It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer’s perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. ARMA(2,1,2)(1,1,1)7 and ARMA (0,1,1)(1,1,0)12 are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

A slope stability analysis has been done on Daesungri area by a computer program using limit equilibrium (Sarma method,1979). For this study, the authors modified the iteration technique and menu system of the algorithm which has been suggested by Hoek(1986). Sarma method is known as a suitable tool for evaluation of slope failure taking into account the kinematics of sliding blocks, and can be used for analysis of slopes of complex profiles such as circular, non-circular, planar sliding surfaces or any combination of these. The insertion of iteration technique and hydrodynamics in this method is very efficient, and gives an accurate estimate of static factor of safety. The analysis allows to specify different shear strength for each slice side and base, and therefore the submergence of any part of slopes is automatically analysed by taking the coordinates of phreatic surface. This program in combination with those stereographic technique developed by Appfied Geology Division of KIER(Korea Institute of Energy and Resources) in 1988 can be expected to give a useful result into evaluation of slope stability