• 제목/요약/키워드: Exponential smoothing forecasting model

검색결과 63건 처리시간 0.027초

최대수요전력 관리 장치의 부하 예측에 관한 연구 (A Study on the Load Forecasting Methods of Peak Electricity Demand Controller)

  • 공인엽
    • 대한임베디드공학회논문지
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    • 제9권3호
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    • pp.137-143
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    • 2014
  • Demand Controller is a load control device that monitor the current power consumption and calculate the forecast power to not exceed the power set by consumer. Accurate demand forecasting is important because of controlling the load use the way that sound a warning and then blocking the load when if forecasted demand exceed the power set by consumer. When if consumer with fluctuating power consumption use the existing forecasting method, management of demand control has the disadvantage of not stable. In this paper, load forecasting of the unit of seconds using the Exponential Smoothing Methods, ARIMA model, Kalman Filter is proposed. Also simulation of load forecasting of the unit of the seconds methods and existing forecasting methods is performed and analyzed the accuracy. As a result of simulation, the accuracy of load forecasting methods in seconds is higher.

Hybrid CSA optimization with seasonal RVR in traffic flow forecasting

  • Shen, Zhangguo;Wang, Wanliang;Shen, Qing;Li, Zechao
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제11권10호
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    • pp.4887-4907
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    • 2017
  • Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm: An Application to the Data of Processed Cooked Rice

  • Takeyasu, Hiromasa;Higuchi, Yuki;Takeyasu, Kazuhiro
    • Industrial Engineering and Management Systems
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    • 제12권3호
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    • pp.244-253
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    • 2013
  • In industries, shipping is an important issue in improving the forecasting accuracy of sales. This paper introduces a hybrid method and plural methods are compared. Focusing the equation of exponential smoothing method (ESM) that is equivalent to (1, 1) order autoregressive-moving-average (ARMA) model equation, a new method of estimating the smoothing constant in ESM had been proposed previously by us which satisfies minimum variance of forecasting error. Generally, the smoothing constant is selected arbitrarily. However, this paper utilizes the above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate the smoothing constant. Thus, theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. This method is executed in the following method. Trend removing by the combination of linear and 2nd order nonlinear function and 3rd order nonlinear function is executed to the original production data of two kinds of bread. Genetic algorithm is utilized to search the optimal weight for the weighting parameters of linear and nonlinear function. For comparison, the monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non-monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.

딥러닝 모형을 활용한 공공자전거 대여량 예측에 관한 연구 (Forecasting of Rental Demand for Public Bicycles Using a Deep Learning Model)

  • 조근민;이상수;남두희
    • 한국ITS학회 논문지
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    • 제19권3호
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    • pp.28-37
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    • 2020
  • 본 연구는 공공자전거의 대여량을 예측하는 딥러닝 모형을 개발하였다. 이를 위하여 공공자전거 대여량 자료, 기상 자료, 그리고 지하철 이용량 자료를 수집하였다. 지수평활 모형, ARIMA 모형과 LSTM기반의 딥러닝 모형을 구축한 후 MSE와 MAE 평가 지표를 사용하여 예측 오차를 비교·평가하였다. 평가 결과, 지수평활 모형으로 MSE 348.74, MAE 14.15 값이 산출되었다. ARIMA 모형으로 MSE 170.10, MAE 9.30 값을 얻었다. 그리고 딥러닝 모형으로 MSE 120.22, MAE 6.76 값이 산출되었다. 지수평활 모형의 값과 비교하여 ARIMA 모형의 MSE는 51%, MAE는 34% 감소하였다. 그리고 딥러닝 모형의 MSE는 66%, MAE는 52% 감소하여 딥러닝 모형의 오차가 가장 적은 것으로 파악되었다. 이러한 결과로부터 공공자전거 대여량 예측 분야에서 딥러닝 모형의 적용시 예측 오차를 크게 감소시킬 수 있을 것으로 판단된다.

수요예측 모형의 비교분석에 관한 사례연구 (A comparative analysis of the Demand Forecasting Models : A case study)

  • 정상윤;황계연;김용진;김진
    • 산업경영시스템학회지
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    • 제17권31호
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    • pp.1-10
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    • 1994
  • The purpose of this study is to search for the most effective forecasting model for condenser with independent demand among the quantitative methods such as Brown's exponential smoothing method, Box-Jenkins method, and multiple regression analysis method. The criterion for the comparison of the above models is mean squared error(MSE). The fitting results of these three methods are as follows. 1) Brown's exponential smoothing method is the simplest one, which means the method is easy to understand compared to others. But the precision is inferior to other ones. 2) Box-Jenkins method requires much historic data and takes time to get to the final model, although the precision is superior to that of Brown's exponential smoothing method. 3) Regression method explains the correlation between parts with similiar demand pattern, and the precision is the best out of three methods. Therefore, it is suggested that the multiple regression method is fairly good in precision for forecasting our item and that the method is easily applicable to practice.

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Forecasting of Stream Qualities in Gumho River by Exponential Smoothing at Gumho2 Measurement Point using Monthly Time Series Data

  • Song, Phil-Jun;Lee, Bo-Ra;Kim, Jin-Yong;Kim, Jong-Tae
    • Journal of the Korean Data and Information Science Society
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    • 제18권3호
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    • pp.609-617
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    • 2007
  • The goal of this study is to forecast the trend of stream quality and to suggest some policy alternatives in Gumbo river. It used the five different monthly time series data such as BOD, COD, T-N and EC of the nine of Gumbo River measurement points from Jan. 1998 to Dec. 2006. Water pollution is serious at Gumbo2 and Palgeo stream measurement points. BOD, COD, T-N and EC data are analyzed with the exponential smoothing model and the trend is forecasted until Dec. 2009.

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Supremacy of Realized Variance MIDAS Regression in Volatility Forecasting of Mutual Funds: Empirical Evidence From Malaysia

  • WAN, Cheong Kin;CHOO, Wei Chong;HO, Jen Sim;ZHANG, Yuruixian
    • The Journal of Asian Finance, Economics and Business
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    • 제9권7호
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    • pp.1-15
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    • 2022
  • Combining the strength of both Mixed Data Sampling (MIDAS) Regression and realized variance measures, this paper seeks to investigate two objectives: (1) evaluate the post-sample performance of the proposed weekly Realized Variance-MIDAS (RVar-MIDAS) in one-week ahead volatility forecasting against the established Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the less explored but robust STES (Smooth Transition Exponential Smoothing) methods. (2) comparing forecast error performance between realized variance and squared residuals measures as a proxy for actual volatility. Data of seven private equity mutual fund indices (generated from 57 individual funds) from two different time periods (with and without financial crisis) are applied to 21 models. Robustness of the post-sample volatility forecasting of all models is validated by the Model Confidence Set (MCS) Procedures and revealed: (1) The weekly RVar-MIDAS model emerged as the best model, outperformed the robust DAILY-STES methods, and the weekly DAILY-GARCH models, particularly during a volatile period. (2) models with realized variance measured in estimation and as a proxy for actual volatility outperformed those using squared residual. This study contributes an empirical approach to one-week ahead volatility forecasting of mutual funds return, which is less explored in past literature on financial volatility forecasting compared to stocks volatility.

Forecasting Exchange Rates: An Empirical Application to Pakistani Rupee

  • ASADULLAH, Muhammad;BASHIR, Adnan;ALEEMI, Abdur Rahman
    • The Journal of Asian Finance, Economics and Business
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    • 제8권4호
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    • pp.339-347
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    • 2021
  • This study aims to forecast the exchange rate by a combination of different models as proposed by Poon and Granger (2003). For this purpose, we include three univariate time series models, i.e., ARIMA, Naïve, Exponential smoothing, and one multivariate model, i.e., NARDL. This is the first of its kind endeavor to combine univariate models along with NARDL to the best of our knowledge. Utilizing monthly data from January 2011 to December 2020, we predict the Pakistani Rupee against the US dollar by a combination of different forecasting techniques. The observations from M1 2020 to M12 2020 are held back for in-sample forecasting. The models are then assessed through equal weightage and var-cor methods. Our results suggest that NARDL outperforms all individual time series models in terms of forecasting the exchange rate. Similarly, the combination of NARDL and Naïve model again outperformed all of the individual as well as combined models with the lowest MAPE value of 0.612 suggesting that the Pakistani Rupee exchange rate against the US Dollar is dependent upon the macro-economic fundamentals and recent observations of the time series. Further evidence shows that the combination of models plays a vital role in forecasting, as stated by Poon and Granger (2003).

주간수요예측 전문가 시스템 개발 (Development of a Weekly Load Forecasting Expert System)

  • 황갑주;김광호;김성학
    • 대한전기학회논문지:전력기술부문A
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    • 제48권4호
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    • pp.365-370
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    • 1999
  • This paper describes the Weekly Load Forecasting Expert System(Named WLoFy) which was developed and implemented for Korea Electric Power Corporation(KEPCO). WLoFy was designed to provide user oriented features with a graphical user interface to improve the user interaction. The various forecasting models such as exponential smoothing, multiple regression, artificial nerual networks, rult-based model, and relative coefficient model also have been included in WLofy to increase the forecasting accuracy. The simulation based on historical data shows that the weekly forecasting results form WLoFy is an improvement when compared to the results from the conventional methods. Especially the forecasting accuracy on special days has been improved remakably.

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한국에서 산업재해율 예측에 의한 산업재해방지 전략에 관한 연구 (The Study on Strategy for Industrial Accident Prevention by the Industrial Accident Rate Forecasting in Korea)

  • 강영식;김태구;안광혁;최도림;정유나;이승호;박민아;이슬;김성현
    • 대한안전경영과학회:학술대회논문집
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    • 대한안전경영과학회 2011년도 춘계학술대회
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    • pp.177-183
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    • 2011
  • Korea has performed strategies for the third industrial accident prevention in order to minimize industrial accident. However, the occupational fatality rate and industrial accident rate appears to be stagnated for 11 years. Therefore, this paper forecasts the occupational fatality rate and industrial accident rate for 10 years. Also, this paper applies regression method (RA), exponential smoothing method (ESM), double exponential smoothing method (DESM), autoregressive integrated moving average (ARIMA) model and proposed analytical function method (PAFM) for trend of industrial accident. Finally, this paper suggests fundamental strategies for industrial accident prevention by forecasting of industrial accident rate in the long term.

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