• The Korean Journal of Applied Statistics
• /
• v.27 no.5
• /
• pp.843-853
• /
• 2014

Accurate electricity demand forecasting for daily peak load is essential for management and planning at electrical facilities. In this paper, we rst, introduce the several time series models that forecast daily peak load and compare the forecasting performance of the models based on Mean Absolute Percentage Error(MAPE). The results show that the Reg-AR-GARCH model outperforms other competing models that consider Cooling Degree Day(CDD) and Heating Degree Day(HDD) as well as seasonal components.

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

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

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

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.51 no.10
• /
• pp.453-459
• /
• 2002

This paper proposes an improved algorithm of the daily peak load forecasting for Korean Thanksgiving Day and the Lunar New Year's day. So far, many studies on the short-term load forecasting have been made to improve the accuracy of the load forecasting. However, the large errors of the load forecasting occur i case of Korean Thanksgiving Day and the Lunar New Year's Day. In order to reduce the errors of the load forecasting, the fuzzy linear regression method is introduced and a good selection method of the past load pattern is presented. Test results show that the proposed algorithm improves the accuracy of the load forecasting.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.51 no.3
• /
• pp.109-117
• /
• 2002

High accuracy of the load forecasting for power systems improves the security of the power system and generation cost. However, the forecasting problem is difficult to handle due to the nonlinear and the random-like behavior of system loads as well as weather conditions and variation of economical environments. So far. many studies on the problem have been made to improve the prediction accuracy using deterministic, stochastic, knowledge based and artificial neural net(ANN) method. In the conventional load forecasting method, the load forecasting maximum error occurred for the holidays on Saturday and Monday. In order to reduce the load forecasting error of the daily peak load for the holidays on Saturday and Monday, fuzzy concept and linear regression theory have been adopted into the load forecasting problem. The proposed algorithm shows its good accuracy that the average percentage errors are 2.11% in 1996 and 2.84% in 1997.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.53 no.6
• /
• pp.358-363
• /
• 2004

Due to the weather sensitivity of the power load, it is difficult to forecast accurately the peak power load of summer season. We improve the accuracy of the load forecasting considering weather condition. We introduced the sensitivity of temperature and proposed an improved forecasting algorithm. The proposed algorithm shows that the error of the load forecasting is 1.5%.

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

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.6
• /
• pp.860-865
• /
• 2014

This paper proposes the method for determination of the capacities of ESS (Energy Storage System) and PCS (Power Conditioning System) for the peak load shaving based on the load data in the substation of urban railway. In addition, this paper analyze the actual measurement of loads in the substation of urban railway. The load of a weekday in the substation of urban railway is high around rush hours in the morning and evening, which is different from that of a normal substation. The required capacities of ESS and PCS become larger as the amount of peak load shaving is higher, and are affected from the patterns of daily load in the substation.

• Proceedings of the KIEE Conference
• /
• 2000.07a
• /
• pp.301-303
• /
• 2000

This paper presents Kohonen neural network and wavelet transform analysis based technique for industrial peak load forecasting for the purpose of peak demand control. Firstly, one year of historical load data were sorted and clustered into several groups using Kohonen neural network and then wavelet transforms are adopted using the Biorthogonal mother wavelet in order to forecast the peak load of one hour ahead. The 5-level decomposition of the daily industrial load curve is implemented to consider the weather sensitive component of loads effectively. The wavelet coefficients associated with certain frequency and time localization is adjusted using the conventional multiple regression method and the components are reconstructed to predict the final loads through a six-scale synthesis technique.