Relevant bibliographies by topics / Peak prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Peak prediction'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Peak prediction"

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Schmitt, Thomas, Tobias Rodemann, and Jürgen Adamy. "The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing." Energies 14, no. 9 (April 29, 2021): 2569. http://dx.doi.org/10.3390/en14092569.

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Model predictive control (MPC) is widely used for microgrids or unit commitment due to its ability to respect the forecasts of loads and generation of renewable energies. However, while there are lots of approaches to accounting for uncertainties in these forecasts, their impact is rarely analyzed systematically. Here, we use a simplified linear state space model of a commercial building including a photovoltaic (PV) plant and real-world data from a 30 day period in 2020. PV predictions are derived from weather forecasts and industry peak pricing is assumed. The effect of prediction accuracy on the resulting cost is evaluated by multiple simulations with different prediction errors and initial conditions. Analysis shows a mainly linear correlation, while the exact shape depends on the treatment of predictions at the current time step. Furthermore, despite a time horizon of 24h, only the prediction accuracy of the first 75min was relevant for the presented setting.
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Xie, Lianku, Qinglei Yu, Jiandong Liu, Chunping Wu, and Guang Zhang. "Prediction of Ground Vibration Velocity Induced by Long Hole Blasting Using a Particle Swarm Optimization Algorithm." Applied Sciences 14, no. 9 (April 30, 2024): 3839. http://dx.doi.org/10.3390/app14093839.

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Obtaining accurate basic parameters for long hole blasting is challenging, and the resulting vibration damage significantly impacts key surface facilities. Predicting ground vibration velocity accurately and mitigating the harmful effects of blasting are crucial aspects of controlled blasting technology. This study focuses on the prediction of ground vibration velocity induced by underground long hole blasting tests. Utilizing the fitting equation based on the US Bureau of Mines (USBM) formula as a baseline for predicting peak particle velocity, two machine learning models suitable for small sample data, Support Vector Regression (SVR) machine and Random Forest (RF), were employed. The models were optimized using the particle swarm optimization algorithm (PSO) to predict peak particle velocity with multiple parameters specific to long hole blasting. Mean absolute error (MAE), mean Squared error (MSE), and coefficient of determination (R2) were used to assess the model predictions. Compared with the fitting equation based on the USBM model, both the Support Vector Regression (SVR) and Random Forest (RF) models accurately and effectively predict peak particle velocity, enhancing prediction accuracy and efficiency. The SVR model exhibited slightly superior predictive performance compared to the RF model.
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Nakashima, Toshihisa, Takayuki Ohno, Keiichi Koido, Hironobu Hashimoto, and Hiroyuki Terakado. "Accuracy of predicting the vancomycin concentration in Japanese cancer patients by the Sawchuk–Zaske method or Bayesian method." Journal of Oncology Pharmacy Practice 26, no. 3 (May 29, 2019): 543–48. http://dx.doi.org/10.1177/1078155219851834.

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Background In cancer patients treated with vancomycin, therapeutic drug monitoring is currently performed by the Bayesian method that involves estimating individual pharmacokinetics from population pharmacokinetic parameters and trough concentrations rather than the Sawchuk–Zaske method using peak and trough concentrations. Although the presence of malignancy influences the pharmacokinetic parameters of vancomycin, it is unclear whether cancer patients were included in the Japanese patient populations employed to estimate population pharmacokinetic parameters for this drug. The difference of predictive accuracy between the Sawchuk–Zaske and Bayesian methods in Japanese cancer patients is not completely understood. Objective To retrospectively compare the accuracy of predicting vancomycin concentrations between the Sawchuk–Zaske method and the Bayesian method in Japanese cancer patients. Methods Using data from 48 patients with various malignancies, the predictive accuracy (bias) and precision of the two methods were assessed by calculating the mean prediction error, the mean absolute prediction error, and the root mean squared prediction error. Results Prediction of the trough and peak vancomycin concentrations by the Sawchuk–Zaske method and the peak concentration by the Bayesian method showed a bias toward low values according to the mean prediction error. However, there were no significant differences between the two methods with regard to the changes of the mean prediction error, mean absolute prediction error, and root mean squared prediction error. Conclusion The Sawchuk–Zaske method and Bayesian method showed similar accuracy for predicting vancomycin concentrations in Japanese cancer patients.
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Gerber, Brandon S., James L. Tangler, Earl P. N. Duque, and J. David Kocurek. "Peak and Post-Peak Power Aerodynamics from Phase VI NASA Ames Wind Turbine Data." Journal of Solar Energy Engineering 127, no. 2 (April 25, 2005): 192–99. http://dx.doi.org/10.1115/1.1862260.

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Constant speed/pitch rotor operation lacks adequate theory for predicting peak and post-peak power. The objective of this study was to identify and quantify how measured blade element performance characteristics from the Phase VI NASA Ames 24m×36m80ft×120ft wind tunnel test of a two-bladed, tapered, twisted rotor relate to the prediction of peak and post-peak rotor power. The performance prediction code, NREL’s Lifting Surface Prescribed Wake code (LSWT), was used to study the flow physics along the blade. Airfoil lift and drag coefficients along the blade were derived using the predicted angle of attack distribution from LSWT and Phase VI measured normal and tangential force coefficients. Through successive iterations, the local lift and drag coefficients were modified until agreement was achieved between the predicted and Phase VI measured normal and tangential force coefficients along the blade. This agreement corresponded to an LSWT angle of attack distribution and modified airfoil data table that reflected the measured three-dimensional aerodynamics. This effort identified five aerodynamic events important to the prediction of peak and post-peak power. The most intriguing event was a rapid increase in drag that corresponds with the occurrence of peak power. This is not currently modeled in engineering performance prediction methods.
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Yang, Hyunje, Honggeun Lim, Haewon Moon, Qiwen Li, Sooyoun Nam, Byoungki Choi, and Hyung Tae Choi. "Identifying the Minimum Number of Flood Events for Reasonable Flood Peak Prediction of Ungauged Forested Catchments in South Korea." Forests 14, no. 6 (May 30, 2023): 1131. http://dx.doi.org/10.3390/f14061131.

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The severity and incidence of flash floods are increasing in forested regions, causing significant harm to residents and the environment. Consequently, accurate estimation of flood peaks is crucial. As conventional physically based prediction models reflect the traits of only a small number of areas, applying them in ungauged catchments is challenging. The interrelationship between catchment characteristics and flood features to estimate flood peaks in ungauged areas remains underexplored, and evaluation standards for the appropriate number of flood events to include during data collection to ensure effective flood peak prediction have not been established. Therefore, we developed a machine-learning predictive model for flood peaks in ungauged areas and determined the minimum number of flood events required for effective prediction. We employed rainfall-runoff data and catchment characteristics for estimating flood peaks. The applicability of the machine learning model for ungauged areas was confirmed by the high predictive performance. Even with the addition of rainfall-runoff data from ungauged areas, the predictive performance did not significantly improve when sufficient flood data were used as input data. This criterion could facilitate the determination of the minimum number of flood events for developing adequate flood peak predictive models.
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Keith, David, and Juan Moreno-Cruz. "Pitfalls of coal peak prediction." Nature 469, no. 7331 (January 2011): 472. http://dx.doi.org/10.1038/469472b.

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Mandoli, Giulia Elena, Federico Landra, Benedetta Chiantini, Carlotta Sciaccaluga, Maria Concetta Pastore, Marta Focardi, Luna Cavigli, et al. "Tricuspid Regurgitation Velocity and Mean Pressure Gradient for the Prediction of Pulmonary Hypertension According to the New Hemodynamic Definition." Diagnostics 13, no. 16 (August 8, 2023): 2619. http://dx.doi.org/10.3390/diagnostics13162619.

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Background: The hemodynamic definition of PH has recently been revised with unchanged threshold of peak tricuspid regurgitation velocity (TRV). The aim of this study was to evaluate the predictive accuracy of peak TRV for PH based on the new (>20 mmHg) and the old (>25 mmHg) cut-off value for mean pulmonary artery pressure (mPAP) and to compare it with the mean right ventricular–right atrial (RV–RA) pressure gradient. Methods: Patients with advanced heart failure were screened from 2016 to 2021. The exclusion criteria were absent right heart catheterization (RHC) results, chronic obstructive pulmonary disease, any septal defect, inadequate acoustic window or undetectable TR. The mean RV–RA gradient was calculated from the velocity–time integral of TR. Results: The study included 41 patients; 34 (82.9%) had mPAP > 20 mmHg and 24 (58.5%) had mPAP > 25 mmHg. The AUC for the prediction of PH with mPAP > 20 mmHg was 0.855 for peak TRV and mean RV–RA gradient was 0.811. AUC for the prediction of PH defined as mPAP > 25 mmHg for peak TRV was 0.860 and for mean RV–RA gradient was 0.830. A cutoff value of 2.4 m/s for peak TRV had 65% sensitivity and 100% positive predictive value for predicting PH according to the new definition. Conclusions: Peak TRV performed better than mean RV–RA pressure gradient in predicting PH irrespective of hemodynamic definitions. Peak TRV performed similarly with the two definitions of PH, but a lower cutoff value had higher sensitivity and equal positive predictive value for PH.
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Soroka, Juliana, Larry Grenkow, Héctor Cárcamo, Scott Meers, Shelley Barkley, and John Gavloski. "An assessment of degree-day models to predict the phenology of alfalfa weevil (Coleoptera: Curculionidae) on the Canadian Prairies." Canadian Entomologist 152, no. 1 (December 21, 2019): 110–29. http://dx.doi.org/10.4039/tce.2019.71.

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AbstractThis study examined the use of degree-day models to predict alfalfa weevil Hypera postica (Gyllenhal) (Coleoptera: Curculionidae) population development on the Canadian prairies. Air temperatures, alfalfa weevil abundance, and instar data were collected in 2013 and 2014 from 13 alfalfa (Medicago sativa Linnaeus; Fabaceae) fields across Alberta, Saskatchewan, and Manitoba. We coupled three alfalfa weevil population prediction models with three temperature data sources to determine which combination most closely aligned with results observed. Our objective was to find the best prediction of peak occurrence of second instar alfalfa weevils, the optimum time for management decisions. Of the parameters analysed, prediction model had the greatest effect on the accuracy of peak instar prediction, with Harcourt and North Dakota models better at predicting population peaks than the Guppy–Mukerji model. Interactions between temperature source and prediction model significantly affected prediction accuracy. The probability of accurate prediction of population peaks to within 3.5 days of actual occurrence using in-field and multiple-site temperature data sets, combined with Harcourt and North Dakota development models, was 0.45–0.70. Lower predictability was found from fields in the Mixed Grass Ecoregion than in other ecoregions. The use of the recommended models can assist growers in timing their monitoring activities and deciding if pest management action is warranted.
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Li, Haitao, Guo Yu, Yizhu Fang, Yanru Chen, Chenyu Wang, and Dongming Zhang. "Studies on natural gas reserves multi-cycle growth law in Sichuan Basin based on multi-peak identification and peak parameter prediction." Journal of Petroleum Exploration and Production Technology 11, no. 8 (June 18, 2021): 3239–53. http://dx.doi.org/10.1007/s13202-021-01212-3.

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AbstractResearch on predicting the growth trend of natural gas reserves will help provide theoretical guidance for natural gas exploration in Sichuan Basin. The growth trend of natural gas reserves in Sichuan Basin is multi-cycle and complex. The multi-cyclic peak is screened by the original multi-cyclic peak judgment standard. Metabolically modified GM(1,3) gray prediction method is used to predict the multi-cycle model parameters. The multi-cycle Hubbert model and Gauss model are used to predict the growth trend of natural gas reserves. The research results show that: (1) The number of cycles of natural gas reserves curve during 1956–2018 is 13. Natural gas reserves will maintain the trend of rapid growth in the short term. (2) Metabolism modified GM(1,3) gray prediction model can improve the accuracy of model prediction. The prediction accuracy of Hubbert model is higher than that of Gauss model. By 2030, the cumulative proven level of natural gas will reach 52.34%. The Sichuan Basin will reach its peak of proven lifetime reserves in the next few years.
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Zhang, Yang. "Peak Traffic Prediction Using Nonparametric Approaches." Advanced Materials Research 378-379 (October 2011): 196–99. http://dx.doi.org/10.4028/www.scientific.net/amr.378-379.196.

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How to accurately predict peak traffic is difficult for various forecasting models. In this paper, least squares support vector machines (LS-SVMs) are investigated to solve such a practical problem. It is the first time to apply the technique and analyze the forecast performance in the domain. For comparison purpose, other two non-parametric predictors are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals that the approach is practically promising.
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Dissertations / Theses on the topic "Peak prediction"

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Al-Rahamneh, Harran Qoblan Mefleh. "Perceived exertion relationships and prediction of peak oxygen uptake in able-bodied and paraplegic individuals." Thesis, University of Exeter, 2010. http://hdl.handle.net/10036/3005.

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Rating of Perceived Exertion (RPE) relates to how ‘hard’ or ‘easy’ an exercise feels. The Borg 6-20 RPE scale is the most widely used scale to estimate the overall, peripheral and central perception of effort. To date, there are a limited number of studies on the use and efficacy of perceived exertion in persons with spinal cord injury and/or disease. The findings from these studies are also equivocal. Therefore, the aims of this thesis were to assess: i) the relationship between the RPE and physical and physiological markers of exercise intensity during arm cranking exercise in able-bodied and individuals with spinal cord disease, ii) the efficacy of sub-maximal RPE values to predict peak oxygen uptake during arm cranking exercise in able-bodied and paraplegic individuals using different exercise protocols, iii) the scalar property of the RPE during arm cranking exercise in able-bodied and paraplegic individuals. To achieve these goals, the thesis has been broken down to a series of seven studies. In each of these studies, except study 6, a group of able-bodied and a group of paraplegic participants were recruited to asses these hypotheses. Paraplegic individuals had spinal cord injury with neurological levels at or below the sixth thoracic vertebra (T6) or flaccid paralysis as a result of poliomyelitis infection. These individuals were physically active and participated in sports like wheelchair basketball, weightlifting, wheelchair racing and table tennis at both professional and recreational levels. Able-bodied participants were healthy and free from pre-existing injuries and physically active but not arm-trained. There were strong relationships between the RPE and each of the physiological and physical indices of exercise intensity during arm cranking exercise regardless of group or gender. Peak oxygen uptake can be predicted with reasonable accuracy from sub-maximal oxygen uptake values elicited during a sub-maximal perceptually-guided, graded exercise test for paraplegic individuals but not for able-bodied participants. It has also been shown that peak oxygen uptake can be predicted from power output using the equation prescribed by the American College of Sports Medicine (ACSM, 2006). Furthermore, for able-bodied participants using estimation procedures, a passive process in which an individual is asked to rate how ‘hard’ or ‘easy’ an exercise feels, the ramp exercise test provided more accurate prediction of peak oxygen uptake compared to the graded exercise test. For paraplegic persons using estimation procedures, the graded exercise test provided more accurate prediction of peak oxygen uptake compared to the ramp exercise test. Finally, the scalar property of the RPE (i.e., similar proportions of time at a given RPE) was evident during arm cranking exercise regardless of group. In conclusion, the prediction of peak oxygen uptake from sub-maximal exercise tests would provide a safer environment of exercise testing. In addition, using a sub-maximal protocol would make peak oxygen uptake more available for sedentary and clinical population compared to the graded exercise test to volitional exhaustion. Prediction of peak oxygen uptake from power output using the ACSM equation would make the estimation of peak oxygen uptake more available for large groups of people. Similar proportions of time were observed at a given RPE regardless of group or exercise intensity. The early RPE responses will give an indicator for how long a participant is going to exercise. This has important implications for rehabilitation settings. Based on the RPE responses the tester or the observer can increase or decrease the work rate to enable the participant to exercise for the desired duration.
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Kiuchi, Ryota. "New Ground Motion Prediction Equations for Saudi Arabia and their Application to Probabilistic Seismic Hazard Analysis." Kyoto University, 2020. http://hdl.handle.net/2433/253095.

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Thornton, Craig Matthew. "Effects of Land Development on Peak Runoff Rate and its Prediction for Brigalow Catchments in Central Queensland, Australia." Thesis, Griffith University, 2012. http://hdl.handle.net/10072/365709.

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The estimation of runoff volume and peak runoff rate has been the focus of significant hydrological research worldwide. The results of these studies, usually in the form of empirical relationships or models, are intrinsically linked to the environment in which the study was conducted. This often limits the applicability and accuracy of the method of runoff estimation at alternative and ungauged locations. Within the brigalow belt of central Queensland, Australia, a scarcity of stream gauging stations to measure runoff volume and peak runoff rate has impeded research on the surface water hydrology of the region. Intermittent failure of these stations and consequently, multiple periods of missing data, have added further complexity and challenge to the understanding of catchment hydrology in the region. Commencing in 1965 and continuing today, the Brigalow Catchment Study in central Queensland has measured both runoff volume and peak runoff rate from three small catchments which initially contained native brigalow scrub. The natural hydrology of the three catchments was characterised during a 17-year calibration period from 1965 to 1981. In 1982, two of the three catchments were cleared, with one developed for cropping and one developed for improved pasture, while the third was retained as an uncleared control catchment. Study of the effect of land development on surface hydrology commenced in 1984. Twenty-one years of record was used to quantify the changes in peak runoff rate associated with land development. Results however, were confounded by missing data. To allow for robust analysis, estimates of missing data were generated via three different methods: (1) multiple variable regression analyses; (2) Soil Conservation Service curve number and graphical peak discharge methodologies; and (3) a simple variable infiltration rate model. The suitability of each technique for the estimation of peak runoff rate was assessed using both graphical and numerical evaluation.
Thesis (Masters)
Master of Philosophy (MPhil)
Griffith School of Engineering
Science, Environment, Engineering and Technology
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Birch, Wiliiam John. "The prediction of peak particle velocity vibration levels in underground structures that arise as the result of surface blasting." Thesis, University of Leeds, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.659028.

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The author of this thesis has been involved in research into the environmental impact of blasting for over 34 years, initially as a founder member and then more recently as the director of the Blasting and Environmental Research Group [BERG]. BERG was originally established at the Department of Mining and Mineral Engineering at the University of Leeds and has a long history of research into the environmental impacts of blasting from quarries and opencast mines. This thesis is concerned with the prediction of peak particle velocity vibration levels in underground structures that arise as a result of surface blasting. It does this by examining two specific case studies at Taffs Wells and Whitwell Quarries in the wider context of the environmental impact of blasting. The initial sections are concerned with the fundamentals of surface blasting, the physics of blast vibrations, a brief history of blasting research in terms of environmental impact and a literature survey of previous case studies related to blast damage levels in underground structures.
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Wang, Zijian. "DM EMI Noise Analysis for Single Channel and Interleaved Boost PFC in Critical Conduction Mode." Thesis, Virginia Tech, 2010. http://hdl.handle.net/10919/32719.

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The critical conduction mode (CRM) power factor correction converters (PFC) are widely used in industry for low power offline switching mode power supplies. For the CRM PFC, the main advantage is to reduce turn-on loss of the main switch. However, the large inductor current ripple in CRM PFC creates huge DM EMI noise, which requires a big EMI filter. The switching frequency of the CRM PFC is variable in half line cycle which makes the EMI characteristics of the CRM PFC are not clear and have not been carefully investigated. The worst case of the EMI noise, which is the baseline to design the EMI filter, is difficult to be identified. In this paper, an approximate mathematical EMI noise model based on the investigation of the principle of the quasi-peak detection is proposed to predict the DM EMI noise of the CRM PFC. The developed prediction method is verified by measurement results and the predicted DM EMI noise is good to evaluate the EMI performance. Based on the noise prediction, the worst case analysis of the DM EMI noise in the CRM PFC is applied and the worst case can be found at some line and load condition, which will be a great help to the EMI filter design and meanwhile leave an opportunity for the optimization of the whole converter design. What is more, the worst case analysis can be extended to 2-channel interleaved CRM PFC and some interesting characteristics can be observed. For example, the great EMI performance improvement through ripple current cancellation in traditional constant frequency PFC by using interleaving techniques will not directly apply to the CRM PFC due to its variable switching frequency. More research needs to be done to abstract some design criteria for the boost inductor and EMI filter in the interleaved CRM PFC.
Master of Science
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Akeil, Salah. "Comparative Study On Ground Vibrations Prediction By Statistical And Neural Networks Approaches At Tuncbilek Coal Mine, Panel Byh." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605058/index.pdf.

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In this thesis, ground vibrations induced by bench blasting from the Tunç
bilek Coal Mine, Panel BYH, were measured to find out the site-specific attenuation and to assess the structural damage risk. A statistical approach is applied to the collected data, and from the data analysis an attenuation relationship is established to be used in predicting the peak particle velocity as well as to calculate the maximum allowable charge per delay. The values of frequencies are also analyzed to investigate the damage potential to the structures of Tunç
bilek Township. A new approach to predict the peak particle velocity is also proposed in this research study. A neural network technique from the branch of the artificial intelligence is put forward as an alternative approach to the statistical technique. Findings of this study indicate, according to USBM (1980) criteria, that there is no damage risk to the structures in Tunç
bilek Township induced by bench blasting performed at Tunç
bilek coal mine, Panel BYH. Therefore, it is concluded that the damage claims put forward by the inhabitants of Tunç
bilek township had no scientific bases. It is also concluded that the empirical statistical technique is not the only acceptable approach that can be taken into account in predicting the peak particle velocity. An alternative and interesting neural network approach can also give a satisfactory accuracy in predicting peak particle velocity when compared to a set of additional recorded data of PPV.
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Goutham, Mithun. "Machine learning based user activity prediction for smart homes." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595493258565743.

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Chen, Yuyao. "Contribution of machine learning to the prediction of building energy consumption." Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0119.

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La prédiction de la consommation énergétique des bâtiments est aujourd’hui un point-clé de la transition énergétique, qui vise à atténuer les effets du réchauffement climatique. La quantité de données disponible étant de plus en plus importante, les modèles prédictifs dits « data-driven » voient leur performances croître constamment. Parmi ces modèles, ceux issus du Machine Learning sont probablement les plus efficaces. Au sein de cette thèse, nous proposons de réaliser un état de l’art complet des contributions du Machine Learning à la la prédiction de la consommation énergétique des bâtiments, via deux axes principaux : l’étape de pré-traitement des données préalable à l’utilisation d’un modèle de Machine Learning et les modèles en tant que tels, jusqu’aux plus complexes qui sont les réseaux neuronaux profonds. Malgré les performances de ces derniers, prédire avec précision la consommation d’électricité lorsque de fortes variations dans le temps apparaissent reste un défi. Pour le relever, nous proposons d’explorer deux voies : l’utilisation de fonctions de pertes soft-DTW et l’ajout de variables exogènes. L’application, à des données réelles, d’un réseau neuronal résiduel LSTM avec la fonction de perte soft-DTW, mène à une amélioration significative via notamment une meilleure compréhension des évolutions associées aux séries temporelles étudiées, en particulier pour les pics. Cependant, les métriques d’erreur classiques se révèlent insuffisantes pour évaluer et valider ou non le modèle prédictif. Nous introduisons donc une analyse de matrice de confusion et deux nouvelles métriques d’erreur : l’erreur de localisation temporelle du pic et l’erreur d’amplitude du pic basée sur l’algorithme DTW. Nos résultats révèlent que le soft-DTW surpasse les fonctions de perte MSE et MAE avec une réduction des erreurs associées à ces métriques, menant à une meilleure précision du modèle global. Afin de pénaliser la fonction de perte soft-DTW, un terme additionnel est introduit parmi l’erreur MSE, l’erreur MAE et l’indice de distorsion temporelle. Les résultats montrent que la pénalité MSE est la plus efficace pour réduire les problèmes de sur-estimation des pics et la réduction de l’effet dit « aigu » de ces pics. Concernant les variables exogènes, leur ajout combiné à la fonction de perte soft-DTW peut améliorer de façon significative notre modèle de prédiction : ainsi, les variables dites calendaires (temporelles) améliorent généralement la performance, en particulier si leur corrélation de Pearson avec la variable cible est importante. Cependant, si cette corrélation est relativement faible, l’inclusion de variables calendaires a un effet négatif sur la performance du modèle. Une conclusion similaire a été faite pour les variables météorologiques
The ongoing energy transition, pivotal to mitigate global warming, could significantly benefit from advances in building energy consumption prediction. With the advent of big data, data-driven models are increasingly effective in forecasting tasks and machine learning is probably the most efficient method to build such predictive models nowadays. In this work, we provide a comprehensive review of machine learning techniques for forecasting, regarding preprocessing as well as state-of-the-art models such as deep neural networks. Despite the achievements of state-of-art models, accurately predicting high-fluctuation electricity consumption still remains a challenge. To tackle this challenge, we propose to explore two paths: the utilization of soft-DTW loss functions and the inclusion of exogenous variables. By applying the soft-DTW loss function with a residual LSTM neural network on a real dataset, we observed significant improvements in capturing the patterns of high-fluctuation load series, especially in peak prediction. However, conventional error metrics prove insufficient in adequately measuring this ability. We therefore introduce confusion matrix analysis and two new error metrics: peak position error and peak load error based on the DTW algorithm. Our findings reveal that soft-DTW outperforms MSE and MAE loss functions with lower peak position and peak load error. We also incorporate soft-DTW loss function with MSE, MAE, and Time Distortion Index. The results show that combining the MSE loss function performs the best and helps alleviate the problem of overestimated and sharp peaks problems occured. By adding exogenous variables with soft-DTW loss functions, the inclusion of calendar variables generally enhances the model’s performance, particularly when these variables exhibit higher Pearson’s correlation coefficients with the target variable. However, when the correlation between the calendar variables and the historical load patterns is relatively low, their inclusion has a negative impact on the model’s performance. A similar relationship is observed with weather variables
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Hiesböcková, Tereza. "Předpovídání povodňových průtoků v měrných profilech Borovnice - Dalečín." Master's thesis, Vysoké učení technické v Brně. Fakulta stavební, 2012. http://www.nusl.cz/ntk/nusl-225458.

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Aim of a work is construction of forecasting models for prediction of flood flows of measuring profile Borovnice – Dalečín on the river Svratka. As a tool for issuing predictions will be used classic hydrological forecasting models, and models based on artificial intelligence methods. Predictive model will be consisting from summer flood flows for the years 1997-2007. In the end of the work will chosen a better method for issuing forecasts
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Preisler, Frederik. "Predicting peak flows for urbanising catchments." Thesis, Queensland University of Technology, 1992.

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Books on the topic "Peak prediction"

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Campeau, Gail Annette. Prediction of shotcrete damage through the analysis of peak particle velocity. Sudbury, Ont: Laurentian University, School of Engineering, 1999.

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Future scenarios: How communities can adapt to peak oil and climate change. Totnes: Green, 2009.

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S, Rohatgi Upendra, and U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research. Division of Systems Research., eds. Bias in peak clad temperature predictions due to uncertainties in modeling of ECC bypass and dissolved non-condensable gas phenomena. Washington, DC: Division of Systems Research, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1990.

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Holmgren, David. Future Scenarios: How Communities Can Adapt to Peak Oil and Climate Change. Chelsea Green Publishing, 2012.

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Prediction of peak VO ́values from 9-minute run distances in young males, 9-14 years. 1985.

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Prediction of peak VO2ș values from 9-minute run distances in young males, 9-14 years. 1985.

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Lynch, Michael C. The “Peak Oil” Scare and the Coming Oil Flood. ABC-CLIO, LLC, 2016. http://dx.doi.org/10.5040/9798400605017.

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Is the earth’s oil supply starting to run out, or is there far more oil than some experts believe? This book points out flaws in the research used to warn of an oil shortfall and predicts that large new reserves of oil are soon to be tapped. In the last decade, oil experts, geologists, and policy makers alike have warned that a peak in oil production around the world was about to be reached and that global economic distress would result when this occurred. But it didn’t happen. The "Peak Oil" Scare and the Coming Oil Flood refutes the recent claims that world oil production is nearing a peak and threatening economic disaster by analyzing the methods used by the theory’s proponents. Author Michael C. Lynch, former researcher at Massachusetts Institute of Technology (MIT), debunks the "Peak Oil" crisis prediction and describes how the next few years will instead see large amounts of new supply that will bring oil prices down and boost the global economy. This book will be invaluable to those involved in the energy industry, including among those fields that are competing with oil, as well as financial institutions for which the price of oil is of critical importance. Lynch uncovers the facts behind the misleading news stories and media coverage on oil production as well as the analytic process that reveals the truth about the global oil supply. General readers will be dismayed to learn how governments have frequently been led astray by seeming logical theories that prove to have no sound basis and will come away with a healthy sense of skepticism about popular economics.
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Comparison of a prediction of maximal oxygen consumption by the YMCA Submaximal Bicycle Ergometer Test to a measurement of peak oxygen consumption. 1987.

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Comparison of a prediction of maximal oxygen consumption by the YMCA Submaximal Bicycle Ergometer Test to a measurement of peak oxygen consumption. 1985.

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Peak oxygen deficit as a predictor of sprint and middle-distance track performance. 1992.

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Book chapters on the topic "Peak prediction"

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Wu, Wenjie, Heping Jin, Gan Wang, Yihan Li, Wanru Zeng, Feng Liu, Huiheng Luo, and Tao Liang. "Research on Wind Power Peak Prediction Method." In Lecture Notes in Electrical Engineering, 643–51. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1068-3_66.

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Scherbart, Alexandra, Wiebke Timm, Sebastian Böcker, and Tim W. Nattkemper. "Improved Mass Spectrometry Peak Intensity Prediction by Adaptive Feature Weighting." In Advances in Neuro-Information Processing, 513–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02490-0_63.

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Goodwin, Morten, and Anis Yazidi. "A Pattern Recognition Approach for Peak Prediction of Electrical Consumption." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 265–75. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-662-44654-6_26.

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Xue, Weixian, and Liangmin Wang. "Prediction of Carbon Peak in Shaanxi Province and Its Cities." In Atlantis Highlights in Intelligent Systems, 938–45. Dordrecht: Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-200-2_97.

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Kanwar, Neeraj, Divay Bargoti, and Vinay Kumar Jadoun. "Power Transformer Summer Peak Load Prediction Using SCADA and Supervised Learning." In Lecture Notes in Electrical Engineering, 215–21. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1476-7_21.

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Li, Yajing, Jieren Cheng, Yuqing Kou, Dongwan Xia, and Victor S. Sheng. "Prediction of Passenger Flow During Peak Hours Based on Deep Learning." In Smart Innovation, Systems and Technologies, 213–28. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7161-9_17.

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Sun, Yanli, Di Zhang, and Qiang Liu. "Prediction of peak carbon emission in Liaoning Province based on energy consumption." In Advances in Urban Engineering and Management Science Volume 2, 435–41. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003345329-57.

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Mahmud, Khizir, Weilun Peng, Sayidul Morsalin, and Jayashri Ravishankar. "A Day-Ahead Power Demand Prediction for Distribution-Side Peak Load Management." In Proceedings of International Joint Conference on Computational Intelligence, 305–15. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7564-4_27.

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Peteuil, Christophe, Simon Carladous, and Nicolle Mathys. "Peak Discharge Prediction in Torrential Catchments of the French Pyrenees: The ANETO Method." In Management of Mountain Watersheds, 93–110. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-2476-1_8.

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Rovelli, Antonio. "Strong Ground Motions in Italy: Accelerogram Spectral Properties and Prediction of Peak Values." In Strong Ground Motion Seismology, 333–54. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-017-3095-2_11.

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Conference papers on the topic "Peak prediction"

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Singh, Rayman Preet, Peter Xiang Gao, and Daniel J. Lizotte. "On hourly home peak load prediction." In 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2012. http://dx.doi.org/10.1109/smartgridcomm.2012.6485977.

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Khafaf, Nameer Al, and Ayman H. El-Hag. "Prediction of leakage current peak value." In 2018 11th International Symposium on Mechatronics and its Applications (ISMA). IEEE, 2018. http://dx.doi.org/10.1109/isma.2018.8330118.

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Weiss, M. A., A. Masarie, and R. Beard. "Peak Deviation from Prediction in Atomic Clocks." In 2007 IEEE International Frequency Control Symposium Joint with the 21st European Frequency and Time Forum. IEEE, 2007. http://dx.doi.org/10.1109/freq.2007.4319231.

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Liu, Jinxiang, and Laura E. Brown. "Prediction of Hour of Coincident Daily Peak Load." In 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE, 2019. http://dx.doi.org/10.1109/isgt.2019.8791587.

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Ai, Songpu, Antorweep Chakravorty, and Chunming Rong. "Evolutionary Ensemble LSTM based Household Peak Demand Prediction." In 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2019. http://dx.doi.org/10.1109/icaiic.2019.8668971.

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Liu, Jianlei, and Kurt J. Marfurt. "Thin bed thickness prediction using peak instantaneous frequency." In SEG Technical Program Expanded Abstracts 2006. Society of Exploration Geophysicists, 2006. http://dx.doi.org/10.1190/1.2370418.

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Ma, Yingwen, and Li Zhou. "Real-time flutter boundary prediction using peak-hold method." In Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XII, edited by Peter J. Shull. SPIE, 2018. http://dx.doi.org/10.1117/12.2301241.

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Tieleman, H. W., M. A. K. Elsayed, and M. R. Hajj. "Prediction of Peak Wind Loads on Low Rise Structures." In Solutions to Coastal Disasters Conference 2005. Reston, VA: American Society of Civil Engineers, 2005. http://dx.doi.org/10.1061/40774(176)49.

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Chiodo, E., and D. Lauria. "Probabilistic description and prediction of electric peak power demand." In 2012 Electrical Systems for Aircraft, Railway and Ship Propulsion (ESARS). IEEE, 2012. http://dx.doi.org/10.1109/esars.2012.6387418.

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Islam, Shafiqul, James Leech, Charles C. Y. Lin, and Lukas Chrostowski. "Peak Blood Glucose Prediction Algorithm Following a Meal Intake." In 2007 Canadian Conference on Electrical and Computer Engineering. IEEE, 2007. http://dx.doi.org/10.1109/ccece.2007.149.

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Reports on the topic "Peak prediction"

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Ronstadt, Jackie A. Post-Wildfire Peak Discharge Prediction Methods in Northern New Mexico. Office of Scientific and Technical Information (OSTI), December 2017. http://dx.doi.org/10.2172/1414163.

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Si, Hongjun, Saburoh Midorikawa, and Tadahiro Kishida. Development of NGA-Sub Ground-Motion Model of 5%-Damped Pseudo-Spectral Acceleration Based on Database for Subduction Earthquakes in Japan. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, December 2020. http://dx.doi.org/10.55461/lien3652.

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Presented within is an empirical ground-motion model (GMM) for subduction-zone earthquakesin Japan. The model is based on the extensive and comprehensive subduction database of Japanese earthquakes by the Pacific Engineering Research Center (PEER). It considers RotD50 horizontal components of peak ground acceleration (PGA), peak ground velocity (PGV), and 5%-damped elastic pseudo-absolute acceleration response spectral ordinates (PSA) at the selected periods ranging from 0.01 to 10 sec. The model includes terms and predictor variables considering tectonic setting (i.e., interplate and intraslab), hypocentral depths (D), magnitude scaling, distance attenuation, and site response. The magnitude scaling derived in this study is well constrained by the data observed during the large-magnitude interface events in Japan (i.e., the 2003 Tokachi-Oki and 2011 Tohoku earthquakes) for different periods. The developed ground-motion prediction equation (GMPE) covers subduction-zone earthquakes that have occurred in Japan for magnitudes ranging from 5.5 to as large as 9.1, with distances less than 300 km from the source.
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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, April 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington Metropolitan Area Transit Authority (WMATA) bus routes operating in Washington, DC. We developed regression models and Artificial Neural Network (ANN) models for predicting travel times of buses for different peak periods (AM, Mid-Day and PM). Our analysis included variables such as number of served bus stops, length of route between bus stops, average number of passengers in the bus, average dwell time of buses, and number of intersections between bus stops. We obtained ANN models for travel times by using approximation technique incorporating two separate algorithms: Quasi-Newton and Levenberg-Marquardt. The training strategy for neural network models involved feed forward and errorback processes that minimized the generated errors. We also evaluated the models with a Comparison of the Normalized Squared Errors (NSE). From the results, we observed that the travel times of buses and the dwell times at bus stops generally increased over time of the day. We gathered travel time equations for buses for the AM, Mid-Day and PM Peaks. The lowest NSE for the AM, Mid-Day and PM Peak periods corresponded to training processes using Quasi-Newton algorithm, which had 3, 2 and 5 perceptron layers, respectively. These prediction models could be adapted by transit agencies to provide the patrons with accurate travel time information at bus stops or online.
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Wells, Beric, Scott Cooley, and Joseph Meacham. Prediction of Peak Hydrogen Concentrations for Deep Sludge Retrieval in Tanks AN-101 and AN-106 from Historical Data of Spontaneous Gas Release Events. Office of Scientific and Technical Information (OSTI), October 2013. http://dx.doi.org/10.2172/1148634.

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Linker, Taylor, and Timothy Jacobs. PR-457-18204-R01 Variable Fuel Effects on Legacy Compressor Engines Phase IV - Predictive NOx Modeling. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), May 2019. http://dx.doi.org/10.55274/r0011584.

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The ultimate goal of this work is to improve the current control methods for large bore, lean burn natural gas engines in order to combat performance and emissions issues during variable fuel composition events. This will be achieved in the long term by simulating the effects of variable fuel composition on a large bore, natural gas engine and developing engine control strategies which work to mitigate adverse effects. The work of Phase IV adds onto previous work by enabling the prediction of NOxemissions in the validated, full-scale engine simulation of a Cooper-Bessemer GMWH-10C developed in Phase III. A sweep of fuel composition was also performed to assess the effect that variable fuel composition has on in-cylinder properties and NOxemissions. Engine-out NOxwas predicted via a chemical kinetic mechanism which was implemented into the existing engine simulation. The mechanism dictates the composition of combustion products in each cylinder, including NO and NO2(NOx). NOxlevels were measured at the simulation exhaust to compare with the experimental NOxdata acquired as part of the data collection carried out in Phase III of this project. The prediction was tuned in order to achieve the closest prediction to real measured NOxvalues. A preliminary sweep of fuel composition was completed by varying the mole fractions of ethane and propane within the natural gas compositions used in the simulation. Changes in in-cylinder pressure, location of peak pressure, in-cylinder temperature, and engine-out NOxwere evaluated based on their trend-wise behavior and compared qualitatively to expected results.
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Huang, Tao, and Venkatesh Merwade. Developing Customized NRCS Unit Hydrographs (Finley UHs) for Ungauged Watersheds in Indiana. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317644.

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The Natural Resources Conservation Service (NRCS, formerly the Soil Conservation Service, SCS) unit hydrograph (UH) is one of the most commonly used synthetic UH methods for hydrologic modeling and engineering design all over the world. However, previous studies have shown that the application of the NRCS UH method for some ungauged watersheds in the state of Indiana produced unrealistic flood predictions for both the peak discharge and the time to peak. The objective of this work is to customize the NRCS UH by analyzing the role of its two key parameters, namely, the peak rate factor (PRF) and the lag time, in creating the runoff hydrograph. Based on 120 rainfall-runoff events collected from 30 small watersheds in Indiana over the past two decades, the observed UHs are derived and the corresponding PRF and lag time are extracted. The observed UHs in Indiana show that the mean value of PRF is 371, which is lower than the standard PRF of 484, and the NRCS lag time equation tends to underestimate the “true” lag time. Moreover, a multiple linear regression method, especially the stepwise selection technique, is employed to relate the NRCS UH parameters to the most appropriate geomorphic attributes extracted from the study watersheds. Both the statewide and regional regression models show that the main channel slope is a major factor in determining the PRF and lag time. A customized Indiana unit hydrograph, referred as Finley UH to honor David Finley who inspired this study, is derived with updated parameters and the Gamma function. Validation results show that the Finley UH provides more reliable and accurate predictions in terms of the peak discharge and the time to peak than the original NRCS UH for the watersheds in Indiana.
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Stewart, Charles W., Stacey A. Hartley, Perry A. Meyer, and Beric E. Wells. Predicting Peak Hydrogen Concentrations from Spontaneous Gas Releases in Hanford Waste Tanks. Office of Scientific and Technical Information (OSTI), July 2005. http://dx.doi.org/10.2172/15016741.

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Derdzinski, Pat, Dale Thoreson, and Larry Tranel. NE Iowa’s Experience with Predictive Equation for Alfalfa Quality (PEAQ). Ames (Iowa): Iowa State University, January 2008. http://dx.doi.org/10.31274/ans_air-180814-887.

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Dinovitzer. L52243 Modeling of Delayed Hydrogen Cracking for In-Service Welds. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 2005. http://dx.doi.org/10.55274/r0010916.

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The objective of this research project was to demonstrate and develop a means of predicting the inspection delay time such that hydrogen cracking would not occur after inspection for in-service welds. Hydrogen cracking is an issue for in-service welding since this form of cracking may occur a significant time after the weldment has cooled to ambient temperatures. Hydrogen cracking has three necessary conditions including a tensile stress, the presence of hydrogen and susceptible microstructure. After weld cooling the microstructure is stable and it was assumed that the local weld stress state is constant, therefore, the potential for cracking is only affected by the change in local hydrogen concentration. It was asserted that after the peak local hydrogen concentration is reached the risk of cracking is over. The current project described the theory, validation and use of the BMT Hydrogen Diffusion and Cracking model. The results of this project was the demonstration of peak hydrogen delay time trends with in-service welding conditions and the development of formulae that estimate the time to peak hydrogen in a weldment as a surrogate for the time to hydrogen cracking. The results of this project provide information useful in establishing in-service weld inspection delay times for fillet weld applications.
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Glover, Austin, and Dusty Brooks. Comparison of Side-on Peak Overpressure Predictions and Measurements for Type IV Composite Overwrapped Pressure Vessel Catastrophic Failure. Office of Scientific and Technical Information (OSTI), January 2023. http://dx.doi.org/10.2172/2004890.

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