• Title/Summary/Keyword: Prediction intervals

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A Parameter Estimation Method of Multiple Time Interval for Low Frequency Oscillation Analysis (저주파진동 해석을 위한 다구간 파라미터 추정 방법)

  • Shim, Kwan-Shik;Kim, Sang-Tae;Choi, Joon-Ho;Nam, Hae-Kon;Ahn, Seon-Ju
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.63 no.7
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    • pp.875-882
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    • 2014
  • In this paper, we propose a new parameter estimation method that can deal with the data of multiple time intervals simultaneously. If there are common modes in the multiple time intervals, it is possible to create a new polynomial by summing the coefficients of the prediction error polynomials of each time interval. By calculating the roots of the new polynomial, it is possible to estimate the common modes that exist in each time interval. The accuracy of the proposed parameter estimation method has been proven by using appropriate test signals.

Design wind speed prediction suitable for different parent sample distributions

  • Zhao, Lin;Hu, Xiaonong;Ge, Yaojun
    • Wind and Structures
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    • v.33 no.6
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    • pp.423-435
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    • 2021
  • Although existing algorithms can predict wind speed using historical observation data, for engineering feasibility, most use moment methods and probability density functions to estimate fitted parameters. However, extreme wind speed prediction accuracy for long-term return periods is not always dependent on how the optimized frequency distribution curves are obtained; long-term return periods emphasize general distribution effects rather than marginal distributions, which are closely related to potential extreme values. Moreover, there are different wind speed parent sample types; how to theoretically select the proper extreme value distribution is uncertain. The influence of different sampling time intervals has not been evaluated in the fitting process. To overcome these shortcomings, updated steps are introduced, involving parameter sensitivity analysis for different sampling time intervals. The extreme value prediction accuracy of unknown parent samples is also discussed. Probability analysis of mean wind is combined with estimation of the probability plot correlation coefficient and the maximum likelihood method; an iterative estimation algorithm is proposed. With the updated steps and comparison using a Monte Carlo simulation, a fitting policy suitable for different parent distributions is proposed; its feasibility is demonstrated in extreme wind speed evaluations at Longhua and Chuansha meteorological stations in Shanghai, China.

Wind Power Interval Prediction Based on Improved PSO and BP Neural Network

  • Wang, Jidong;Fang, Kaijie;Pang, Wenjie;Sun, Jiawen
    • Journal of Electrical Engineering and Technology
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    • v.12 no.3
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    • pp.989-995
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    • 2017
  • As is known to all that the output of wind power generation has a character of randomness and volatility because of the influence of natural environment conditions. At present, the research of wind power prediction mainly focuses on point forecasting, which can hardly describe its uncertainty, leading to the fact that its application in practice is low. In this paper, a wind power range prediction model based on the multiple output property of BP neural network is built, and the optimization criterion considering the information of predicted intervals is proposed. Then, improved Particle Swarm Optimization (PSO) algorithm is used to optimize the model. The simulation results of a practical example show that the proposed wind power range prediction model can effectively forecast the output power interval, and provide power grid dispatcher with decision.

Development of a Prediction Model of Solar Irradiances Using LSTM for Use in Building Predictive Control (건물 예측 제어용 LSTM 기반 일사 예측 모델)

  • Jeon, Byung-Ki;Lee, Kyung-Ho;Kim, Eui-Jong
    • Journal of the Korean Solar Energy Society
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    • v.39 no.5
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    • pp.41-52
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    • 2019
  • The purpose of the work is to develop a simple solar irradiance prediction model using a deep learning method, the LSTM (long term short term memory). Other than existing prediction models, the proposed one uses only the cloudiness among the information forecasted from the national meterological forecast center. The future cloudiness is generally announced with four categories and for three-hour intervals. In this work, a daily irradiance pattern is used as an input vector to the LSTM together with that cloudiness information. The proposed model showed an error of 5% for learning and 30% for prediction. This level of error has lower influence on the load prediction in typical building cases.

Continuous Multiple Prediction of Stream Data Based on Hierarchical Temporal Memory Network (계층형 시간적 메모리 네트워크를 기반으로 한 스트림 데이터의 연속 다중 예측)

  • Han, Chang-Yeong;Kim, Sung-Jin;Kang, Hyun-Syug
    • KIPS Transactions on Computer and Communication Systems
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    • v.1 no.1
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    • pp.11-20
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    • 2012
  • Stream data shows a sequence of values changing continuously over time. Due to the nature of stream data, its trend is continuously changing according to various time intervals. Therefore the prediction of stream data must be carried out simultaneously with respect to multiple intervals, i.e. Continuous Multiple Prediction(CMP). In this paper, we propose a Continuous Integrated Hierarchical Temporal Memory (CIHTM) network for CMP based on the Hierarchical Temporal Memory (HTM) model which is a neocortex leraning algorithm. To develop the CIHTM network, we created three kinds of new modules: Shift Vector Senor, Spatio-Temporal Classifier and Multiple Integrator. And also we developed learning and inferencing algorithm of CIHTM network.

Deep Learning Research on Vessel Trajectory Prediction Based on AIS Data with Interpolation Techniques

  • Won-Hee Lee;Seung-Won Yoon;Da-Hyun Jang;Kyu-Chul Lee
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.3
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    • pp.1-10
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    • 2024
  • The research on predicting the routes of ships, which constitute the majority of maritime transportation, can detect potential hazards at sea in advance and prevent accidents. Unlike roads, there is no distinct signal system at sea, and traffic management is challenging, making ship route prediction essential for maritime safety. However, the time intervals of the ship route datasets are irregular due to communication disruptions. This study presents a method to adjust the time intervals of data using appropriate interpolation techniques for ship route prediction. Additionally, a deep learning model for predicting ship routes has been developed. This model is an LSTM model that predicts the future GPS coordinates of ships by understanding their movement patterns through real-time route information contained in AIS data. This paper presents a data preprocessing method using linear interpolation and a suitable deep learning model for ship route prediction. The experimental results demonstrate the effectiveness of the proposed method with an MSE of 0.0131 and an Accuracy of 0.9467.

Abrupt Noise Cancellation and Speech Restoration for Speech Enhancement (음질 개선을 위한 돌발잡음 제거와 음성복원)

  • Son BeakKwon;Hahn Minsoo
    • Proceedings of the KSPS conference
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    • 2003.10a
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    • pp.101-104
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    • 2003
  • In this paper, speech quality is improved by removing abrupt noise intervals and then substituting the gaps with estimates of the previous speech waveform. An abrupt noise detection signal has been proposed as a prediction error signal by utilizing LP coefficients of the previous frame. Abrupt noise intervals are estimated by using spectral energy. After removing estimated noise intervals, we applied several waveform substitution techniques such as zero substitution, previous frame repetition, pattern matching, and pitch waveform replication. To prove the validity of our algorithm, the LPC spectral distortion test and the recognition test are executed and, the results show that the speech quality is fairly well improved.

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Design of One-Class Classifier Using Hyper-Rectangles (Hyper-Rectangles를 이용한 단일 분류기 설계)

  • Jeong, In Kyo;Choi, Jin Young
    • Journal of Korean Institute of Industrial Engineers
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    • v.41 no.5
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    • pp.439-446
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    • 2015
  • Recently, the importance of one-class classification problem is more increasing. However, most of existing algorithms have the limitation on providing the information that effects on the prediction of the target value. Motivated by this remark, in this paper, we suggest an efficient one-class classifier using hyper-rectangles (H-RTGLs) that can be produced from intervals including observations. Specifically, we generate intervals for each feature and integrate them. For generating intervals, we consider two approaches : (i) interval merging and (ii) clustering. We evaluate the performance of the suggested methods by computing classification accuracy using area under the roc curve and compare them with other one-class classification algorithms using four datasets from UCI repository. Since H-RTGLs constructed for a given data set enable classification factors to be visible, we can discern which features effect on the classification result and extract patterns that a data set originally has.

Bayesian Prediction Inferences for the Burr Model Under the Random Censoring (랜덤중단(中斷)된 Burr모형(模型)에서 베이지안 예측추론(豫測推論))

  • Sohn, Joong-K.;Ko, Jeong-Hwan
    • Journal of the Korean Data and Information Science Society
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    • v.4
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    • pp.109-120
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    • 1993
  • Using a noninformative prior and a gamma prior, the Bayesian predictive density and the prediction intervals for a future observation or the p-th order statistic of n' future observations from the Burr distribution have been obtained. In additions, we examine the sensitivities of the results to the choice of model.

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