• International journal of advanced smart convergence
• /
• v.10 no.4
• /
• pp.59-65
• /
• 2021

Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.

• Atmosphere
• /
• v.31 no.1
• /
• pp.73-83
• /
• 2021

Numerical weather prediction (NWP) models play an essential role in predicting weather factors, but using them is challenging due to various factors. To overcome the difficulties of NWP models, deep learning models have been deployed in weather forecasting by several recent studies. This study adapts long short-term memory (LSTM), which demonstrates remarkable performance in time-series prediction. The combination of LSTM model input of meteorological features and activation functions have a significant impact on the performance therefore, the results from 5 combinations of input features and 4 activation functions are analyzed in 9 Automated Surface Observing System (ASOS) stations corresponding to cities/islands/mountains. The optimized LSTM model produces better performance within eight forecast hours than Local Data Assimilation and Prediction System (LDAPS) operated by Korean meteorological administration. Therefore, this study illustrates that this LSTM model can be usefully applied to very short-term weather forecasting, and further studies about CNN-LSTM model with 2-D spatial convolution neural network (CNN) coupled in LSTM are required for improvement.

• Journal of Distribution Science
• /
• v.21 no.7
• /
• pp.65-71
• /
• 2023

Purpose: This study attempted to analyze the impacts of the backward linkage and output multipliers, and investigate the price fluctuation and the price forecast amongst the manufacturing sectors associated with food processing industrial output of Thailand. Research design, data and methodology: The Thailand Input-Output table with a size of 180 x 180 sectors from 2005, 2010, and 2015 was utilized while the secondary data of the time series from January 2002 to December 2021 were processed via a multiplicative model and Box-Jenkins model. Results: The backward linkage analysis indicates that canning and preserving of the meat sector majorly utilized the factors of production from the slaughtering sector; canning and preservation of fish and other seafoods sector largely used those factors from the ocean and coastal fishing sector; and the sugar sector used those of the sugarcane sector. Notably, the output multiplier analysis indicated that output multipliers of those 3 manufacturing sectors were highly increased; meanwhile the price fluctuation continually existed in all forms. Besides, the price forecast suggested that prices of chicken and sugarcane tended to be higher; whereas, the price of shrimp was unstable. Conclusions: Food processing industry contains the favorable components to be one of the industries of the future of Thailand.

• Journal of the Korean earth science society
• /
• v.44 no.3
• /
• pp.185-195
• /
• 2023

The prolonged and heavy East Asian summer precipitation in 2020 may have been caused by an enhanced Madden-Julian Oscillation (MJO), which requires evaluation using forecast models. We examined the performance of GloSea6, an operational forecast model, in predicting the East Asian summer precipitation during July 2020, and investigated the role of MJO in the extreme rainfall event. Two experiments, CON and EXP, were conducted using different convection schemes, 6A and 5A, respectively to simulate various aspects of MJO. The EXP runs yielded stronger forecasts of East Asian precipitation for July 2020 than the CON runs, probably due to the prominent MJO realization in the former experiment. The stronger MJO created stronger moist southerly winds associated with the western North Pacific subtropical high, which led to increased precipitation. The strengthening of the MJO was found to improve the prediction accuracy of East Asian summer precipitation. However, it is important to note that this study does not discuss the impact of changes in the convection scheme on the modulation of MJO. Further research is needed to understand other factors that could strengthen the MJO and improve the forecast.

• Journal of the Korean earth science society
• /
• v.40 no.6
• /
• pp.572-583
• /
• 2019

In this study, the atmospheric vertical structure (AVS) associated with summertime (June, July, and August) heavy rainfall in Seoul was classified into three patterns (Loaded Gun: L, Inverted V: IV, and Thin Tube: TT) using rawinsonde soundings launched at Osan from 2009 to 2018. The characteristics of classified AVS and precipitation property were analyzed. Occurrence frequencies in each type were 34.7% (TT-type), 20.4% (IV-type), 20.4% (LG-type), and 24.5% (Other-type), respectively. The mean value of Convective Available Potential Energy (1131.1 J kg^-1) for LG-types and Storm Relative Helicity (357.6 ㎡s^-2) for TT-types was about 2 times higher than that of other types, which seems to be the difference in the mechanism of convection at the low level atmosphere. The composited synoptic fields in all cases showed a pattern that warm and humid southwesterly wind flows into the Korean Peninsula. In the cases of TT-type, the low pressure center (at 850 hPa) was followed by the trough in upper-level (at 500 hPa) as the typical pattern of a low pressure deepening. The TT-type was strongly influenced by the low level jet (at 850 hPa), showing a pattern of connecting the upper- and low-level jets. The result of analysis indicated that precipitation was intensified in the first half of all types. IV-type precipitation induced by thermal instability tended to last for a short term period with strong precipitation intensity, while TT-type by mechanical instability showed weak precipitation over a long term period.

• Journal of Technology Innovation
• /
• v.9 no.1
• /
• pp.95-120
• /
• 2001

One of the systematic attempts for technological forecasting is Delphi Method that externalizes and manipulates unformalized experts opinion in a particular problem or subject. It has, however, a critical shortcoming that it can not reflect the degree of interaction that exists among forecast events or subject. Gordon and Hayward(1969) criticize that when the forecast events are strongly interrelated, a totally unrealistic consensus may result. They proposed a new forecasting method that considers the interaction of events, that is, Cross Impact Analysis (CIA). A number of related models have been developed after them. In this study, we examine a variety of research results related to CIA obtained by literature survey and propose the limitation and future research direction. This analysis would be expected to help us to create a strategic scenario on future technology development at the government and firm level.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.25 no.4
• /
• pp.178-186
• /
• 2017

Air travel has become an essential part of the global society and its sustainable development is expected. Airlines profit structure and network operation will be influenced by internalization of external costs like emission charge. This additional cost of the airlines will be directly pose air ticket fare increase and demand of air passenger will be decreased. EU-ETS is a part of environmental binding to airlines fly to EU territory airports. This study analyzes the impact of emission charges by application of EU-ETS on airlines network change. For long-term forecast, a reliable estimation of the future price of carbon dioxide (CO2) will be used.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2008.05a
• /
• pp.2193-2196
• /
• 2008

In this study, the new methodology such as support vector machines neural networks model (SVM-NNM) using the statistical learning theory is introduced to forecast flood stage in Nakdong river, Republic of Korea. The SVM-NNM in hydrologic time series forecasting is relatively new, and it is more problematic in comparison with classification. And, the multilayer perceptron neural networks model (MLP-NNM) is introduced as the reference neural networks model to compare the performance of SVM-NNM. And, for the performances of the neural networks models, they are composed of training, cross validation, and testing data, respectively. From this research, we evaluate the impact of the SVM-NNM and the MLP-NNM for the forecasting of the hydrologic time series in Nakdong river. Furthermore, we can suggest the new methodology to forecast the flood stage and construct the optimal forecasting system in Nakdong river, Republic of Korea.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.111-112
• /
• 2008

Although two types of uncertainty such as randomness and fuzziness simultaneously exist in power systems, yet they have been treated as distinct fields to evaluate the power system reliability. Thus, this paper presents a reliability assessment method based on a combined concept of fuzzy and probability. To reflect the two-fold uncertainty, a modified load duration curve(MLDC) is proposed using the probability distribution of historical load data in which a fuzzy model for the peak load forecast is embedded. IEEE RTS system was used to demonstrate the usefulness and applicability of the proposed method, and the reliability indices were obtained using the proposed MLDC. The results show a wider insight into impact of load fuzziness on uncertainties of reliability indices for power systems.

• International Journal of High-Rise Buildings
• /
• v.9 no.1
• /
• pp.105-111
• /
• 2020

In recent years the construction sector has grown significantly in terms of investment and research on robotics and automation, yet it is still a low-tech and disjointed industry. One of the main scopes of this paper is to determine how robotic automation can provide the answers to the needs this industry has. To that end, an overall framework and development agenda of current technological innovation in the field has been outlined. Possible drawbacks and driving forces in the development of robots in the construction site have been identified. In addition, the review provides for state-of-the-art policies and regulations, as well as the short and medium-term outlook in different markets and countries. Ultimately, the forecast impact on traditional processes, construction sites, emerging technologies and related professions has been summarized in order to delineate prospective repercussions and future directions towards self-sufficiency.