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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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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 (March 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 (August 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 (July 31, 2023): 607–17. http://dx.doi.org/10.7465/jkdi.2023.34.4.607.
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Oukaira, Aziz, Amrou Zyad Benelhaouare, Dariush Amirkhani, Jamal Zbitou, and Ahmed Lakhssassi. "Silicon Die Transient Thermal Peak Prediction Approach." ITM Web of Conferences 48 (2022): 02007. http://dx.doi.org/10.1051/itmconf/20224802007.
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It is well known that Field Programmable Gate Arrays (FPGA) are good platforms for implementing embedded systems because of their configurable nature. However, the temperature of FPGAs is becoming a serious concern. Improvements in manufacturing technology led to increased logic density in integrated circuits as well as higher clock frequencies. As logic density increases, so do power density, which in turn increases the temperature, FPGAs follow the same path. A prediction of the thermal state of the Altera Cyclone V System-on-Chip (SoC) is presented in this work. The prediction study employs a numerical technique called Finite Element Method (FEM), which is a discretization method to approximate the real solution of the Partial Differential Equation (PDE) for heat transfer around the board's critical sources. The DE1 5CSEMA5F31C6N board was simulated using the COMSOL Multiphysics® tool for predicting thermal peaks during 13 hours of normal operation. Using the NISA tool, we obtained very similar results to those previously obtained with a margin of error of 2 %. As a result, a Verilog code implementation that describes the same approach used by the last two simulation tools is uploaded to the FPGA to verify the results of these simulations. This paper provides a more accurate vision of the level of operating stability of our FPGA board, which are currently the most important source for prototyping and designing the world's largest systems.
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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 (April 1, 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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Kim, Dong-Hoon, Eun-Kyu Lee, and Naik Bakht Sania Qureshi. "Peak-Load Forecasting for Small Industries: A Machine Learning Approach." Sustainability 12, no. 16 (August 13, 2020): 6539. http://dx.doi.org/10.3390/su12166539.
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Peak-load forecasting prevents energy waste and helps with environmental issues by establishing plans for the use of renewable energy. For that reason, the subject is still actively studied. Most of these studies are focused on improving predictive performance by using varying feature information, but most small industrial facilities cannot provide such information because of a lack of infrastructure. Therefore, we introduce a series of studies to implement a generalized prediction model that is applicable to these small industrial facilities. On the basis of the pattern of load information of most industrial facilities, new features were selected, and a generalized model was developed through the aggregation of ensemble models. In addition, a new method is proposed to improve prediction performance by providing additional compensation to the prediction results by reflecting the fewest opinions among the prediction results of each model. Actual data from two small industrial facilities were applied to our process, and the results proved the effectiveness of our proposed method.
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Romine, William, Noah Schroeder, Tanvi Banerjee, and Josephine Graft. "Toward Mental Effort Measurement Using Electrodermal Activity Features." Sensors 22, no. 19 (September 28, 2022): 7363. http://dx.doi.org/10.3390/s22197363.
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The ability to monitor mental effort during a task using a wearable sensor may improve productivity for both work and study. The use of the electrodermal activity (EDA) signal for tracking mental effort is an emerging area of research. Through analysis of over 92 h of data collected with the Empatica E4 on a single participant across 91 different activities, we report on the efficacy of using EDA features getting at signal intensity, signal dispersion, and peak intensity for prediction of the participant’s self-reported mental effort. We implemented the logistic regression algorithm as an interpretable machine learning approach and found that features related to signal intensity and peak intensity were most useful for the prediction of whether the participant was in a self-reported high mental effort state; increased signal and peak intensity were indicative of high mental effort. When cross-validated by activity moderate predictive efficacy was achieved (AUC = 0.63, F1 = 0.63, precision = 0.64, recall = 0.63) which was significantly stronger than using the model bias alone. Predicting mental effort using physiological data is a complex problem, and our findings add to research from other contexts showing that EDA may be a promising physiological indicator to use for sensor-based self-monitoring of mental effort throughout the day. Integration of other physiological features related to heart rate, respiration, and circulation may be necessary to obtain more accurate predictions.
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Zeng, Qinghui, Xiaolin Yu, Haobo Ni, Lina Xiao, Ting Xu, Haisheng Wu, Yuliang Chen, et al. "Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm." PLOS Neglected Tropical Diseases 17, no. 6 (June 7, 2023): e0011418. http://dx.doi.org/10.1371/journal.pntd.0011418.
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Predicting the specific magnitude and the temporal peak of the epidemic of individual local outbreaks is critical for infectious disease control. Previous studies have indicated that significant differences in spatial transmission and epidemic magnitude of dengue were influenced by multiple factors, such as mosquito population density, climatic conditions, and population movement patterns. However, there is a lack of studies that combine the above factors to explain their complex nonlinear relationships in dengue transmission and generate accurate predictions. Therefore, to study the complex spatial diffusion of dengue, this research combined the above factors and developed a network model for spatiotemporal transmission prediction of dengue fever using metapopulation networks based on human mobility. For improving the prediction accuracy of the epidemic model, the ensemble adjusted Kalman filter (EAKF), a data assimilation algorithm, was used to iteratively assimilate the observed case data and adjust the model and parameters. Our study demonstrated that the metapopulation network-EAKF system provided accurate predictions for city-level dengue transmission trajectories in retrospective forecasts of 12 cities in Guangdong province, China. Specifically, the system accurately predicts local dengue outbreak magnitude and the temporal peak of the epidemic up to 10 wk in advance. In addition, the system predicted the peak time, peak intensity, and total number of dengue cases more accurately than isolated city-specific forecasts. The general metapopulation assimilation framework presented in our study provides a methodological foundation for establishing an accurate system with finer temporal and spatial resolution for retrospectively forecasting the magnitude and temporal peak of dengue fever outbreaks. These forecasts based on the proposed method can be interoperated to better support intervention decisions and inform the public of potential risks of disease transmission.
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Latifoğlu, Levent, and Emre Altuntaş. "Deep Learning Approaches for Stream Flow and Peak Flow Prediction: A Comparative Study." European Journal of Research and Development 4, no. 1 (March 31, 2024): 61–84. http://dx.doi.org/10.56038/ejrnd.v4i1.422.
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Stream flow prediction is crucial for effective water resource management, flood prevention, and environmental planning. This study investigates the performance of various deep neural network architectures, including LSTM, biLSTM, GRU, and biGRU models, in stream flow and peak stream flow predictions. Traditional methods for stream flow forecasting have relied on hydrological models and statistical techniques, but recent advancements in machine learning and deep learning have shown promising results in improving prediction accuracy. The study compares the performance of the models using comprehensive evaluations with 1-6 input steps for both general stream flow and peak stream flow predictions. Additionally, a detailed analysis is conducted specifically for the biLSTM model, which demonstrated high performance results. The biLSTM model is evaluated for 1-4 ahead forecasting, providing insights into its specific strengths and capabilities in capturing the dynamics of stream flow. Results show that the biLSTM model outperforms other models in terms of prediction accuracy, especially for peak stream flow forecasting. Scatter plots illustrating the forecasting performances of the models further demonstrate the effectiveness of the biLSTM model in capturing temporal dependencies and nonlinear patterns in stream flow data. This study contributes to the literature by evaluating and comparing the performance of deep neural network models for general and peak stream flow prediction, highlighting the effectiveness of the biLSTM model in improving the accuracy and reliability of stream flow forecasts.
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Zheng, Jingbin, Shaoqing Zhang, Dong Wang, and Jun Jiang. "Optimization for the Assessment of Spudcan Peak Resistance in Clay–Sand–Clay Deposits." Journal of Marine Science and Engineering 9, no. 7 (June 24, 2021): 689. http://dx.doi.org/10.3390/jmse9070689.
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Clay–sand–clay deposits are commonly encountered in the offshore field. For spudcan installation in this soil stratigraphy, the potential for punch-through exists, with the peak penetration resistance formed within the interbedded sand layer. Therefore, a careful assessment of the penetration resistance profile has to be performed. Based on the recently proposed failure-stress-dependent model, this paper presents a modified predictive model for estimating the peak resistance. The modified model incorporates the bearing capacity depth factor and the protruded soil plug in the bottom clay layer into the formulation. It is proven that the modified predictive model provides improved deterministic estimations for the peak resistances measured in centrifuge tests. Based on the modified predictive model, a parameter optimization technique is utilized to optimize the prediction of peak resistance using penetration resistances observed beforehand. A detailed application procedure is proposed and applied to the centrifuge tests accumulated from existing publications, with further improvement on the predictions demonstrated. The proposed parameter optimization procedure combined with the modified predictive model provides an approach to perform real-time optimization for assessing spudcan peak resistance in clay–sand–clay deposits.
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Nilsson, Lars-Olof. "Chloride profiles with a peak – why and what are the consequences for predictions?" MATEC Web of Conferences 364 (2022): 02024. http://dx.doi.org/10.1051/matecconf/202236402024.
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Chloride ingress profiles do almost always have a peak at some depth but most prediction models are missing this peak. Some prediction models, such as the fib model, simply “cut off” a slice of the concrete up to the peak in further predictions. Other prediction models use data only from the profiles beyond the peak but include the concrete up to the peak as if it has the same properties as the rest of the concrete. A physical model has been developed to quantify the local changes because of leaching and the consequences of these changes with time. The model uses Fick’s 1st law for chloride diffusion and linear chloride binding. The depth of leaching with time is modelled with a simple square-root equation. The consequences of leaching are assumed to be linear from the surface into the maximum depth of leaching. The consequences of leaching are modelled as depth-dependent changes of porosity, chloride binding and the diffusion coefficient in Fick’s first law.
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Jones, Trevor H., and N. Brad Willms. "A critique of Hubbert’s model for peak oil." FACETS 3, no. 1 (October 1, 2018): 260–74. http://dx.doi.org/10.1139/facets-2017-0097.
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In 1956, Shell Oil Company geologist M. King Hubbert published a model for the growth and decline over time of the production rates of oil extracted from the land mass of the continental US. Employing an estimate for the amount of ultimately recoverable oil and a logistic curve for the oil production rate, he accurately predicted a peak in US oil production for 1970. His arguments and the success of his prediction have been much celebrated, and the original paper has 1400 publication citations to date. The theory of “peak oil” (and subsequently, of natural resource scarcity in general) has consequently become associated with Hubbert and “Hubbert” curves and models. However, his prediction for the timing of a world peak oil production rate and the subsequent predictions of many others have proven inaccurate. We revisit the Hubbert model for oil extraction and provide an analysis of it and several variants in the language of (time) autonomous differential equations.
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Glanz, James. "Bold Prediction Downplays the Sun's Next Peak." Science 275, no. 5302 (February 14, 1997): 927. http://dx.doi.org/10.1126/science.275.5302.927.
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van derVeen, C. J. "Reevaluating Hubbert's prediction of U.S. peak oil." Eos, Transactions American Geophysical Union 87, no. 20 (2006): 199. http://dx.doi.org/10.1029/2006eo200003.
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Hefke, Frederik, Roland Schmucki, and Peter Güntert. "Prediction of peak overlap in NMR spectra." Journal of Biomolecular NMR 56, no. 2 (April 13, 2013): 113–23. http://dx.doi.org/10.1007/s10858-013-9727-9.
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Kim, Eunhye, Tsatsral Amarbayasgalan, and Hoon Jung. "Efficient Weighted Ensemble Method for Predicting Peak-Period Postal Logistics Volume: A South Korean Case Study." Applied Sciences 12, no. 23 (November 23, 2022): 11962. http://dx.doi.org/10.3390/app122311962.
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Demand prediction for postal delivery services is useful for managing logistic operations optimally. Particularly for holiday periods, namely the Lunar New Year and Korean Thanksgiving Day (Chuseok) in South Korea, the logistics service increases sharply compared with the usual period, which makes it hard to provide reliable operation in mail centers. This study proposes a Multilayer Perceptron-based weighted ensemble method for predicting the accepted parcel volumes during special periods. The proposed method consists of two main phases: the first phase enriches the training dataset via synthetic samples using unsupervised learning; the second phase builds two Multilayer Perceptron models using internal and external factor-derived features for prediction. The final result is estimated by the weighted average predictions of these models. We conducted experiments on 25 Korean mail center datasets. The experimental study on the dataset provided by Korea Post shows better performance than other compared methods.
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Kompor, Wongnarin, Sayaka Yoshikawa, and Shinjiro Kanae. "Use of Seasonal Streamflow Forecasts for Flood Mitigation with Adaptive Reservoir Operation: A Case Study of the Chao Phraya River Basin, Thailand, in 2011." Water 12, no. 11 (November 16, 2020): 3210. http://dx.doi.org/10.3390/w12113210.
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Predicting streamflow can help water managers make policy decisions for individual river basins. In 2011, heavy rainfall from May until October resulted in the largest flood event in the history of Thailand. This event created difficulty for water managers, who lacked information to make predictions. Studies on the 2011 Thai flood have proposed alternative reservoir operations for flood mitigation. However, no study to date has used predictive information to determine how to control reservoirs and mitigate such extreme floods. Thus, the objective of this study is to update and develop a method for using streamflow predictive data to support adaptive reservoir operation with the aim of mitigating the 2011 flood. The study area was the Chao Phraya River Basin, one of the most important basins in Thailand. We obtained predictive information from a hydrological model with a reservoir operation module using an ensemble of seasonal precipitation data from the European Centre for Medium–Range Weather Forecasts (ECMWF). The six-month ECMWF prediction period was used to support the operation plan for mitigating flooding in 2011 around each reservoir during the wet season. Decision-making for reservoir operation based on seasonal predictions was conducted on a monthly time scale. The results showed that peak river discharge decreased slightly, by around 4%, when seasonal predictive data were used. Moreover, changing the reservoir operation plan and using seasonal predictions decreased the peak river discharge by around 20%.
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Han, Heechan, Changhyun Choi, Jaewon Jung, and Hung Soo Kim. "Deep Learning with Long Short Term Memory Based Sequence-to-Sequence Model for Rainfall-Runoff Simulation." Water 13, no. 4 (February 8, 2021): 437. http://dx.doi.org/10.3390/w13040437.
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Accurate runoff prediction is one of the important tasks in various fields such as agriculture, hydrology, and environmental studies. Recently, with massive improvements of computational system and hardware, the deep learning-based approach has recently been applied for more accurate runoff prediction. In this study, the long short-term memory model with sequence-to-sequence structure was applied for hourly runoff predictions from 2015 to 2019 in the Russian River basin, California, USA. The proposed model was used to predict hourly runoff with lead time of 1–6 h using runoff data observed at upstream stations. The model was evaluated in terms of event-based performance using the statistical metrics including root mean square error, Nash-Sutcliffe Efficiency, peak runoff error, and peak time error. The results show that proposed model outperforms support vector machine and conventional long short-term memory models. In addition, the model has the best predictive ability for runoff events, which means that it can be effective for developing short-term flood forecasting and warning systems. The results of this study demonstrate that the deep learning-based approach for hourly runoff forecasting has high predictive power and sequence-to-sequence structure is effective method to improve the prediction results.
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Sun, Rui, Wanwan Qi, Tong Zheng, and Jinlei Qi. "Explainable Machine-Learning Predictions for Peak Ground Acceleration." Applied Sciences 13, no. 7 (April 3, 2023): 4530. http://dx.doi.org/10.3390/app13074530.
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Peak ground acceleration (PGA) prediction is of great significance in the seismic design of engineering structures. Machine learning is a new method to predict PGA and does have some advantages. To establish explainable prediction models of PGA, 3104 sets of uphole and downhole seismic records collected by the KiK-net in Japan were used. The feature combinations that make the models perform best were selected through feature selection. The peak bedrock acceleration (PBA), the predominant frequency (FP), the depth of the soil when the shear wave velocity reaches 800 m/s (D800), and the bedrock shear wave velocity (Bedrock Vs) were used as inputs to predict the PGA. The XGBoost (eXtreme Gradient Boosting), random forest, and decision tree models were established, and the prediction results were compared with the numerical simulation results The influence between the input features and the model prediction results were analyzed with the SHAP (SHapley Additive exPlanations) value. The results show that the R2 of the training dataset and testing dataset reach up to 0.945 and 0.915, respectively. On different site classifications and different PGA intervals, the prediction results of the XGBoost model are better than the random forest model and the decision tree model. Even if a non-integrated algorithm (decision tree model) is used, its prediction effect is better than the numerical simulation methods. The SHAP values of the three machine learning models have the same distribution and densities, and the influence of each feature on the prediction results is consistent with the existing empirical data, which shows the rationality of the machine learning models and provides reliable support for the prediction results.
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Gupta, R. N., P. Pal Roy, and B. Singh. "Prediction of peak particle velocity and peak air pressure generated by buried explosion." International Journal of Mining and Geological Engineering 6, no. 1 (March 1988): 15–26. http://dx.doi.org/10.1007/bf00881024.
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Gupta, R. N., P. Pal Roy, and B. Singh. "Prediction of peak particle velocity and peak air pressure generated by buried explosion." International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts 26, no. 2 (March 1989): 78. http://dx.doi.org/10.1016/0148-9062(89)90222-2.
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Nakubulwa, Susan Kiwanuka, K. Baisley, and J. Levin. "Prediction of peak expiratory flow rate in a Ugandan population." South African Respiratory Journal 21, no. 4 (December 4, 2015): 96. http://dx.doi.org/10.7196/sarj.2015.v21i4.36.
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<p>Background. Peak expiratory ow rate (PEFR) measurement is one of the commonly used methods for assessing lung function in general practice<br />consultations. e reference values for use by this method are mainly from Caucasian populations; data for African populations are limited. e<br />existence of ethnic and racial dierences in lung function necessitates further generation of PEFR reference values for use in African populations.<br />Objective. To generate equations for predicting PEFR in a Ugandan population.<br />Methods. e PEFR study was cross-sectional and based in rural south-western Uganda. Participants were aged 15 years or more, without respiratory<br />symptoms and were residents of the study area. Multiple regression equations for predicting PEFR were tted separately for males and females. e<br />model used for PEFR prediction was: logePEFR = intercept + a(age, y) + b(logeage) + c(1/height in cm), where a, b and c are the regression coecients.<br />Results. e eligible study population consisted of 774 males and 781 females. Median height was 164 cm (males) and 155 cm (females).<br />e majority of participants had never smoked (males 76.7%; females 98.3%). e equation which gave the best t for males was<br />logePEFR = 6.188 – 0.019age + 0.557logeage – 199.945/height and for females: logePEFR = 5.948 – 0.014 age + 0.317logeage – 85.147/height.<br />Conclusion. e curvilinear model obtained takes into consideration the changing trends of PEFR with increasing age from adolescence<br />to old age. It provides PEFR prediction equations that can be applied in East African populations.</p>
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Xie, Shijie, Rubing Yao, Yatao Yan, Hang Lin, Peilei Zhang, and Yifan Chen. "Hybrid Machine-Learning-Based Prediction Model for the Peak Dilation Angle of Rock Discontinuities." Materials 16, no. 19 (September 24, 2023): 6387. http://dx.doi.org/10.3390/ma16196387.
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The peak dilation angle is an important mechanical feature of rock discontinuities, which is significant in assessing the mechanical behaviour of rock masses. Previous studies have shown that the efficiency and accuracy of traditional experimental methods and analytical models in determining the shear dilation angle are not completely satisfactory. Machine learning methods are popular due to their efficient prediction of outcomes for multiple influencing factors. In this paper, a novel hybrid machine learning model is proposed for predicting the peak dilation angle. The model incorporates support vector regression (SVR) techniques as the primary prediction tools, augmented with the grid search optimization algorithm to enhance prediction performance and optimize hyperparameters. The proposed model was employed on eighty-nine datasets with six input variables encompassing morphology and mechanical property parameters. Comparative analysis is conducted between the proposed model, the original SVR model, and existing analytical models. The results show that the proposed model surpasses both the original SVR model and analytical models, with a coefficient of determination (R2) of 0.917 and a mean absolute percentage error (MAPE) of 4.5%. Additionally, the study also reveals that normal stress is the most influential mechanical property parameter affecting the peak dilation angle. Consequently, the proposed model was shown to be effective in predicting the peak dilation angle of rock discontinuities.
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Arhin, Stephen, Babin Manandhar, and Hamdiat Baba-Adam. "Predicting Travel Times of Bus Transit in Washington, D.C. Using Artificial Neural Networks." Civil Engineering Journal 6, no. 11 (November 1, 2020): 2245–61. http://dx.doi.org/10.28991/cej-2020-03091615.
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This study aimed to develop travel time prediction models for transit buses to assist decision-makers improve service quality and patronage. Six-months’ worth of Automatic Vehicle Location and Automatic Passenger Counting data for six Washington Metropolitan Area Transit Authority bus routes operating in Washington, DC was used for this study. Artificial Neural Network (ANN) models were developed for predicting travel times of buses for different peak periods. The analysis included variables such as length of route between stops, average dwell time and number of intersections between bus stops amongst others. Quasi-Newton algorithm was used to train the data to obtain the ideal number of perceptron layers that generated the least amount of error for all peak models. Comparison of the Normalized Squared Errors generated during the training process was done to evaluate the models. Travel time equations for buses were obtained for different peaks using ANN. The results indicate that the prediction models can effectively predict bus travel times on selected routes during different peaks of the day with minimal percentage errors. These prediction models can be adapted by transit agencies to provide patrons with more accurate travel time information at bus stops or online. Doi: 10.28991/cej-2020-03091615 Full Text: PDF
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Mutuku, Vincent, Joshua Mwema, and Mutwiri Joseph. "Time-Series Prediction of Gamma-Ray Counts Using XGB Algorithm." Open Journal for Information Technology 5, no. 1 (August 11, 2022): 33–40. http://dx.doi.org/10.32591/coas.ojit.0501.03033m.
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Radioactivity is spontaneous and thus not easy to predict when it will occur. The average number of decay events in a given interval can lead to accurate projection of the activity of a sample. The possibility of predicting the number of events that will occur in a given time using machine learning has been investigated. The prediction performance of the Extreme gradient boosted (XGB) regression algorithm was tested on gamma-ray counts for K-40, Pb-212 and Pb-214 photo peaks. The accuracy of the prediction over a six-minute duration was observed to improve at higher peak energies. The best performance was obtained at 1460keV photopeak energy of K-40 while the least is at 239keV peak energy of Pb-212. This could be attributed to higher number of data points at higher peak energies which are broad for NaITi detector hence the model had more features to learn from. High R-squared values in the order of 0.99 and 0.97 for K-40 and Pb-212 peaks respectively suggest model overfitting which is attributed to the small number of detector channels. Although radioactive events are spontaneous in nature and not easy to predict when they will occur, it has been established that the average number of counts during a given period of time can be modelled using the XGB algorithm. A similar study with a NaITi gamma detector of high channel numbers and modelling with other machine learning algorithms would be important to compare the findings of the current study.
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Zhao, Wuchao, Jiang Qian, and Pengzhao Jia. "Peak Response Prediction for RC Beams under Impact Loading." Shock and Vibration 2019 (January 22, 2019): 1–12. http://dx.doi.org/10.1155/2019/6813693.
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In this paper, a novel and simple method for predicting the peak response of RC beams subjected to impact loading is proposed. The theoretical basis for calculating the peak impact force originates from the contact law, the principle of conservation of energy, the impulse-momentum theorem, and the wave theory. Additionally, the conventional beam theory, in conjunction with the well-known layered-section approach, is utilized to obtain the force-deflection relationship of the RC beam. Subsequently, by taking into account the strain rate effect, the maximum midspan deflection of RC beams under impact loading is determined based on the conservation of energy approach. A comparison with 143 impact tests has shown that the proposed method is able to estimate the maximum midspan deflection of RC beams under impact loading with high accuracy. The prediction of the peak impact force is shown to be slightly overestimated, which however can be used in the anti-impact design to preclude the shear failure near the impact point.
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Zhang, Zhaohui, Qiuwen Liu, Ligong Chen, and Pengwei Wang. "A Peak Prediction Method for Subflow in Hybrid Data Flow." Scientific Programming 2020 (February 14, 2020): 1–13. http://dx.doi.org/10.1155/2020/2548351.
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Subflow prediction is required in resource active elastic scaling, but the existing single flow prediction methods cannot accurately predict the peak variation of subflow in hybrid data flow. These do not consider the correlation between subflows. The difficulty is that it is hard to calculate the correlation between different data flows in hybrid data flow. In order to solve this problem, this paper proposes a new method DCCSPP (subflow peak prediction of hybrid data flow based on delay correlation coefficients) to predict the peak value of hybrid data flow. Firstly, we establish a delay correlation coefficient model based on the sliding time window to determine the delay time and delay correlation coefficient. Next, based on the model, a hybrid data flow subflow peak prediction model and algorithm are established to achieve accurate peak prediction of subflow. Experiments show that our prediction model has achieved better results. Compared with LSTM, our method has decreased the MAE about 18.36% and RMSE 13.50%. Compared with linear regression, MAE and RMSE are decreased by 27.12% and 25.58%, respectively.
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Pu, Xingfan, Jian Yao, and Rongyue Zheng. "Forecast of Energy Consumption and Carbon Emissions in China’s Building Sector to 2060." Energies 15, no. 14 (July 6, 2022): 4950. http://dx.doi.org/10.3390/en15144950.
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The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building CO2 emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building CO2 emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building CO2 emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies.
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Chrystyn, Henry. "Validation of the use of Bayesian Analysis in the Optimization of Gentamicin Therapy from the Commencement of Dosing." Drug Intelligence & Clinical Pharmacy 22, no. 1 (January 1988): 49–53. http://dx.doi.org/10.1177/106002808802200112.
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A computer program based on the statistical technique of Bayesian analysis has been adapted to run on several microcomputers. The clinical application of this method for gentamicin has been validated in 13 patients with varying degrees of renal function by a comparison of the accuracy of this method to a predictive algorithm method and one using standard pharmacokinetic principles. Blood samples for serum gentamicin analysis were taken after the administraiton of an intravenous loading dose of gentamicin. The results produced by each method were used to predict the peak and trough values measured on day 3 of therapy. Of the three methods studied, Bayesian analysis, using a serum gentamicin concentration drawn four hours after the initial dose, was the least biased and the most precise method for predicting the observed levels. The mean prediction error of the Bayesian analysis method, using the four-hour sample, was −0.03 mg/L for the peak serum concentration and −0.07 mg/L for the trough level on day 3. Using this method the corresponding root mean squared prediction error was 0.60 mg/L and 0.36 mg/L for the peak and trough levels, respectively.
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Bommer*, Julian J., and John E. Alarcon*. "THE PREDICTION AND USE OF PEAK GROUND VELOCITY." Journal of Earthquake Engineering 10, no. 1 (January 2006): 1–31. http://dx.doi.org/10.1080/13632460609350586.
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Lu, Peng. "Cost heterogeneity and peak prediction in collective actions." Expert Systems with Applications 79 (August 2017): 130–39. http://dx.doi.org/10.1016/j.eswa.2017.02.009.
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Brenneis, Marco, Niklas Thewes, Jana Holder, Felix Stief, and Sebastian Braun. "Validation of central peak height method for final adult height predictions on long leg radiographs." Bone & Joint Open 4, no. 10 (October 10, 2023): 750–57. http://dx.doi.org/10.1302/2633-1462.410.bjo-2023-0105.r1.
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AimsAccurate skeletal age and final adult height prediction methods in paediatric orthopaedics are crucial for determining optimal timing of growth-guiding interventions and minimizing complications in treatments of various conditions. This study aimed to evaluate the accuracy of final adult height predictions using the central peak height (CPH) method with long leg X-rays and four different multiplier tables.MethodsThis study included 31 patients who underwent temporary hemiepiphysiodesis for varus or valgus deformity of the leg between 2014 and 2020. The skeletal age at surgical intervention was evaluated using the CPH method with long leg radiographs. The true final adult height (FHTRUE) was determined when the growth plates were closed. The final height prediction accuracy of four different multiplier tables (1. Bayley and Pinneau; 2. Paley et al; 3. Sanders – Greulich and Pyle (SGP); and 4. Sanders – peak height velocity (PHV)) was then compared using either skeletal age or chronological age.ResultsAll final adult height predictions overestimated the FHTRUE, with the SGP multiplier table having the lowest overestimation and lowest absolute deviation when using both chronological age and skeletal age. There were no significant differences in final height prediction accuracy between using skeletal age and chronological age with PHV (p = 0.652) or SGP multiplier tables (p = 0.969). Adult height predictions with chronological age and SGP (r = 0.769; p ≤ 0.001), as well as chronological age and PHV (r = 0.822; p ≤ 0.001), showed higher correlations with FHTRUE than predictions with skeletal age and SGP (r = 0.657; p ≤ 0.001) or skeletal age and PHV (r = 0.707; p ≤ 0.001).ConclusionThere was no significant improvement in adult height prediction accuracy when using the CPH method compared to chronological age alone. The study concludes that there is no advantage in routinely using the CPH method for skeletal age determination over the simple use of chronological age. The findings highlight the need for more accurate methods to predict final adult height in contemporary patient populations.Cite this article: Bone Jt Open 2023;4(10):750–757.
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Li, Ting Ting, Xing Xing Zhang, Shi Zhong Ma, and Zhao Wang. "The Application of Peak Number Attribute in the Prediction of River Sand." Advanced Materials Research 838-841 (November 2013): 1591–94. http://dx.doi.org/10.4028/www.scientific.net/amr.838-841.1591.
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Its a commonly used method to predict layer by using seismic attributes, especially for some of the less well control channel sand bodies whose role is more important. Putaohua reservoir in Gaotaizi oilfield mainly develop shallow water delta front subaqueous distributary channel sand bodies which has narrow rivers and thin sand bodies, meanwhile, the existing well density is difficult to control the trend and boundary of the channel. By using seismic forward modeling analysis techniques, this paper researched the differences of seismic reflection characteristics among different geological model of channel sand bodies, then , further pointed out the methods of channel sand prediction by using the peak number attribute and analyzed the predictive effect. The results show that this method can effectively improve the prediction accuracy of thin interbedded reservoir.
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Jampole, Ezra, Eduardo Miranda, and Gregory G. Deierlein. "Predicting earthquake-induced sliding displacements using effective incremental ground velocity." Earthquake Spectra 36, no. 1 (January 13, 2020): 378–99. http://dx.doi.org/10.1177/8755293019878200.
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This article evaluates a pulse intensity measure, the effective incremental ground velocity ( EIGV), for predicting sliding displacements induced by real ground motions. EIGV is based on computing the additional incremental velocity of a pulse after a system begins to slide. Predictions of peak sliding displacements are made using multiple ground motion and pulse intensity measures, and it is found that at high friction levels, defined here as friction coefficient above 0.15, EIGV is a very effective parameter with a lognormal standard deviation of predicted displacements around 0.5, despite including only the properties of the largest pulse in a record. For high-friction systems, very few pulses usually cause the peak sliding displacement during the response history, hence the potential for an effective pulse intensity measure. EIGV improves sliding displacement predictions compared to existing intensity measures, which are geared toward conventional hysteretic systems. Prediction equations are developed for peak relative sliding displacement as a function of EIGV, the sliding interface coefficient of friction, and the radius of curvature for concave sliding surfaces.
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Takahashi, K., R. Ooka, and S. Ikeda. "Anomaly detection and missing data imputation in building energy data for automated data pre-processing." Journal of Physics: Conference Series 2069, no. 1 (November 1, 2021): 012144. http://dx.doi.org/10.1088/1742-6596/2069/1/012144.
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Abstract A new trend in building automation is the implementation of smart energy management systems to measure and control building systems without a need for decision-making by human operators. Artificial intelligence can optimize these systems by predicting future demand to make informed decisions about how to efficiently operate individual equipment. These machine learning algorithms use historical data to learn demand trends and require high quality datasets in order to make accurate predictions. But because of issues with data transmission or sensor errors, real world datasets often contain outliers or have data missing. In most research settings, these values can be simply omitted, but in practice, anomalies compromise the automation system’s prediction accuracy, rendering it unable to maximize energy savings. This study explores different machine learning algorithms for anomaly detection for automatically pre-processing incoming data using a case study on an actual electrical demand in a hospital building in Japan, namely cluster-based techniques such as k-means clustering and neural network-based approaches such as the autoencoder. Once anomalies were identified, the missing data was filled with prediction values from a deep neural network model. The newly composed data was then evaluated based on detection accuracy, prediction accuracy and training time. The proposed method of processing anomaly values allows the prediction model to process collected data without interruption, and shows similar predictive accuracy as manually processing the data. These predictions allow energy systems to optimize HVAC equipment control, increasing energy savings and reducing peak building loads.
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Sciannameo, Veronica, Danila Azzolina, Corrado Lanera, Aslihan Şentürk Acar, Maria Assunta Corciulo, Rosanna Irene Comoretto, Paola Berchialla, and Dario Gregori. "Fitting Early Phases of the COVID-19 Outbreak: A Comparison of the Performances of Used Models." Healthcare 11, no. 16 (August 21, 2023): 2363. http://dx.doi.org/10.3390/healthcare11162363.
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The COVID-19 outbreak involved a spread of prediction efforts, especially in the early pandemic phase. A better understanding of the epidemiological implications of the different models seems crucial for tailoring prevention policies. This study aims to explore the concordance and discrepancies in outbreak prediction produced by models implemented and used in the first wave of the epidemic. To evaluate the performance of the model, an analysis was carried out on Italian pandemic data from February 24, 2020. The epidemic models were fitted to data collected at 20, 30, 40, 50, 60, 70, 80, 90, and 98 days (the entire time series). At each time step, we made predictions until May 31, 2020. The Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE) were calculated. The GAM model is the most suitable parameterization for predicting the number of new cases; exponential or Poisson models help predict the cumulative number of cases. When the goal is to predict the epidemic peak, GAM, ARIMA, or Bayesian models are preferable. However, the prediction of the pandemic peak could be made carefully during the early stages of the epidemic because the forecast is affected by high uncertainty and may very likely produce the wrong results.
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Leandri, Pietro, and Massimo Losa. "Peak Friction Prediction Model Based on Surface Texture Characteristics." Transportation Research Record: Journal of the Transportation Research Board 2525, no. 1 (January 2015): 91–99. http://dx.doi.org/10.3141/2525-10.
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This paper proposes a new model for predicting the speed gradient of peak friction values on asphalt pavements on the basis of surface characteristics. The innovative feature of the proposed model is the reliable estimation of peak friction values experienced by vehicles equipped with an antilock brake system at a certain vehicle speed. To define the experimental model, several types of dense asphalt concrete surface layers with various surface characteristics were analyzed by in situ tests. Friction was measured with the Skiddometer BV11 and the British pendulum tester, and texture properties were measured with a laser profilometer. The Rado model was used to predict peak friction values at three vehicle speeds, and these data were used to determine the gradient of peak friction values for each pavement section. The spectral analysis of pavement profile data was used to define a texture parameter negatively correlated with peak friction values; this parameter was introduced in a new formulation of the speed number Sp* that was a measure of the influence of pavement macrotexture on peak friction values. The speed number Sp* was used in the new exponential model proposed for defining the gradient of peak friction values. The results show that the model is highly reliable; because the model allows identification of texture characteristics to be modified to optimize peak friction values, it is particularly useful for optimization of the mix design and maintenance of pavement surfaces.
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Kahl, Jonathan D. W. "Forecasting Peak Wind Gusts Using Meteorologically Stratified Gust Factors and MOS Guidance." Weather and Forecasting 35, no. 3 (May 28, 2020): 1129–43. http://dx.doi.org/10.1175/waf-d-20-0045.1.
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Abstract Gust prediction is an important element of weather forecasting services, yet reliable methods remain elusive. Peak wind gusts estimated by the meteorologically stratified gust factor (MSGF) model were evaluated at 15 locations across the United States during 2010–17. This model couples gust factors, site-specific climatological measures of “gustiness,” with wind speed and direction forecast guidance. The model was assessed using two forms of model output statistics (MOS) guidance at forecast projections ranging from 1 to 72 h. At 11 of 15 sites the MSGF model showed skill (improvement over climatology) in predicting peak gusts out to projections of 72 h. This has important implications for operational wind forecasting because the method can be utilized at any location for which the meteorologically stratified gust factors have been determined. During particularly windy conditions the MSGF model exhibited skill in predicting peak gusts at forecast projections ranging from 6 to 72 h at roughly half of the sites analyzed. Site characteristics and local wind climatologies were shown to exert impacts on gust factor model performance. The MSGF method represents a viable option for the operational prediction of peak wind gusts, although model performance will be sensitive to the quality of the necessary wind speed and direction forecasts.
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Wu, Zhenfen, Zhe Wang, Qiliang Yang, and Changyun Li. "Prediction Model of Electric Power Carbon Emissions Based on Extended System Dynamics." Energies 17, no. 2 (January 18, 2024): 472. http://dx.doi.org/10.3390/en17020472.
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In response to global climate change, China has committed to peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, commonly known as the “30–60 Dual Carbon”. Under the background of “30–60 Dual Carbon”, this article takes the electric power industry, which is the main industry contributing to China’s carbon emission, as the research object, explores the time and peak value of the carbon peak of the electric power industry, and analyzes whether carbon neutrality can be realized under the peak method, so as to get the carbon neutrality path of the electric power industry and serve as the theoretical basis for the formulation of relevant policies. The Environmental Kuznets Curve inspection and the relationship analysis are carried out, then the system dynamics model is constructed, the carbon emissions from 2020 to 2040 are simulated, and the peak time is predicted. Three different scenarios are set to explore the path of electricity carbon neutralization under the premise of a fixed peak. It is shown that Gross Domestic Product per capita index factors have the largest positive contribution, and thermal power share index factors have the largest negative contribution to electricity carbon emissions. Based on the current efforts of the new policy, carbon emissions can achieve the peak carbon emissions’ target before 2030, and it is expected to peak in 2029, with a peak range of about 4.95 billion tons. After the power industry peaks in 2029, i.e., Scenario 3, from coal 44%, gas 9% (2029) to coal 15%, gas 7% (2060), where the CCUS technology is widely used, this scenario can achieve carbon neutrality in electricity by 2060. Adjusting the power supply structure, strictly controlling the proportion of thermal power, optimizing the industrial structure, and popularization of carbon capture, utilization, and storage technology will all contribute to the “dual carbon” target of the power sector.
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Dangar, Nikhil S., and Pravin H. Vataliya. "Prediction of Lifetime Milk Yield using Principal Component Analysis in Gir Cattle." Indian Journal of Veterinary Sciences & Biotechnology 18, no. 4 (September 15, 2022): 92–96. http://dx.doi.org/10.48165/ijvsbt.18.4.19.
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The objective of the research was to investigate the relationship among production traits i.e., lactation milk yield, lactation length and lactation peak milk yield of the first three lactations using principal component analysis and formulation of prediction equation to predict lifetime milk production in Gir cattle. Data were from multiparous dairy cows of the University farm. Principal component analysis with correlation matrix was used to find the relationship among lactation milk yield, lactation length and lactation peak milk yield of first three lactation and other fixed effects, including the year of calving, season and parity with random effect of sire. The principal components were fitted to identify the best-fitted model for predicting lifetime milk yield using all principal components as a predictor in different combinations. The first six principal components (first lactation milk yield, lactation length and peak milk yield, second lactation milk yield, lactation length and peak milk yield), explained 98% variation in the estimated values with adjusted R2= 59.85% variation in the estimated values. The curve estimation analysis revealed that the first six principal components as the predictor was the most fitting model for predicting lifetime milk yield. The prediction equation found most fitted will be useful for the selection of Gir cattle at an early stage of lactation.