• Title, Summary, Keyword: forecasting model

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Forecasting Total Marine Production through Multiple Time Series Model

  • Cho, Yong-Jun
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.1
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    • pp.63-76
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    • 2006
  • Marine production forecasting in fisheries is a crucial factor for managing and maintaining fishery resources. Thus this paper aims to generate a forecasting model of total marine production. The most generally method of time series model is to generate the most optimal single forecasting model. But the method could induce a different forecasting results when it does not properly infer a model To overcome the defect, I am trying to propose a single forecasting through multiple time series model. In other word, by comparing and integrating the output resulted from ARIMA and VAR model (which are typical method in a forecasting methodology), I tried to draw a forecasting. It is expected to produce more stable and delicate forecasting prospect than a single model. Through this, I generated 3 models on a yearly and monthly data basis and then here I present a forecasting from 2006 to 2010 through comparing and integrating 3 models. In conclusion, marine production is expected to show a decreasing tendency for the coming years.

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24 hour Load Forecasting using Combined Very-short-term and Short-term Multi-Variable Time-Series Model (초단기 및 단기 다변수 시계열 결합모델을 이용한 24시간 부하예측)

  • Lee, WonJun;Lee, Munsu;Kang, Byung-O;Jung, Jaesung
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.66 no.3
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    • pp.493-499
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    • 2017
  • This paper proposes a combined very-short-term and short-term multi-variate time-series model for 24 hour load forecasting. First, the best model for very-short-term and short-term load forecasting is selected by considering the least error value, and then they are combined by the optimal forecasting time. The actual load data of industry complex is used to show the effectiveness of the proposed model. As a result the load forecasting accuracy of the combined model has increased more than a single model for 24 hour load forecasting.

A Study on the Rainfall Forecasting Using Neural Network Model in Nakdong River Basin - A Comparison with Multivariate Model- (낙동강유역에서 신경망 모델을 이용한 강우예측에 관한 연구 - 다변량 모델과의 비교 -)

  • Cho, Hyeon-Kyeong;Lee, Jeung-Seok
    • Journal of the Korean Society of Industry Convergence
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    • v.2 no.2
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    • pp.51-59
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    • 1999
  • This study aims at the development of the techniques for the rainfall forecasting in river basins by applying neural network theory and compared with results of Multivariate Model (MVM). This study forecasts rainfall and compares with a observed values in the San Chung gauging stations of Nakdong river basin for the rainfall forecasting of river basin by proposed Neural Network Model(NNM). For it, a multi-layer Neural Network is constructed to forecast rainfall. The neural network learns continuous-valued input and output data. The result of rainfall forecasting by the Neural Network Model is superior to the results of Multivariate Model for rainfall forecasting in the river basin. So I think that the Neural Network Model is able to be much more reliable in the rainfall forecasting.

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Electricity Price Forecasting in Ontario Electricity Market Using Wavelet Transform in Artificial Neural Network Based Model

  • Aggarwal, Sanjeev Kumar;Saini, Lalit Mohan;Kumar, Ashwani
    • International Journal of Control, Automation, and Systems
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    • v.6 no.5
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    • pp.639-650
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    • 2008
  • Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

A Basic Study on the Flood-Flow Forecasting System Model with Integrated Optimal Operation of Multipurpose Dams (댐저수지군의 최적연계운영을 고려한 유출예측시스템모형 구축을 위한 기초적 연구)

  • 안승섭
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.37 no.3_4
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    • pp.48-60
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    • 1995
  • A flood - flow forecasting system model of river basins has been developed in this study. The system model consists of the data management system(the observation and telemetering system, the rainfall forecasting and data-bank system), the flood runoff simulation system, the reservoir operation simulation system, the flood forecasting simulation system, the flood warning system and the user's menu system. The Multivariate Rainfall Forecasting model, Meteorological factor regression model and Zone expected rainfall model for rainfall forecasting and the Streamflow synthesis and reservoir regulation(SSARR) model for flood runoff simulation have been adopted for the development of a new system model for flood - flow forecasting. These models are calibrated to determine the optimal parameters on the basis of observed rainfall, 7 streamfiow and other hydrological data during the past flood periods.

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Cluster Analysis and Meteor-Statistical Model Test to Develop a Daily Forecasting Model for Jejudo Wind Power Generation (제주도 일단위 풍력발전예보 모형개발을 위한 군집분석 및 기상통계모형 실험)

  • Kim, Hyun-Goo;Lee, Yung-Seop;Jang, Moon-Seok
    • Journal of Environmental Science International
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    • v.19 no.10
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    • pp.1229-1235
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    • 2010
  • Three meteor-statistical forecasting models - the transfer function model, the time-series autoregressive model and the neural networks model - were tested to develop a daily forecasting model for Jejudo, where the need and demand for wind power forecasting has increased. All the meteorological observation sites in Jejudo have been classified into 6 groups using a cluster analysis. Four pairs of observation sites among them, all having strong wind speed correlation within the same meteorological group, were chosen for a model test. In the development of the wind speed forecasting model for Jejudo, it was confirmed that not only the use a wind dataset at the objective site itself, but the introduction of another wind dataset at the nearest site having a strong wind speed correlation within the same group, would enhance the goodness to fit of the forecasting. A transfer function model and a neural network model were also confirmed to offer reliable predictions, with the similar goodness to fit level.

Daily Maximum Electric Load Forecasting for the Next 4 Weeks for Power System Maintenance and Operation (전력계통 유지보수 및 운영을 위한 향후 4주의 일 최대 전력수요예측)

  • Jung, Hyun-Woo;Song, Kyung-Bin
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.63 no.11
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    • pp.1497-1502
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    • 2014
  • Electric load forecasting is essential for stable electric power supply, efficient operation and management of power systems, and safe operation of power generation systems. The results are utilized in generator preventive maintenance planning and the systemization of power reserve management. Development and improvement of electric load forecasting model is necessary for power system maintenance and operation. This paper proposes daily maximum electric load forecasting methods for the next 4 weeks with a seasonal autoregressive integrated moving average model and an exponential smoothing model. According to the results of forecasting of daily maximum electric load forecasting for the next 4 weeks of March, April, November 2010~2012 using the constructed forecasting models, the seasonal autoregressive integrated moving average model showed an average error rate of 6,66%, 5.26%, 3.61% respectively and the exponential smoothing model showed an average error rate of 3.82%, 4.07%, 3.59% respectively.

Development of Traffic Accident Forecasting Model in Pusan (부산시 교통사고예측모형의 개발)

  • 이일병;임현정
    • Journal of Korean Society of Transportation
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    • v.10 no.3
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    • pp.103-122
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    • 1992
  • The objective of this research is to develop a traffic accident forecasting model using traffic accident data in pusan from 1963 to 1991 and then to make short-term forecasts('93~'94) of traffic accidents in pusan. In this research, several forecasting models are developed. They include a multiple regression model, a time-series ARIMA model, a Logistic curve model, and a Gompertz curve model. Among them, the model which shows the most significance in forecasting accuracy is selected as the traffic accident forecasting model. The results of this research are as followings. 1. The existing model such as Smeed model which was developed for foreign countries shows only 47.8% explanation for traffic accident deaths in Korea. 2. A nonliner regression model ($R^2$=0.9432) and a Logistic curve model are appeared to be th gest forecasting models for the number of traffic accidents, and a Logistic curve model shows th most significance in predicting the accident deaths and injuries. 3. The forecasting figures of the traffic accidents in pusan are as followings: . In 1993, 31, 180 accidents are predicted to happen, and 430 persons are predicted to be deaths and 29, 680 persons are predicated to be injuries. . In 1994, 33, 710 accidents are predicted to happen, and 431.persons are predicted to be deat! and 30, 510 persons are predicted to be injuried. Therefore, preventive measures against traffic accidents are certainly required.

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Comparison of forecasting performance of time series models for the wholesale price of dried red peppers: focused on ARX and EGARCH

  • Lee, Hyungyoug;Hong, Seungjee;Yeo, Minsu
    • Korean Journal of Agricultural Science
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    • v.45 no.4
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    • pp.859-870
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    • 2018
  • Dried red peppers are a staple agricultural product used in Korean cuisine and as such, are an important aspect of agricultural producers' income. Correctly forecasting both their supply and demand situations and price is very important in terms of the producers' income and consumer price stability. The primary objective of this study was to compare the performance of time series forecasting models for dried red peppers in Korea. In this study, three models (an autoregressive model with exogenous variables [ARX], AR-exponential generalized autoregressive conditional heteroscedasticity [EGARCH], and ARX-EGARCH) are presented for forecasting the wholesale price of dried red peppers. As a result of the analysis, it was shown that the ARX model and ARX-EGARCH model, each of which adopt both the rolling window and the adding approach and use the agricultural cooperatives price as the exogenous variable, showed a better forecasting performance compared to the autoregressive model (AR)-EGARCH model. Based on the estimation methods and results, there was no significant difference in the accuracy of the estimation between the rolling window and adding approach. In the case of dried red peppers, there is limitation in building the price forecasting models with a market-structured approach. In this regard, estimating a forecasting model using only price data and identifying the forecast performance can be expected to complement the current pricing forecast model which relies on market shipments.

A Quality Forecasting System in Glass Melting Processes using Genetic Algorithms (유전 알고리즘을 이용한 유리 용해 공정에서의 불량예측 시스템)

  • Jung, Ho-Sang;Jeong, Bong-Ju
    • IE interfaces
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    • v.13 no.1
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    • pp.78-91
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    • 2000
  • This paper presents a computerized quality forecasting system for glass manufacturing. In forecasting the molten glass quality, we are concerned with three major issues : (1) to find the reasonable time lags between a set of process conditions and the quality measurement of glass products, (2) to find the most significant process variables affecting the quality, and (3) to construct the appropriate causal forecasting models using genetic algorithms. The experimental results show the proposed model results in better forecasting than linear regression model. The suggested forecasting model was implemented successfully and is being currently used in a real manufacturing line.

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