intensively in winter and summer, and generates ARIMA models to predict the peak load of summer and winter. In addition, this paper also proposes hybrid ARIMA-based models (ARIMA-Hybrid) using a discomfort index and a sensible temperature to enhance the conventional ARIMA model. To verify the proposed algorithm, both ARIMA and ARIMA-Hybrid models are developed based on peak load data obtained from 2006 to 2015 and their forecasting results are compared by using the peak load in 2016. The simulation result indicates that the proposed ARIMA-Hybrid models shows the relatively improved performance than the conventional ARIMA model.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.199-200
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• 2011

Load forecasting is very important for power system analysis and planning. This paper suggests yearly load forecasting of considering weekly normalization and seasonal load characteristics. Each weekly peak load is normalized and the average value is calculated. The new hourly peak load is seasonally collected. This method was used for yearly load forecasting. The results of the actual data and forecast data were calculated error rate by comparing.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.304-306
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• 2000

Load modeling, micro method, needs field test to identify the validity of methodology applied to modeling. This paper presents seasonal field test method and measurement results on serveral substations. Seasonal load characteristics were analyzed by the developed substation load model and correlation coefficients of seasonal load of substation under base, peak and average load time.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.161-171
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• 2019

Varied methods have been researched continuously because the past as the daily maximum electricity demand expectation has been a crucial task in the nation's electrical supply and demand. Forecasting the daily peak electricity demand accurately can prepare the daily operating program about the generating unit, and contribute the reduction of the consumption of the unnecessary energy source through efficient operating facilities. This method also has the advantage that can prepare anticipatively in the reserve margin reduced problem due to the power consumption superabundant by heating and air conditioning that can estimate the daily peak load. This paper researched a model that can forecast the next day's daily peak load when considering the influence of temperature and weekday, weekend, and holidays in the Seasonal ARIMA, TBATS, Seasonal Reg-ARIMA, and NNETAR model. The results of the forecasting performance test on the model of this paper for a Seasonal Reg-ARIMA model and NNETAR model that can consider the day of the week, and temperature showed better forecasting performance than a model that cannot consider these factors. The forecasting performance of the NNETAR model that utilized the artificial neural network was most outstanding.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2446-2448
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• 2001

In addition to the stabilization of electricity supply and the quality management of electricity, load balance has been an important strategy for achieving high quality load management. Among many techniques for load management, direct load management has been actively studied and applied for increasing the efficiency of power facility and suppressing peak load. In Korea, the highest peak load is demanded in summer rather than in winter, and almost 50% of the peak load comes from cooling load. Currently, applicable systems are limited to air conditioners that have the cooling capacity less than 2kW. This paper describes the development of remote controlled air conditioners and the result of the field test of the new type air conditioner. The technical specification based on the test will be applied to the new model of the remote controlled air conditioner. The wide distribution of the air conditioners to the public will be helpful to control peak demand due to cooling load in summer time. Financial investment to generating, transmission, distribution facilities will be decreased from flatting the seasonal power load.

• Proceedings of the KIEE Conference
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• 1985.07a
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• pp.144-147
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• 1985

In electricity industry, the improvement of load factor by flattening of load has been considered to be more important than any other tasks and has received wide concern and interest. Especially while annual peak load had occured early evening in winter during past decades, but we found the trend has changed so that annual peak load occured during the daytime in summer since1981 The useful practicing methods of this load management ale as follows; 1. Inducing of midnight load by thermal storage water heating 2. Seasonal differential rates. 3. Revising the peak load priceing (Time-of -use) It seems hard to expect that load research can be carried out in a short time, and we all have to exert outselves continuously to provide efficient load management method without wasting resources.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.349-360
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• 2013

Forecasting the daily peak load for electricity demand is an important issue for future power plants and power management. We first introduce several time series models to predict the peak load for electricity demand and then compare the performance of models under the RMSE(root mean squared error) and MAPE(mean absolute percentage error) criteria.

• IE interfaces
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• v.16 no.1
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• pp.16-26
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• 2003

In Korea, cooling power load, which occupies about 20% of peak load in 2000 and fluctuates depending on the popular usage of air conditioning systems, has been recently the focus of the load management. The first work of KEPCO (Korea Electric Power Corporation) to regulate cooling load as low as possible was to estimate its approximate scale and to develop the indirect methods to estimate it from the available time series data for the average hourly loads. However, KEPCO would like to have their methods improved both theoretically and practically. In this paper, we analyze their current indirect methods and detect their faults to design better indirect estimation methods. Under one of the assumptions of "no cooling load in April or May", the linear relationship between basic loads and GDP's, and the normalized seasonal factors of the Winters' multiplicative seasonal model, we provide ten indirect estimation methods in total and suggest the estimated cooling load(1988-1999) based on our various indirect methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2202-2205
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• 2011

Demand side management is used to maintain the reliability of power systems and to increase the economic benefits by avoiding power plant construction. This paper presents a systematic method to calculate the quantity of seasonal demand side management using time series. A numerical example is presented to calculate the quantity of demand side management in winter season using time series.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.141-146
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• 2014

This paper suggests a way of generating one-day load curves for performance improvement of peak shaving in a power system. This Peak Shaving algorithm is a long-term scheduling algorithm of PMS (Power Management System) for BESS (Battery Energy Storage System). The main purpose of a PMS is to manage the input and output power from battery modules placed in a power system. Generally, when a Peak Shaving algorithm is used, a difference occurs between predict load curves and real load curves. This paper suggests a way of minimizing the difference by making predict load curves that consider weekly normalization and seasonal load characteristics for smooth energy charging and discharging.