• Title/Summary/Keyword: demand prediction

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Development of the Roundwood Demand Prediction Model

  • Kim, Dong-Jun
    • Journal of Korean Society of Forest Science
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    • v.95 no.2
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    • pp.203-208
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    • 2006
  • This study compared the roundwood demand prediction accuracy of econometric and time-series models using Korean data. The roundwood was divided into softwood and hardwood by species. The econometric model of roundwood demand was specified with four explanatory variables; own price, substitute price, gross domestic product, dummy. The time-series model was specified with lagged endogenous variable. The dummy variable reflected the abrupt decrease in roundwood demand in the late 1990's in the case of softwood roundwood, and the boom of plywood export in the late 1970's in the case of hardwood roundwood. On the other hand, the prediction accuracy was estimated on the basis of Residual Mean Square Errors(RMSE). The results showed that the softwood roundwood demand prediction can be performed more accurately by econometric model than by time-series model. However, the hardwood roundwood demand prediction accuracy was similar in the case of using econometric and time-series model.

A Nonparametric Prediction Model of District Heating Demand (비모수 지역난방 수요예측모형)

  • Park, Joo Heon
    • Environmental and Resource Economics Review
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    • v.11 no.3
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    • pp.447-463
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    • 2002
  • The heat demand prediction is an essential issue in management of district heating system. Without an accurate prediction through the lead-time period, it might be impossible to make a rational decision on many issues such as heat production scheduling and heat exchange among the plants which are very critical for the district heating company. The heat demand varies with the temperature as well as the time nonlinearly. And the parametric specification of the heat demand model would cause a misspecification bias in prediction. A nonparametric model for the short-term heat demand prediction has been developed as an alternative to avoiding the misspecification error and tested with the actual data. The prediction errors are reasonably small enough to use the model to predict a few hour ahead heat demand.

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A Study on the Demand Prediction Model for Repair Parts of Automotive After-sales Service Center Using LSTM Artificial Neural Network (LSTM 인공신경망을 이용한 자동차 A/S센터 수리 부품 수요 예측 모델 연구)

  • Jung, Dong Kun;Park, Young Sik
    • The Journal of Information Systems
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    • v.31 no.3
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    • pp.197-220
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    • 2022
  • Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV2) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.

Clustering of Seoul Public Parking Lots and Demand Prediction (서울시 공영주차장 군집화 및 수요 예측)

  • Jeongjoon Hwang;Young-Hyun Shin;Hyo-Sub Sim;Dohyun Kim;Dong-Guen Kim
    • Journal of Korean Society for Quality Management
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    • v.51 no.4
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    • pp.497-514
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    • 2023
  • Purpose: This study aims to estimate the demand for various public parking lots in Seoul by clustering similar demand types of parking lots and predicting the demand for new public parking lots. Methods: We examined real-time parking information data and used time series clustering analysis to cluster public parking lots with similar demand patterns. We also performed various regression analyses of parking demand based on diverse heterogeneous data that affect parking demand and proposed a parking demand prediction model. Results: As a result of cluster analysis, 68 public parking lots in Seoul were clustered into four types with similar demand patterns. We also identified key variables impacting parking demand and obtained a precise model for predicting parking demands. Conclusion: The proposed prediction model can be used to improve the efficiency and publicity of public parking lots in Seoul, and can be used as a basis for constructing new public parking lots that meet the actual demand. Future research could include studies on demand estimation models for each type of parking lot, and studies on the impact of parking lot usage patterns on demand.

Deep Learning Model for Electric Power Demand Prediction Using Special Day Separation and Prediction Elements Extention (특수일 분리와 예측요소 확장을 이용한 전력수요 예측 딥 러닝 모델)

  • Park, Jun-Ho;Shin, Dong-Ha;Kim, Chang-Bok
    • Journal of Advanced Navigation Technology
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    • v.21 no.4
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    • pp.365-370
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    • 2017
  • This study analyze correlation between weekdays data and special days data of different power demand patterns, and builds a separate data set, and suggests ways to reduce power demand prediction error by using deep learning network suitable for each data set. In addition, we propose a method to improve the prediction rate by adding the environmental elements and the separating element to the meteorological element, which is a basic power demand prediction elements. The entire data predicted power demand using LSTM which is suitable for learning time series data, and the special day data predicted power demand using DNN. The experiment result show that the prediction rate is improved by adding prediction elements other than meteorological elements. The average RMSE of the entire dataset was 0.2597 for LSTM and 0.5474 for DNN, indicating that the LSTM showed a good prediction rate. The average RMSE of the special day data set was 0.2201 for DNN, indicating that the DNN had better prediction than LSTM. The MAPE of the LSTM of the whole data set was 2.74% and the MAPE of the special day was 3.07 %.

Development of the Plywood Demand Prediction Model

  • Kim, Dong-Jun
    • Journal of Korean Society of Forest Science
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    • v.97 no.2
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    • pp.140-143
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    • 2008
  • This study compared the plywood demand prediction accuracy of econometric and vector autoregressive models using Korean data. The econometric model of plywood demand was specified with three explanatory variables; own price, construction permit area, dummy. The vector autoregressive model was specified with lagged endogenous variable, own price, construction permit area and dummy. The dummy variable reflected the abrupt decrease in plywood consumption in the late 1990's. The prediction accuracy was estimated on the basis of Residual Mean Squared Error, Mean Absolute Percentage Error and Theil's Inequality Coefficient. The results showed that the plywood demand prediction can be performed more accurately by econometric model than by vector autoregressive model.

Development of the Lumber Demand Prediction Model

  • Kim, Dong-Jun
    • Journal of Korean Society of Forest Science
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    • v.95 no.5
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    • pp.601-604
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    • 2006
  • This study compared the accuracy of partial multivariate and vector autoregressive models for lumber demand prediction in Korea. The partial multivariate model has three explanatory variables; own price, construction permit area and dummy. The dummy variable reflected the boom of lumber demand in 1988, and the abrupt decrease in 1998. The VAR model consists of two endogenous variables, lumber demand and construction permit area with one lag. On the other hand, the prediction accuracy was estimated by Root Mean Squared Error. The results showed that the estimation by partial multivariate and vector autoregressive model showed similar explanatory power, and the prediction accuracy was similar in the case of using partial multivariate and vector autoregressive model.

The Comparison of Prediction Capability from Various Prediction methods on Demand. (수요예측시스템 상의 다양한 예측방법의 예측력 비교)

  • Kim, Do-Goan
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2017.05a
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    • pp.137-139
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    • 2017
  • Modern manufacturing fields have been changed to use optimal manufacturing volume on the optimal demand prediction. This research is to compare the prediction capability of various prediction methods. And then, it is to suggest a flexible selection of the optimal prediction method according to optimal prediction capability.

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Forecasting Electric Power Demand Using Census Information and Electric Power Load (센서스 정보 및 전력 부하를 활용한 전력 수요 예측)

  • Lee, Heon Gyu;Shin, Yong Ho
    • Journal of Korea Society of Industrial Information Systems
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    • v.18 no.3
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    • pp.35-46
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    • 2013
  • In order to develop an accurate analytical model for domestic electricity demand forecasting, we propose a prediction method of the electric power demand pattern by combining SMO classification techniques and a dimension reduction conceptualized subspace clustering techniques suitable for high-dimensional data cluster analysis. In terms of electricity demand pattern prediction, hourly electricity load patterns and the demographic and geographic characteristics can be analyzed by integrating the wireless load monitoring data as well as sub-regional unit of census information. There are composed of a total of 18 characteristics clusters in the prediction result for the sub-regional demand pattern by using census information and power load of Seoul metropolitan area. The power demand pattern prediction accuracy was approximately 85%.

Development of Daily Peak Power Demand Forecasting Algorithm with Hybrid Type composed of AR and Neuro-Fuzzy Model (자기회귀모델과 뉴로-퍼지모델로 구성된 하이브리드형태의 일별 최대 전력 수요예측 알고리즘 개발)

  • Park, Yong-San;Ji, Pyeong-Shik
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.63 no.3
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    • pp.189-194
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    • 2014
  • Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method based on hybrid type composed of AR and Neuro-Fuzzy model. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.