Time Series Modeling for Analysis and Control

Ohtsu, Kohei; Peng, Hui; Kitagawa, Genshiro

doi:10.1007/978-4-431-55303-8

Cited by 7 publications

(2 citation statements)

References 43 publications

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“…The predictive modelling is carried out with the method random forest [30] and an ARX (autoregressive with exogenous variables) time series model [31]. To predict the total heating power ŷt in February, the January data is used to train a model.…”

Section: Statistical Testsmentioning

confidence: 99%

Residual Analysis of Predictive Modelling Data for Automated Fault Detection in Building’s Heating, Ventilation and Air Conditioning Systems

et al. 2020

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Faults in Heating, Ventilation and Air Conditioning (HVAC) systems affect the energy efficiency of buildings. To date, there rarely exist methods to detect and diagnose faults during the operation of buildings that are both cost-effective and sufficient accurate. This study presents a method that uses artificial intelligence to automate the detection of faults in HVAC systems. The automated fault detection is based on a residual analysis of the predicted total heating power and the actual total heating power using an algorithm that aims to find an optimal decision rule for the determination of faults. The data for this study was provided by a detailed simulation of a residential case study house. A machine learning model and an ARX model predict the building operation. The model for fault detection is trained on a fault-free data set and then tested with a faulty operation. The algorithm for an optimal decision rule uses various statistical tests of residual properties such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that it is possible to predict faults for both known faults and unknown faults. The challenge is to find the optimal algorithm to determine the best decision rules. In the outlook of this study, further methods are presented that aim to solve this challenge.

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Section: Statistical Testsmentioning

confidence: 99%

Residual Analysis of Predictive Modelling Data for Automated Fault Detection in Building’s Heating, Ventilation and Air Conditioning Systems

et al. 2020

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“…The total heating power is the model output and the values of the total heating power one hour and two hours ago at each point in time are added as predictors (features) to the models. The predictive modelling is carried out with the method random forest [15] and an ARX (autoregressive with exogenous variables) time series model [16]. To predict the total heating powerŷ t in February, the January data is used for training and the February data for testing.…”

Section: Statistical Testsmentioning

confidence: 99%

Identifying faults in the building system based on model prediction and residuum analysis

et al. 2020

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The energy efficiency of the building HVAC systems can be improved when faults in the running system are known. To this day, there are no cost-efficient, automatic methods that detect faults of the building HVAC systems to a satisfactory degree. This study induces a new method for fault detection that can replace a graphical, user-subjective evaluation of a building data measured on site with an automatic, data-based approach. This method can be a step towards cost-effective monitoring. For this research, the data from a detailed simulation of a residential case study house was used to compare a faultless operation of a building with a faulty operation. We argue that one can detect faults by analysing the properties of residuals of the prediction to the actual data. A machine learning model and an ARX model predict the building operation, and the method employs various statistical tests such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that the amount of data, the type and density of system faults significantly affect the accuracy of the prediction of faults. It became apparent that the challenge is to find a decision rule for the best combination of statistical tests on residuals to predict a fault.

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Improved autoregressive model for correction of noise serial correlation in fast fMRI

Luo

Misaki

Mulyana

et al. 2020

Magnetic Resonance in Med

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Purpose: In rapidly acquired functional MRI (fast fMRI) data, the noise serial correlations (SC) can produce problematically overestimated T-statistics which lead to invalid statistical inferences. This study aims to evaluate and improve the accuracy of high-order autoregressive model (AR(p), where p is the model order) based prewhitening method in the SC correction. Methods: Fast fMRI images were acquired at rest (null data) using a multiband simultaneous multi-slice echo planar imaging pulse sequence with repetition time (TR) = 300 and 500 ms. The SC effect in the fast fMRI data was corrected using the prewhitening method based on two AR(p) models: (1) the conventional model (fixed AR(p)) which preselects a constant p for all the image voxels; (2) an improved model (AR AICc ) that employs the corrected Akaike information criterion voxel-wise to automatically select the model orders for each voxel. To evaluate accuracy of SC correction, false positive characteristics were measured by assuming the presence of block and event-related tasks in the null data without image smoothing. The performance of prewhitening was also examined in smoothed images by adding pseudo task fMRI signals into the null data and comparing the detected to simulated activations (ground truth). Results: The measured false positive characteristics agreed well with the theoretical curve when using the AR AICc , and the activation maps in the smoothed data matched the ground truth. The AR AICc showed improved performance than the fixed AR(p) method. Conclusion: The AR AICc can effectively remove noise SC, and accurate statistical analysis results can be obtained with the AR AICc correction in fast fMRI. K E Y W O R D SAkaike information criterion, autocorrelation, autoregressive model, fast fMRI, noise serial correlation, prewhitening 1294 | LUO et aL.

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Time Series Modeling for Analysis and Control

Cited by 7 publications

References 43 publications

Residual Analysis of Predictive Modelling Data for Automated Fault Detection in Building’s Heating, Ventilation and Air Conditioning Systems

Residual Analysis of Predictive Modelling Data for Automated Fault Detection in Building’s Heating, Ventilation and Air Conditioning Systems

Identifying faults in the building system based on model prediction and residuum analysis

Improved autoregressive model for correction of noise serial correlation in fast fMRI

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