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Literatura académica sobre el tema "Peak prediction"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 19 de julio de 2025

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Artículos de revistas sobre el tema "Peak prediction"

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 (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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Keith, David, and Juan Moreno-Cruz. "Pitfalls of coal peak prediction." Nature 469, no. 7331 (2011): 472. http://dx.doi.org/10.1038/469472b.

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Tangka, George Morris William, and Lidya Chitra Laoh. "Deep Learning for Peak Load Duration Curve Forecasting." CogITo Smart Journal 10, no. 1 (2024): 603–12. http://dx.doi.org/10.31154/cogito.v10i1.694.603-612.

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As the energy landscape changes towards renewable energy sources and smart grid technologies, accurate prediction of peak load duration curve (PLDC) becomes crucial to ensure power system stability. The background to this research is the urgent need for more effective prediction methods to manage increasingly complex energy loads. This research presents a leading-edge approach to PLDC prediction, leveraging Deep Learning, a subsection of artificial intelligence. Focusing on data from the Taiwan State Electric Company, this study uses a Long Short-Term Memory (LSTM) network to capture complex load patterns. The LSTM model, consisting of two layers and trained on 2019-2020 data, demonstrated excellent accuracy with a Mean Absolute Percentage Error (MAPE) as low as 0.03%. These results confirm the potential of Deep Learning to revolutionize PLDC predictions in complex energy systems. These research recommendations involve exploring diverse datasets, integrating real-time data streams, and conducting comparative analyses for more reliable prediction methodologies. The benefits of this research include providing relevant insights for sustainable energy resource management amidst a dynamic energy landscape.

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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 (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 (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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Zhao, Mengchen, Santiago Gomez-Rosero, Hooman Nouraei, Craig Zych, Miriam A. M. Capretz, and Ayan Sadhu. "Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning." Energies 17, no. 7 (2024): 1672. http://dx.doi.org/10.3390/en17071672.

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Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the implementation of proactive peak-shaving strategies, the effective scheduling of battery response, and an enhancement of smart grid management. The current research on peak demand for commercial buildings has shown a gap in addressing timestamps for peak consumption incidents. To bridge the gap, an Energy Peaks and Timestamping Prediction (EPTP) framework is proposed to not only identify the energy peaks, but to also accurately predict the timestamps associated with their occurrences. In this EPTP framework, energy consumption prediction is performed with a long short-term memory network followed by the timestamp prediction using a multilayer perceptron network. The proposed framework was validated through experiments utilizing real-world commercial supermarket data. This evaluation was performed in comparison to the commonly used block maxima approach for indexing. The 2-h hit rate saw an improvement from 21% when employing the block maxima approach to 52.6% with the proposed EPTP framework for the hourly resolution. Similarly, the hit rate increased from 65.3% to 86% for the 15-min resolution. In addition, the average minute deviation decreased from 120 min with the block maxima approach to 62 min with the proposed EPTP framework with high-resolution data. The framework demonstrates satisfactory results when applied to high-resolution data obtained from real-world commercial supermarket energy consumption.

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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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Duncan, Michael J., Joanne Hankey, Mark Lyons, Rob S. James, and Alan M. Nevill. "Peak Power Prediction in Junior Basketballers." Journal of Strength and Conditioning Research 27, no. 3 (2013): 597–603. http://dx.doi.org/10.1519/jsc.0b013e31825d97ac.

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Ramesh, S., Bhaskar Natarajan, and Gopika Bhagat. "Peak load prediction using weather variables." Energy 13, no. 8 (1988): 671–79. http://dx.doi.org/10.1016/0360-5442(88)90097-7.

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Kim, Seunghawk, Gwangseob Kim, and Kyeong-Eun Lee. "Rainfall peak prediction using deep learning." Journal of the Korean Data And Information Science Society 34, no. 4 (2023): 607–17. http://dx.doi.org/10.7465/jkdi.2023.34.4.607.

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Tesis sobre el tema "Peak prediction"

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 Full Text

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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 Tun&ccedil 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 Tun&ccedil 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 Tun&ccedil bilek Township induced by bench blasting performed at Tun&ccedil bilek coal mine, Panel BYH. Therefore, it is concluded that the damage claims put forward by the inhabitants of Tun&ccedil 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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Libros sobre el tema "Peak prediction"

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

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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. 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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Orlov, Dmitry. Reinventing Collapse: The Soviet Experience and American Prospects. New Society Publishers, Limited, 2011.

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Capítulos de libros sobre el tema "Peak prediction"

Wu, Wenjie, Heping Jin, Gan Wang, et al. "Research on Wind Power Peak Prediction Method." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1068-3_66.

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Zhang, Peng, Junbo Mu, and Jie Luo. "Analysis of Carbon Emission Impact Factors and Trend Prediction Based on LMDI and ARIMA Models: A Case Study of Zhejiang Province." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8401-1_13.

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AbstractThe present study proposed a method to examine the carbon emissions of various departments in Zhejiang Province from 2003 to 2020 using the IPCC sectoral method. The use of the LMDI model analyzed the factors that influence carbon emission change in Zhejiang Province. The ARIMA prediction model and grey prediction model are utilized to forecast carbon emissions of Zhejiang Province in the future. The proposed measures for carbon emission reduction in Zhejiang Province are given, and some reference basis is provided for similar provinces to carry out low-carbon transformation. The results demonstrated that: (1) The carbon emission of Zhejiang Province from 2003 to 2020 shows a linear increase trend, with a growth rate of 172% during the 18 years. (2) The energy structure of Zhejiang Province is developing towards energy cleanliness. (3) Energy intensity and industrial structure are inhibiting effects, economic output and population size are promoting effects, and energy structure has both inhibiting and promoting times. (4) ARIMA’s prediction of carbon emissions in Zhejiang Province in the next few years is more accurate than that of the grey prediction model. The prediction results of ARIMA show that Zhejiang Province will usher in the carbon peak in 2025, while the grey prediction results show that it will not usher in the carbon peak before 2027.

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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. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02490-0_63.

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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. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-200-2_97.

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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. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-662-44654-6_26.

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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. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7161-9_17.

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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. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1476-7_21.

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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. 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. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7564-4_27.

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Bertipaglia, Alberto, Mohsen Alirezaei, Riender Happee, and Barys Shyrokau. "A Learning-Based Model Predictive Contouring Control for Vehicle Evasive Manoeuvres." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_89.

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AbstractThis paper presents a novel Learning-based Model Predictive Contouring Control (L-MPCC) algorithm for evasive manoeuvres at the limit of handling. The algorithm uses the Student-t Process (STP) to minimise model mismatches and uncertainties online. The proposed STP captures the mismatches between the prediction model and the measured lateral tyre forces and yaw rate. The mismatches correspond to the posterior means provided to the prediction model to improve its accuracy. Simultaneously, the posterior covariances are propagated to the vehicle lateral velocity and yaw rate along the prediction horizon. The STP posterior covariance directly depends on the variance of observed data, so its variance is more significant when the online measurements differ from the recorded ones in the training set and smaller in the opposite case. Thus, these covariances can be utilised in the L-MPCC’s cost function to minimise the vehicle state uncertainties. In a high-fidelity simulation environment, we demonstrate that the proposed L-MPCC can successfully avoid obstacles, keeping the vehicle stable while driving a double lane change manoeuvre at a higher velocity than an MPCC without STP. Furthermore, the proposed controller yields a significantly lower peak sideslip angle, improving the vehicle’s manoeuvrability compared to an L-MPCC with a Gaussian Process.

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Actas de conferencias sobre el tema "Peak prediction"

Kruger, Rodrigo, Abdullah Mueen, and Vinicius M. A. Souza. "Peak Prediction in Time Series: Comparing Approaches for Energy High-Load Prediction." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651140.

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Jara, Sebastían, Rodrigo Salas, Ricardo Ñanculef, Israel Valverde, Sergio Uribe, and Julio Sotelo. "Prediction of Peak-to-Peak Pressure Gradient in Patients with Aortic Coarctation Using Physics-Informed Neural Networks." In 2024 L Latin American Computer Conference (CLEI). IEEE, 2024. http://dx.doi.org/10.1109/clei64178.2024.10700502.

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Zhou, Peishan, Belinda Schwerin, and Stephen So. "U-Net Based Fetal R-peak Prediction From Abdominal ECG Signals." In 2024 9th International Conference on Signal and Image Processing (ICSIP). IEEE, 2024. http://dx.doi.org/10.1109/icsip61881.2024.10671460.

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Hu, Lingli. "End-to-End Inventory Replenishment Model with Bi-LSTM Peak Prediction for Demand." In 2024 7th International Conference on Computer Information Science and Application Technology (CISAT). IEEE, 2024. http://dx.doi.org/10.1109/cisat62382.2024.10695396.

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Chen, Yikun, Lifen Huang, Ziyi Cai, Lu Qiu, Haiming Chen, and Sijing Lai. "Carbon peak and new energy vehicle market prediction system based on multimodal machine learning." In Second International Conference on Intelligent Transportation and Smart Cities (ICITSC 2025), edited by Yaxian Li, Vitaliy Mezhuyev, and Zongzhi Li. SPIE, 2025. https://doi.org/10.1117/12.3073625.

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Muppawar, Harsh, and Utkarsha Pacharney. "AI-Driven Prediction Model for Forecasting the Spread and Peak of COVID-19 using Epidemiological Data." In 2025 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2025. https://doi.org/10.1109/icears64219.2025.10940372.

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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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Mohammed, Mona, Arabi Keshk, and Hatem M. Ahmed. "VGG Model for Peak Ground Acceleration Prediction." In 2024 International Conference on Machine Intelligence and Smart Innovation (ICMISI). IEEE, 2024. http://dx.doi.org/10.1109/icmisi61517.2024.10580357.

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Informes sobre el tema "Peak prediction"

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

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Brown, Dr Brendan, and Dr Rick Llewellyn. Improving Agricultural Policy and Programming through Data-Driven Adoption Prediction. Asian Productivity Organization, 2024. http://dx.doi.org/10.61145/cwao6767.

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This report evaluates the Adoption and Diffusion Outcome Prediction Tool (ADOPT) as a decision support system for agricultural policy across APO member economies. Through validation studies in Bangladesh, India, and Lao PDR, the research demonstrates ADOPT's strength in predicting peak adoption rates while identifying its tendency to overestimate adoption speed in developing contexts. The study introduces the 'Four Ps' framework and concludes that ADOPT, combined with structured analysis methods, provides valuable support for data-driven agricultural policy decisions in Asian contexts.

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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, 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, 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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DiFolco, Donna, Julie Maier, Donna DiFolco, and Julie Maier. Snowshoe hare population trends at mineral and non-mineral sites in the central Brooks Range, Alaska: Final report on the snowshoe hare ecology project, 1997?2023. National Park Service, 2024. http://dx.doi.org/10.36967/2306544.

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This report presents data we have compiled while monitoring localized snowshoe hare populations in the central Brooks Range near Wiseman, Alaska, using track and pellet counts. In addition, we documented snowshoe hare geophagy?the practice of consuming soil?via trail cameras, thus confirming the use of mineral licks by snowshoe hares in this area. Evidence of geophagy by snowshoe hares (Lepus americanus) was observed during track count surveys in Gates of the Arctic National Park and Preserve west of Wiseman, Alaska, in 1997?2001 when the hare population reached an exceptionally high peak. Long-time residents claimed that hares with winter access to mineral licks reached higher densities than hares without year-round access to licks. In 2007 we initiated pellet plot counts to monitor hare populations in four areas where hares had year-round access to a mineral lick (?mineral? sites) and three areas where hares did not have minerals available year-round (?non-mineral? sites). Hare densities in non-mineral areas peaked around 2009, albeit moderately, 10 years after the extreme peak in 1997?2001 documented by the track count. By contrast, hares at mineral sites exhibited no apparent increase in population densities at this time. Local knowledge predicted that there would not be another large increase in hare densities in mineral areas until approximately 2018. Pellet count data later supported this prediction when, in 2019, peaks of hare populations at mineral sites surpassed those at non-mineral sites.

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Alves, Jose-Henrique, Roberto Padilla-Hernandez, Deanna Spindler, et al. Development of a wave model component in the first coupled Global Ensemble Forecast System at NOAA. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49784.

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We describe the development of the wave component in the first global-scale coupled operational forecast system using the Unified Forecasting System at NOAA, part of the U.S. National Weather Service operational forecasting suite. The operational implementation of the atmosphere–wave coupled Global Ensemble Forecast System, version 12, was a critical step in NOAA’s transition to the broader community based UFS framework. GEFSv12 represents a significant advancement, extending forecast ranges and empowering the NWS to deliver advanced weather predictions with extended lead times for high-impact events. The integration of a coupled wave component with higher spatial and temporal resolution and optimized physics parameterizations enhanced forecast skill and predictability, particularly benefiting winter storm predictions of wave heights and peak wave periods. This endeavor encountered challenges addressed by the simultaneous development of new features that enhanced wave model forecast skill and product quality and facilitated by a team collaborating with NOAA’s operational forecasting centers. The GEFSv12 upgrade marks a pivotal shift in NOAA’s global forecasting capabilities, setting a new standard in wave prediction. We also describe the coupled GEFSv12-Wave component impacts on NOAA operational forecasts and ongoing experimental enhancements, which represent a substantial contribution to NOAA’s transition to the fully coupled UFS framework.

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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), 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. Pipeline Research Council International, Inc. (PRCI), 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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Tiku, Sanjay, Arnav Rana, and Binoy John. PR214-223810-R01 Improvement in Dent Assessment and Management Tools. Pipeline Research Council International, Inc. (PRCI), 2024. http://dx.doi.org/10.55274/r0000090.

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This project builds on and supplements existing mechanical damage (MD) assessment and management tools, developed on behalf of Pipeline Research Council International (PRCI), Interstate Natural Gas Association of America (INGAA), Canadian Energy Pipeline Association (CEPA), American Petroleum Institute (API), other research organizations and individual pipeline operators, many of which are included in API Recommended Practice (RP) 1183 (1). Since the assembly of API RP 1183, PRCI has continued its mechanical damage strategic research priority in the development of a greater understanding of the behavior of mechanical damage and the production of data to support engineering assessment. The objective of this research project is to develop and/or modify: - Scaling factors to develop separate safety factors associated with fatigue life prediction of restrained and unrestrained dents, - Modification of dent weld interaction approach to consider distance of welds from the dent peak, and - Development of stress range magnification factor approach for Level 2 assessment similar to other screening life approaches incorporated in API RP 1183.

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Senz, Anja, and Belinda Uebler. Public Predictions about China’s Carbon Emissions Peak: Dynamics and Impacts. Beyond the Horizon ISSG, 2024. http://dx.doi.org/10.31175/eh4s.2014.13.

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