• Title/Summary/Keyword: long-term prediction

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Long-term prediction of safety parameters with uncertainty estimation in emergency situations at nuclear power plants

  • Hyojin Kim;Jonghyun Kim
    • Nuclear Engineering and Technology
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    • v.55 no.5
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    • pp.1630-1643
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    • 2023
  • The correct situation awareness (SA) of operators is important for managing nuclear power plants (NPPs), particularly in accident-related situations. Among the three levels of SA suggested by Ensley, Level 3 SA (i.e., projection of the future status of the situation) is challenging because of the complexity of NPPs as well as the uncertainty of accidents. Hence, several prediction methods using artificial intelligence techniques have been proposed to assist operators in accident prediction. However, these methods only predict short-term plant status (e.g., the status after a few minutes) and do not provide information regarding the uncertainty associated with the prediction. This paper proposes an algorithm that can predict the multivariate and long-term behavior of plant parameters for 2 h with 120 steps and provide the uncertainty of the prediction. The algorithm applies bidirectional long short-term memory and an attention mechanism, which enable the algorithm to predict the precise long-term trends of the parameters with high prediction accuracy. A conditional variational autoencoder was used to provide uncertainty information about the network prediction. The algorithm was trained, optimized, and validated using a compact nuclear simulator for a Westinghouse 900 MWe NPP.

CNN-LSTM Coupled Model for Prediction of Waterworks Operation Data

  • Cao, Kerang;Kim, Hangyung;Hwang, Chulhyun;Jung, Hoekyung
    • Journal of Information Processing Systems
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    • v.14 no.6
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    • pp.1508-1520
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    • 2018
  • In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

On Long-term Prediction Scheme in Ocean Engineering

  • Kwon, Sun-Hong;Kim, Dea-Woong
    • International Journal of Ocean Engineering and Technology Speciallssue:Selected Papers
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    • v.3 no.1
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    • pp.29-34
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    • 2000
  • This paper proposes a long-term prediction of offshore structures in ocean waves. All short-term statistics is generated by the simulation for all the combinations of significant wave heights and spectral peak periods. The simulation has been tested first on linear system, whose analytic solution is known, to verify if the simulation works accurately. Then the scheme was applied to the nonlinear system. This paper demonstrated that the proposed scheme could be an efficient tool in estimating the response of offshore structures.

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Evaluation of Decomposition Effect in Long-term Settlement Prediction of Fresh Refuse Landfill (신선한 쓰레기 매립지의 장기 침하 예측에 대한 분해효과 평가)

  • 박현일;이승래
    • Geotechnical Engineering
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    • v.14 no.6
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    • pp.127-138
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    • 1998
  • In refuse landfills, a considerable amount of settlement occurs due to the decomposition of refuse over several years. In this paper, several prediction methods are applied to the measured settlement data of fresh refuse sites. The effect of biological decomposition on the settlement characteristics is investigated in predicting the long-term settlement of refuse landfill sites in view of the predicted settlement curves and the amount of long-term settlement. Irrespective of the applied models, the long term settlement may not be correctly estimated if the model parameters do not contain the decomposition effects. Among the proposed several prediction methods, Gibson & Lo model and hyperbolic model seem to represent the long-term settlement characteristics, but the power creep law seems to considerably overestimate the long-term settlement.

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An experimental and numerical study on long-term deformation of SRC columns

  • An, Gyeong-Hee;Seo, Jun-Ki;Cha, Sang-Lyul;Kim, Jin-Keun
    • Computers and Concrete
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    • v.22 no.3
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    • pp.261-267
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    • 2018
  • Long-term deformation of a steel-reinforced concrete (SRC) column is different from that of a reinforced concrete (RC) column due to the different moisture distribution. Wide-flange steel in an SRC column obstructs diffusion and makes long-term deformation slower. Previous studies analyzed the characteristics of long-term deformation of SRC columns. In this study, an additional experiment is conducted to more precisely investigate the effect of wide-flange steel on the long-term deformation of SRC columns. Long-term deformation, especially creep of SRC columns with various types of wide-flange steel, is tested. Wide-flange steel for the experiment is made of thin acrylic panels that can block diffusion but does not have strength, because the main purpose of this study is to exclusively demonstrate the long-term deformation of concrete caused by moisture diffusion, not by the reinforcement ratio. Experimental results show that the long-term deformation of a SRC column develops slower than that in a RC column, and it is slower as the wide-flange steel hinders diffusion more. These experimental results can be used for analytical prediction of long-term deformation of various SRC columns. An example of the analytical prediction is provided. According to the experimental and analytical results, it is clear that a new prediction model for long-term deformation of SRC columns should be developed in further studies.

The Prediction of Long-Term Creep Behavior of Recycled PET Polymer Concrete (PET 재활용 폴리머 콘크리트의 장기 크리프 거동 예측)

  • 조병완;태기호;박종화;박성규
    • Proceedings of the Korea Concrete Institute Conference
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    • 2003.11a
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    • pp.445-448
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    • 2003
  • Polymer concrete using wastes PET recycled resin that is, in general, more excellent mechanical properties than portland cement concrete. A lot of works are carried out about short-term properties of polymer concrete, however, little work has done to define their long-term properties, that is, sustain load such as creep. In this study will show the data that can long-term behavior of polymer concrete by short term creep test of polymer concrete that was affect to the temperature and the time to predict to long-term creep behavior. Then prediction equation was similar tendency that was comparing to short-term creep test and long-term creep test.

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Prediction of Baltic Dry Index by Applications of Long Short-Term Memory (Long Short-Term Memory를 활용한 건화물운임지수 예측)

  • HAN, Minsoo;YU, Song-Jin
    • Journal of Korean Society for Quality Management
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    • v.47 no.3
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    • pp.497-508
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    • 2019
  • Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.

Comparison of long-term forecasting performance of export growth rate using time series analysis models and machine learning analysis (시계열 분석 모형 및 머신 러닝 분석을 이용한 수출 증가율 장기예측 성능 비교)

  • Seong-Hwi Nam
    • Korea Trade Review
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    • v.46 no.6
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    • pp.191-209
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    • 2021
  • In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

A Multi-step Time Series Forecasting Model for Mid-to-Long Term Agricultural Price Prediction

  • Jonghyun, Park;Yeong-Woo, Lim;Do Hyun, Lim;Yunsung, Choi;Hyunchul, Ahn
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.2
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    • pp.201-207
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    • 2023
  • In this paper, we propose an optimal model for mid to long-term price prediction of agricultural products using LGBM, MLP, LSTM, and GRU to compare and analyze the three strategies of the Multi-Step Time Series. The proposed model is designed to find the optimal combination between the models by selecting methods from various angles. Prior agricultural product price prediction studies have mainly adopted traditional econometric models such as ARIMA and LSTM-type models. In contrast, agricultural product price prediction studies related to Multi-Step Time Series were minimal. In this study, the experiment was conducted by dividing it into two periods according to the degree of volatility of agricultural product prices. As a result of the mid-to-long-term price prediction of three strategies, namely direct, hybrid, and multiple outputs, the hybrid approach showed relatively superior performance. This study academically and practically contributes to mid-to-long term daily price prediction by proposing an effective alternative.

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.