Capability of the Bayesian Forecasting Method to Predict Field Time Series

This paper addresses the challenge of forecasting the future values of gas turbine measureable quantities. The final aim is the simulation of “virtual sensors” capable of producing statistically coherent measurements aimed at replacing anomalous observations discarded from the time series. Among the different available approaches, the Bayesian forecasting method (BFM) adopted in this paper uses the information held by a pool of observations as knowledge base to forecast the values at a future state. The BFM algorithm is applied in this paper to Siemens field data to assess its prediction capability, by considering two different approaches, i.e., single-step prediction (SSP) and multistep prediction (MSP). While SSP predicts the next observation by using true data as base of knowledge, MSP uses previously predicted data as base of knowledge to perform the prediction of future time steps. The results show that BFM single-step average prediction error can be very low, when filtered field data are analyzed. On the contrary, the average prediction error achieved in case of BFM multistep prediction is remarkably higher. To overcome this issue, the BFM single-step prediction scheme is also applied to clusters of time-wise averaged data. In this manner, an acceptable average prediction error can be achieved by considering clusters composed of 60 observations.

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Capability of the Bayesian Forecasting Method to Predict Field Time Series

Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy ◽

10.1115/gt2018-75006 ◽

2018 ◽

Author(s):

Nicolò Gatta ◽

Mauro Venturini ◽

Lucrezia Manservigi ◽

Giuseppe Fabio Ceschini ◽

Giovanni Bechini

Keyword(s):

Time Series ◽

Prediction Error ◽

Field Data ◽

Single Step ◽

Bayesian Forecasting ◽

Virtual Sensors ◽

Average Prediction Error ◽

Prediction Scheme ◽

Forecasting Method ◽

Prediction Capability

This paper addresses the challenge of forecasting the future values of gas turbine measureable quantities. The final aim is the simulation of “virtual sensors” capable of producing statistically coherent measurements aimed at replacing anomalous observations discarded from the time series. Among the different available approaches, the Bayesian Forecasting Method (BFM) adopted in this paper uses the information held by a pool of observations as knowledge base to forecast the values at a future state. The BFM algorithm is applied in this paper to Siemens field data to assess its prediction capability, by considering two different approaches, i.e. single-step prediction (SSP) and multi-step prediction (MSP). While SSP predicts the next observation by using true data as base of knowledge, MSP uses previously predicted data as base of knowledge to perform the prediction of future time steps. The results show that BFM single-step average prediction error can be very low, when filtered field data are analyzed. On the contrary, the average prediction error achieved in case of BFM multi-step prediction is remarkably higher. To overcome this issue, the BFM single-step prediction scheme is also applied to clusters of time-wise averaged data. In this manner, an acceptable average prediction error can be achieved by considering clusters composed of 60 observations.

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Towards a synthesis of data-based and theory-based models of environmental systems

Water Science & Technology ◽

10.2166/wst.2006.012 ◽

2006 ◽

Vol 53 (1) ◽

pp. 101-108 ◽

Cited By ~ 3

Author(s):

Z. Lin ◽

M.B. Beck

Keyword(s):

Time Series ◽

Prediction Error ◽

Field Data ◽

The Other ◽

Theoretical Interpretation ◽

Environmental Systems ◽

Joint Application ◽

Overall Performance ◽

Recursive Prediction ◽

Use Of Models

A two-pronged approach to interpreting field data through the use of models is presented. This approach builds upon both data- and theory-based models and their associated methods of system identification. It seeks to overcome their respective limitations: that theory-based models are not unambiguously identifiable from the observations, while a well identified data-based model may not be capable of a satisfactory theoretical interpretation. The purpose of the approach is thereby to gain a deeper understanding of complex environmental systems. Recursive methods of time-series analysis are used to identify the data-based models and the modified recursive prediction error algorithm is employed for parameter estimation of the theory-based models. The results of these identification exercises for the two classes of models can be compared in terms of the macro-parameters of the studied system's time constant and steady-state gain. Two case studies are presented to illustrate the overall performance of the two-pronged approach. It is found that: (1) more is to be gained through the joint application of the two classes of models than the exclusive use of either; (2) to some extent, identifying the structure and estimating the parameters of one type of model can be improved by recourse to the corresponding results for the other; and (3) reconciliation of the results from identifying the two classes of model in the parameter space has significant advantages over the more familiar process of evaluating a model's performance in the terms of its (observed) state space features.

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Algae Migration Characteristics in Fresh Water Red Tide

Water Science & Technology ◽

10.2166/wst.1993.0162 ◽

1993 ◽

Vol 28 (7) ◽

pp. 197-201 ◽

Cited By ~ 1

Author(s):

Dunchun Wang ◽

Isao Somiya ◽

Shigeo Fujii

Keyword(s):

Water Quality ◽

Time Series ◽

Fresh Water ◽

Field Data ◽

Field Survey ◽

Red Tide ◽

Bottom Layer ◽

Total Biomass ◽

Quality Indices ◽

Vertical Distributions

To understand the algae migration characteristics in the fresh water red tide, we performed a field survey in the Shorenji Reservoir located in Nabari City, Japan. From the analysis of the field data, it is found that the patterns of vertical distributions of the indices representing biomass are very different in the morning and the afternoon. Since some water quality indices have reverse fluctuations between the surface and the bottom layer in respect of the time series changes and the total biomass of the vertical water column is relatively constant, it is concluded that vertical and daily biomass variation of red tide alga is caused by its daily migration, that is the movement from the bottom layer to the surface in the morning and the reverse movement in the afternoon.

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Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽

10.1515/phys-2021-0043 ◽

2021 ◽

Vol 19 (1) ◽

pp. 360-374

Author(s):

Yuan Pei ◽

Lei Zhenglin ◽

Zeng Qinghui ◽

Wu Yixiao ◽

Lu Yanli ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Data ◽

Load Forecasting ◽

Series Data ◽

Forecasting Model ◽

Combined Model ◽

Forecasting Accuracy ◽

Forecasting Method ◽

Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

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Study on forecasting method of hydrological time series

2020 International Conference on Information Science, Parallel and Distributed Systems (ISPDS) ◽

10.1109/ispds51347.2020.00023 ◽

2020 ◽

Author(s):

Sipei Huang ◽

Yue Liu ◽

Yuansheng Lou

Keyword(s):

Time Series ◽

Forecasting Method

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Dimension Analysis-Based Model for Prediction of Shale Compressive Strength

Advances in Materials Science and Engineering ◽

10.1155/2016/7948612 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Xiangyu Fan ◽

Fenglin Xu ◽

Lin Chen ◽

Qiao Chen ◽

Zhiwei Liu ◽

...

Keyword(s):

Compressive Strength ◽

Regression Model ◽

Nonlinear Regression ◽

Prediction Error ◽

Diameter Ratio ◽

Nonlinear Regression Model ◽

Average Prediction Error ◽

Model Average ◽

Dimension Analysis ◽

Longmaxi Shale

The compressive strength of shale is a comprehensive index for evaluating the shale strength, which is linked to shale well borehole stability. Based on correlation analysis between factors (confining stress, height/diameter ratio, bedding angle, and porosity) and shale compressive strength (Longmaxi Shale in Sichuan Basin, China), we develop a dimension analysis-based model for prediction of shale compressive strength. A nonlinear-regression model is used for comparison. A multitraining method is used to achieve reliability of model prediction. The results show that, compared to a multi-nonlinear-regression model (average prediction error = 19.5%), the average prediction error of the dimension analysis-based model is 19.2%. More importantly, our dimension analysis-based model needs to determine only one parameter, whereas the multi-nonlinear-regression model needs to determine five. In addition, sensitivity analysis shows that height/diameter ratio has greater sensitivity to compressive strength than other factors.

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NONLINEAR TIME SERIES ANALYSIS OF ELECTROENCEPHALOGRAM TRACINGS OF CHILDREN WITH AUTISM

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127412500447 ◽

2012 ◽

Vol 22 (03) ◽

pp. 1250044

Author(s):

LANCE ONG-SIONG CO TING KEH ◽

ANA MARIA AQUINO CHUPUNGCO ◽

JOSE PERICO ESGUERRA

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Prediction Error ◽

Children With Autism ◽

Nonlinear Time Series ◽

Nonlinear Time Series Analysis ◽

Normal Children ◽

Series Analysis ◽

Significant Difference ◽

Severe Autism

Three methods of nonlinear time series analysis, Lempel–Ziv complexity, prediction error and covariance complexity were employed to distinguish between the electroencephalograms (EEGs) of normal children, children with mild autism, and children with severe autism. Five EEG tracings per cluster of children aged three to seven medically diagnosed with mild, severe and no autism were used in the analysis. A general trend seen was that the EEGs of children with mild autism were significantly different from those with severe or no autism. No significant difference was observed between normal children and children with severe autism. Among the three methods used, the method that was best able to distinguish between EEG tracings of children with mild and severe autism was found to be the prediction error, with a t-Test confidence level of above 98%.

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