• Title/Summary/Keyword: forecasting accuracy

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Forecasting Internet Traffic by Using Seasonal GARCH Models

  • Kim, Sahm
    • Journal of Communications and Networks
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    • v.13 no.6
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    • pp.621-624
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    • 2011
  • With the rapid growth of internet traffic, accurate and reliable prediction of internet traffic has been a key issue in network management and planning. This paper proposes an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) error model for forecasting internet traffic and evaluates its performance by comparing it with seasonal autoregressive integrated moving average (ARIMA) models in terms of root mean square error (RMSE) criterion. The results indicated that the seasonal AR-GARCH models outperformed the seasonal ARIMA models in terms of forecasting accuracy with respect to the RMSE criterion.

Short-Term Load Forecast for Near Consecutive Holidays Having The Mixed Load Profile Characteristics of Weekdays and Weekends (평일과 주말의 특성이 결합된 연휴전 평일에 대한 단기 전력수요예측)

  • Park, Jeong-Do;Song, Kyung-Bin;Lim, Hyeong-Woo;Park, Hae-Soo
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.61 no.12
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    • pp.1765-1773
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    • 2012
  • The accuracy of load forecast is very important from the viewpoint of economical power system operation. In general, the weekdays' load demand pattern has the continuous time series characteristics. Therefore, the conventional methods expose stable performance for weekdays. In case of special days or weekends, the load demand pattern has the discontinuous time series characteristics, so forecasting error is relatively high. Especially, weekdays near the thanksgiving day and lunar new year's day have the mixed load profile characteristics of both weekdays and weekends. Therefore, it is difficult to forecast these days by using the existing algorithms. In this study, a new load forecasting method is proposed in order to enhance the accuracy of the forecast result considering the characteristics of weekdays and weekends. The proposed method was tested with these days during last decades, which shows that the suggested method considerably improves the accuracy of the load forecast results.

A Study on Forecasting Spare Parts Demand based on Data-Mining (데이터 마이닝 기반의 수리부속 수요예측 연구)

  • Kim, Jaedong;Lee, Hanjun
    • Journal of Internet Computing and Services
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    • v.18 no.1
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    • pp.121-129
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    • 2017
  • Demand forecasting is one of the most critical tasks in defense logistics, because the failure of the task can bring about a huge waste of budget. Up to date, ROK-MND(Republic of Korea - Ministry of National Defense) has analyzed past component consumption data with time-series techniques to predict each component's demand. However, the accuracy of the prediction still needs to be improved. In our study, we attempted to find consumption pattern using data mining techniques. We gathered an 18,476 component consumption data first, and then derived diverse features to utilize them in identification of demanding patterns in the consumption data. The results show that our approach improves demand forecasting with higher accuracy.

Short-Term Electrical Load Forecasting using Neuro-Fuzzy Models (뉴로-퍼지 모델을 이용한 단기 전력 수요 예측시스템)

  • Park, Yeong-Jin;Sim, Hyeon-Jeong;Wang, Bo-Hyeon
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.49 no.3
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    • pp.107-117
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    • 2000
  • This paper proposes a systematic method to develop short-term electrical load forecasting systems using neuro-fuzzy models. The primary goal of the proposed method is to improve the performance of the prediction model in terms of accuracy and reliability. For this, the proposed method explores the advantages of the structure learning of the neuro-fuzzy model. The proposed load forecasting system first builds an initial structure off-line for each hour of four day types and then stores the resultant initial structures in the initial structure bank. Whenever a prediction needs to be made, the proposed system initializes the neuro-fuzzy model with the appropriate initial structure stored and trains the initialized model. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1993 and 1994 at KEPCO. Simulation results reveal that the prediction system developed in this paper can achieve a remarkable improvement on both accuracy and reliability compared with the prediction systems based on multilayer perceptrons, radial basis function networks, and neuro-fuzzy models without the structure learning.

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An Empirical Study of Financial Analyst's Forecasting Activities on the Firm's Operating Performances (기업실적에 대한 재무분석가의 예측활동에 관한 실증연구)

  • Kwak, Jae-Seok
    • The Korean Journal of Financial Management
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    • v.20 no.1
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    • pp.93-124
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    • 2003
  • This paper studies the financial analyst's forecasting activities on the firm's operating performance during the period from 1999 to 2003. In this study, financial analyst's forecasting activities are focused on the sales, operating income and net income and financial analyst's forecasting accuracy, forecasting revising patterns and forecasting activities to the unexpected firm's operating performance are studied. Some empirical findings in this study are as follows. First, standard estimate error on the sales, operating income and net income are all significantly negative value and so financial analyst's forecast on the firm's operating performance are upwardly biased. Second, domestic financial analyst's forecasting activities is relatively more accuracy than foreign financial analyst's forecasting activities. Third, forecasting time is more close to the end of the operating performance announcement day, forecasting activities are more accuracy. Fourth, comparing with individual financial analyst's forecast, consensus forecast is more accuracy. Fifth, in the comparative forecasting activities study according to the prior firm's operating performance, financial analyst's forecasting revision activities are found to be upward or downward. Sixth, financial analysts overreact in the sales forecast and underreact in the operating income and net income forecast. Seventh, in the empirical analysis on the Easterwood-Nutt's test model(1999) which the firm's performance change are divided into the expected performance change and the unexpected performance change, it is found that financial analyst's forecasting activities on the firm's operating performance are systematically optimistic.

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A Study on Improving Forecasting Accuracy for Expenditures of Residential Building Projects through Selecting Similar Cases

  • Yi June-Seong
    • Korean Journal of Construction Engineering and Management
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    • v.4 no.4 s.16
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    • pp.114-122
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    • 2003
  • Dynamic and fragmented characteristics are two of the most significant factors that distinguish the construction industry from other industries. Previous forecasting techniques have failed to solve the problems derived from the above characteristics, and do not provide considerable support This paper deals with providing a more precise forecasting by applying Case-based Reasoning (CBR). The newly developed model in this study enables project managers to forecast monthly expenditures with less time and effort by retrieving and referring only projects of a similar nature, while filtering out irrelevant cases included in database. For the purpose of accurate forecasting, the choice of the numbers of referring projects was investigated. It is concluded that selecting similar projects at $5{\~}6{\%}$ out of the whole database will produce a more precise forecasting. The new forecasting model, which suggests the predicted values based on previous projects, is more than just a forecasting methodology; it provides a bridge that enables current data collection techniques to be used within the context of the accumulated information. This will eventually help all the participants in the construction industry to build up the knowledge derived from invaluable experience.

Load Forecasting and ESS Scheduling Considering the Load Pattern of Building (부하 패턴을 고려한 건물의 전력수요예측 및 ESS 운용)

  • Hwang, Hye-Mi;Park, Jong-Bae;Lee, Sung-Hee;Roh, Jae Hyung;Park, Yong-Gi
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.65 no.9
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    • pp.1486-1492
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    • 2016
  • This study presents the electrical load forecasting and error correction method using a real building load pattern, and the way to manage the energy storage system with forecasting results for economical load operation. To make a unique pattern of target load, we performed the Hierarchical clustering that is one of the data mining techniques, defined load pattern(group) and forecasted the demand load according to the clustering result of electrical load through the previous study. In this paper, we propose the new reference demand for improving a predictive accuracy of load demand forecasting. In addition we study an error correction method for response of load events in demand load forecasting, and verify the effects of proposed correction method through EMS scheduling simulation with load forecasting correction.

Short-term Electric Load Forecasting for Summer Season using Temperature Data (기온 데이터를 이용한 하계 단기전력수요예측)

  • Koo, Bon-gil;Kim, Hyoung-su;Lee, Heung-seok;Park, Juneho
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.64 no.8
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    • pp.1137-1144
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    • 2015
  • Accurate and robust load forecasting model is very important in power system operation. In case of short-term electric load forecasting, its result is offered as an standard to decide a price of electricity and also can be used shaving peak. For this reason, various models have been developed to improve forecasting accuracy. In order to achieve accurate forecasting result for summer season, this paper proposes a forecasting model using corrected effective temperature based on Heat Index and CDH data as inputs. To do so, we establish polynomial that expressing relationship among CDH, load, temperature. After that, we estimate parameters that is multiplied to each of the terms using PSO algorithm. The forecasting results are compared to Holt-Winters and Artificial Neural Network. Proposing method shows more accurate by 1.018%, 0.269%, 0.132% than comparison groups, respectively.

Spatio-temporal Load Forecasting Considering Aggregation Features of Electricity Cells and Uncertainties in Input Variables

  • Zhao, Teng;Zhang, Yan;Chen, Haibo
    • Journal of Electrical Engineering and Technology
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    • v.13 no.1
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    • pp.38-50
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    • 2018
  • Spatio-temporal load forecasting (STLF) is a foundation for building the prediction-based power map, which could be a useful tool for the visualization and tendency assessment of urban energy application. Constructing one point-forecasting model for each electricity cell in the geographic space is possible; however, it is unadvisable and insufficient, considering the aggregation features of electricity cells and uncertainties in input variables. This paper presents a new STLF method, with a data-driven framework consisting of 3 subroutines: multi-level clustering of cells considering their aggregation features, load regression for each category of cells based on SLS-SVRNs (sparse least squares support vector regression networks), and interval forecasting of spatio-temporal load with sampled blind number. Take some area in Pudong, Shanghai as the region of study. Results of multi-level clustering show that electricity cells in the same category are clustered in geographic space to some extent, which reveals the spatial aggregation feature of cells. For cellular load regression, a comparison has been made with 3 other forecasting methods, indicating the higher accuracy of the proposed method in point-forecasting of spatio-temporal load. Furthermore, results of interval load forecasting demonstrate that the proposed prediction-interval construction method can effectively convey the uncertainties in input variables.

Application of Neural Networks to Short-Term Load Forecasting Using Electrical Load Pattern (전력부하의 유형별 단기부하예측에 신경회로망의 적용)

  • Park, Hu-Sik;Mun, Gyeong-Jun;Kim, Hyeong-Su;Hwang, Ji-Hyeon;Lee, Hwa-Seok;Park, Jun-Ho
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.48 no.1
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    • pp.8-14
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    • 1999
  • This paper presents the methods of short-term load forecasting Kohonen neural networks and back-propagation neural networks. First, historical load data is divided into 5 patterns for the each seasonal data using Kohonen neural networks and using these results, load forecasting neural network is used for next day hourly load forecasting. Next day hourly load of weekdays and weekend except holidays are forecasted. For load forecasting in summer, max-temperature and min-temperature data as well as historical hourly load date are used as inputs of load forecasting neural networks for a better forecasting accuracy. To show the possibility of the proposed method, it was tested with hourly load data of Korea Electric Power Corporation(1994-95).

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