• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.1
• /
• pp.25-30
• /
• 2016

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.

• Journal of Environmental Science International
• /
• v.21 no.6
• /
• pp.713-724
• /
• 2012

Emission rates of biogenic hydrocarbon emitted from broad-leaved trees grown at Jeju Island were estimated using a dynamic enclosure method. Leaf temperature, PAR and relative humidity were monitored during the sampling time. The emission rates of isoprene and monoterpene were measured for five plants(Carpinus laxiflora, Quercus serrata, Styrax japonicus, Quercus acutissima, Quercus crispula) during the sampling period at the Halla mountain sites. Among five tree species, the highest isoprene emission rate of 10.60 ${\mu}g\;gdw^{-1}hr^{-1}$ was observed for Quercus serrata. The seasonal emission rates were the highest during summer and the emission of isoprene was highly affected by light and temperature variations. The highest emission rate of isoprene was occurred between 13:00 and 14:00, but isoprene was not emitted in nighttime because of the absence of light.

• Journal of Korean Society of Transportation
• /
• v.32 no.4
• /
• pp.401-409
• /
• 2014

This study deals with capacity expansion planning of airport infrastructure in view of economic validation that reflect construction costs and social benefits according to the reduction of passengers' delay time. We first forecast the airport peak-demand which has a seasonal and cyclical feature with ARIMA model that has been one of the most widely used linear models in time series forecasting. A discrete event simulation model is built for estimating actual delay time of passengers that consider the passenger's dynamic flow within airport infrastructure after arriving at the airport. With the trade-off relationship between cost and benefit, we determine an economic quantity of conveyor that will be expanded. Through the experiment performed with the case study of Incheon international airport, we demonstrate that our approach can be an effective method to solve the airport expansion problem with seasonal passenger arrival and dynamic operational aspects in airport infrastructure.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.9 no.4
• /
• pp.1-12
• /
• 2006

The usages of urban riverine areas for the Gapchoen and Yudungcheon in Daejoen City were evaluated by analyzing riverbed characteristics and water quality and by surveying the status of the floodplain usage including questionnaires of people visiting the rivers. Both rivers appear to be stable with insignificant bed changes as the riverbeds are dominated by gravels. Water qualities of both rivers have been improved significantly over the past decade although there are quite large seasonal fluctuations, which is common in most rivers in Korea. The results of floodplain usage analyses show that Gapcheon is dominated with static uses (>70%) such as promenades and resting facilities, while Yudungcheon by dynamic uses (>44%) such as sports facilities. Overall, both rivers require better plans for riverine area usage management considering a balance between the dynamic uses and the static uses such as natural observation places for education and habitats for birds and fish in the rivers. The questionnaire survey results indicate that overall the present status of both rivers are satisfactory and that water quality improvement is one of the key factors to enhance the value of the riverine areas. Future river restoration should be conducted by taking into account the characteristics of urban rivers in harmony with surrounding natural sceneries.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.1
• /
• pp.7-15
• /
• 2017

Ambient Adjusted line Rating(AAR) method for overhead transmission lines considering Risk Tolerance(RT) was proposed in this paper. AAR is suitable for system operators to plan their operation strategy and maintenance schedule because this can be designed as a seasonal line rating. Several candidate transmission lines are chosen to apply the proposed method in the paper. As a result, it is shown that system reliability was significantly enhanced through maximizing transfer capability, solving the system constraints.

• Proceedings of the KSRS Conference
• /
• 2009.03a
• /
• pp.97-100
• /
• 2009

The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. An adaptive feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. In this study, the Normalized Difference Vegetation Index (NDVI) was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula for 1996 and 2000 using a dynamic technique, and the adaptive reconstruction of harmonic model was then applied to the NDVI time series for tracking changes on the ground surface. The results show that the adaptive approach is potentially very effective for continuously monitoring changes on near-real time.

• The Journal of Society for e-Business Studies
• /
• v.24 no.1
• /
• pp.1-16
• /
• 2019

Recently, since responding to meteorological changes depending on increasing greenhouse gas and electricity demand, the importance prediction of photovoltaic power (PV) is rapidly increasing. In particular, the prediction of PV power generation may help to determine a reasonable price of electricity, and solve the problem addressed such as a system stability and electricity production balance. However, since the dynamic changes of meteorological values such as solar radiation, cloudiness, and temperature, and seasonal changes, the accurate long-term PV power prediction is significantly challenging. Therefore, in this paper, we propose PV power prediction model based on deep learning that can be improved the PV power prediction performance by learning to use meteorological and seasonal information. We evaluate the performances using the proposed model compared to seasonal ARIMA (S-ARIMA) model, which is one of the typical time series methods, and ANN model, which is one hidden layer. As the experiment results using real-world dataset, the proposed model shows the best performance. It means that the proposed model shows positive impact on improving the PV power forecast performance.

• Transactions of the KSME C: Technology and Education
• /
• v.3 no.1
• /
• pp.1-13
• /
• 2015

In this study, a new dynamic VRF-type heat pump simulation model is proposed which incorporates frosting and defrosting models. Toward this end, a simple frosting model based on the perfect analogy, and lumped system based defrost model, are proposed. Then, frosting and defrosting models are incorporated into a dynamic heat pump model which adopts segment-by-segment local heat exchanger model and map-based variable speed compressor model. Thus, the model can naturally represent locally uneven frosting and defrosting on the heat exchanger surface. Developed simulation model is validated against available experimental data to show good agreement within 10% error for capacity and COP. Finally, developed dynamic heat pump model is applied to annual heating season simulation to show that seasonal COP of heat pump is degraded by 7% due to frosting and defrosting.

• Journal of Korean Society on Water Environment
• /
• v.26 no.2
• /
• pp.356-364
• /
• 2010

The chemical and biological reaction of the aquatic organism is closely related with temperature variation and water temperature is one of the most important factors that should be considered in establishing sustainable reservoir operation scheme to minimize adverse environmental impacts related with dam construction. This paper investigates temperature variation in the downstream of Yongdam Reservoir using sampled data collected from total 8 temperature monitoring stations placed along the main river and the major tributaries. Using KoRiv1, 1-dimensional dynamic water quality simulation model, temperature variation in the downstream of Yongdam Reservoir has been simulated. The simulated results were compared with sampled data collected from May 15 to August 1 2008 by applying two different temperature modeling schemes, equilibrium temperature and full heat budget method. From the result of statistical analysis, seasonal temperature variation has been simulated by applying the equilibrium temperature scheme for comparison of the difference between the reservoir operation and the natural conditions.

• Smart Structures and Systems
• /
• v.4 no.2
• /
• pp.183-194
• /
• 2008

An important historical monument of Cyprus is an aqueduct that was built in 1747 to provide water to the city of Larnaca and to its port. Because of its importance to the cultural heritage of Cyprus, the aqueduct has been selected as one of the case-study monuments in the project Wide-Range Non-Intrusive devices toward Conservation of Historical Monuments in the Mediterranean Area (WIND-CHIME). Detailed drawings of the aqueduct obtained from the Department of Antiquities of Cyprus have been used for the development of a computational model. The model was fine-tuned through the measurement of the dynamic characteristics of the aqueduct using forced and ambient vibrations. It should be noted that measurement of the dynamic characteristics of the structure were performed twice in a period of three years (June of 2004 and May of 2007). Significant differences were noted and they are attributed to soil structure interaction effects due to seasonal variations of the water-level in a nearby salt-lake. The system identification results for both cases are presented here. This monument was used to test the effectiveness of shape memory alloy (SMA) pre-stressed devices, which were developed during the course of the project, in protecting it without spoiling its monumental value.