Academic literature on the topic 'Variable prediction horizons'
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Journal articles on the topic "Variable prediction horizons"
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Alamaniotis, Miltiadis, and Georgios Karagiannis. "Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short Term Wind Speed Forecasting in Smart Power." International Journal of Monitoring and Surveillance Technologies Research 5, no. 3 (July 2017): 1–14. http://dx.doi.org/10.4018/ijmstr.2017070101.
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This article describes how the integration of renewable energy in the power grid is a critical issue in order to realize a smart grid infrastructure. To that end, intelligent methods that monitor and currently predict the values of critical variables of renewable energy are essential. With respect to wind power, such variable is the wind speed given that it is of great interest to efficient schedule operation of a wind farm. In this article, a new methodology for predicting wind speed is presented for very short-term prediction horizons. The methodology integrates multiple Gaussian process regressors (GPR) via the adoption of an optimization problem whose solution is given by the particle swarm optimization algorithm. The optimized framework is utilized for the average hourly wind speed prediction for a prediction horizon of six hours ahead. Results demonstrate the ability of the methodology in accurately forecasting the wind speed. Furthermore, obtained forecasts are compared with those taken from single Gaussian process regressors as well from the integration of the same multiple GPR using a genetic algorithm.
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Abduljabbar, Rusul L., Hussein Dia, and Pei-Wei Tsai. "Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction." Journal of Advanced Transportation 2021 (March 26, 2021): 1–16. http://dx.doi.org/10.1155/2021/5589075.
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This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks. The unidirectional LSTM (Uni-LSTM) model provides high performance through its ability to recognize longer sequences of traffic time series data. In this work, Uni-LSTM is extended to bidirectional LSTM (BiLSTM) networks which train the input data twice through forward and backward directions. The paper presents a comparative evaluation of the two models for short-term speed and traffic flow prediction using a common dataset of field observations collected from multiple freeways in Australia. The results showed BiLSTM performed better for variable prediction horizons for both speed and flow. Stacked and mixed Uni-LSTM and BiLSTM models were also investigated for 15-minute prediction horizons resulting in improved accuracy when using 4-layer BiLSTM networks. The optimized 4-layer BiLSTM model was then calibrated and validated for multiple prediction horizons using data from three different freeways. The validation results showed a high degree of prediction accuracy exceeding 90% for speeds up to 60-minute prediction horizons. For flow, the model achieved accuracies above 90% for 5- and 10-minute prediction horizons and more than 80% accuracy for 15- and 30-minute prediction horizons. These findings extend the set of AI models available for road operators and provide them with confidence in applying robust models that have been tested and evaluated on different freeways in Australia.
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Montaser, Eslam, José-Luis Díez, and Jorge Bondia. "Glucose Prediction under Variable-Length Time-Stamped Daily Events: A Seasonal Stochastic Local Modeling Framework." Sensors 21, no. 9 (May 4, 2021): 3188. http://dx.doi.org/10.3390/s21093188.
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Accurate glucose prediction along a long-enough time horizon is a key component for technology to improve type 1 diabetes treatment. Subjects with diabetes might benefit from supervision and control systems that accurately predict risks and trigger corrective actions early enough with improved mitigation. However, large intra-patient variability poses big challenges to glucose prediction. In previous works by the authors, clustering and local modeling techniques with seasonal stochastic models proved to be efficient, allowing for good glucose prediction accuracy for long prediction horizons. Continuous glucose monitoring (CGM) data were partitioned into fixed-length postprandial time subseries and clustered with Fuzzy C-Means to collect similar behaviors, enforcing seasonality at each cluster after subseries concatenation. Then, seasonal stochastic models were identified for each cluster and local predictions were integrated into a global prediction. However, free-living conditions do not support the fixed-length partition of CGM data since daily events duration is variable. In this work, a new algorithm is provided to overcome this constraint, allowing better coping with patient’s variability under variable-length time-stamped daily events in supervision and control applications. Besides predicted glucose, two real-time indices are additionally provided—a crispness index, indicating good representation of current glucose behavior by a single model, and a normality index, allowing for the detection of an abnormal glucose behavior (unusual according to registered historical data). The framework is tested in a proof-of-concept in silico study with ten patients over four month training data and two independent two month validation datasets, with and without abnormal behaviors, from the distribution version of the UVA/Padova simulator extended with diverse sources of intra-patient variability.
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Faria, Álvaro José Gomes de, Sérgio Henrique Godinho Silva, Leônidas Carrijo Azevedo Melo, Renata Andrade, Marcelo Mancini, Luiz Felipe Mesquita, Anita Fernanda dos Santos Teixeira, Luiz Roberto Guimarães Guilherme, and Nilton Curi. "Soils of the Brazilian Coastal Plains biome: prediction of chemical attributes via portable X-ray fluorescence (pXRF) spectrometry and robust prediction models." Soil Research 58, no. 7 (2020): 683. http://dx.doi.org/10.1071/sr20136.
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Portable X-ray fluorescence (pXRF) spectrometry has been successfully used for soil attribute prediction. However, recent studies have shown that accurate predictions may vary according to soil type and environmental conditions, motivating investigations in different biomes. Hence, this work attempted to accurately predict soil pH, sum of bases (SB), cation exchange capacity (CEC) at pH 7.0 and base saturation (BS) using pXRF-obtained data with high variability and robust prediction models in the Brazilian Coastal Plains biome. A total of 285 soil samples were collected to generate prediction models for A (n = 123), B (n = 162) and A+B (n = 285) horizons through stepwise multiple linear regression, support vector machine with linear kernel (SVM) and random forest. Data were divided into calibration (75%) and validation (25%) sets. Accuracy of the predictions was assessed by coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and residual prediction deviation (RPD). The A+B horizons dataset had optimal performance, especially for SB predictions using SVM, achieving R2 = 0.82, RMSE = 1.02 cmolc dm–3, MAE = 1.17 cmolc dm–3 and RPD = 2.33. The most important predictor variable was Ca. Predictions using pXRF data were accurate especially for SB. Limitations of the predictions caused by soil classes and environmental conditions should be further investigated in other regions.
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Goldstein, Benjamin A., Michael J. Pencina, Maria E. Montez-Rath, and Wolfgang C. Winkelmayer. "Predicting mortality over different time horizons: which data elements are needed?" Journal of the American Medical Informatics Association 24, no. 1 (June 29, 2016): 176–81. http://dx.doi.org/10.1093/jamia/ocw057.
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Objective: Electronic health records (EHRs) are a resource for “big data” analytics, containing a variety of data elements. We investigate how different categories of information contribute to prediction of mortality over different time horizons among patients undergoing hemodialysis treatment. Material and Methods: We derived prediction models for mortality over 7 time horizons using EHR data on older patients from a national chain of dialysis clinics linked with administrative data using LASSO (least absolute shrinkage and selection operator) regression. We assessed how different categories of information relate to risk assessment and compared discrete models to time-to-event models. Results: The best predictors used all the available data (c-statistic ranged from 0.72–0.76), with stronger models in the near term. While different variable groups showed different utility, exclusion of any particular group did not lead to a meaningfully different risk assessment. Discrete time models performed better than time-to-event models. Conclusions: Different variable groups were predictive over different time horizons, with vital signs most predictive for near-term mortality and demographic and comorbidities more important in long-term mortality.
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Liu, Chengyuan, Josep Vehí, Parizad Avari, Monika Reddy, Nick Oliver, Pantelis Georgiou, and Pau Herrero. "Long-Term Glucose Forecasting Using a Physiological Model and Deconvolution of the Continuous Glucose Monitoring Signal." Sensors 19, no. 19 (October 8, 2019): 4338. http://dx.doi.org/10.3390/s19194338.
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(1) Objective: Blood glucose forecasting in type 1 diabetes (T1D) management is a maturing field with numerous algorithms being published and a few of them having reached the commercialisation stage. However, accurate long-term glucose predictions (e.g., >60 min), which are usually needed in applications such as precision insulin dosing (e.g., an artificial pancreas), still remain a challenge. In this paper, we present a novel glucose forecasting algorithm that is well-suited for long-term prediction horizons. The proposed algorithm is currently being used as the core component of a modular safety system for an insulin dose recommender developed within the EU-funded PEPPER (Patient Empowerment through Predictive PERsonalised decision support) project. (2) Methods: The proposed blood glucose forecasting algorithm is based on a compartmental composite model of glucose–insulin dynamics, which uses a deconvolution technique applied to the continuous glucose monitoring (CGM) signal for state estimation. In addition to commonly employed inputs by glucose forecasting methods (i.e., CGM data, insulin, carbohydrates), the proposed algorithm allows the optional input of meal absorption information to enhance prediction accuracy. Clinical data corresponding to 10 adult subjects with T1D were used for evaluation purposes. In addition, in silico data obtained with a modified version of the UVa-Padova simulator was used to further evaluate the impact of accounting for meal absorption information on prediction accuracy. Finally, a comparison with two well-established glucose forecasting algorithms, the autoregressive exogenous (ARX) model and the latent variable-based statistical (LVX) model, was carried out. (3) Results: For prediction horizons beyond 60 min, the performance of the proposed physiological model-based (PM) algorithm is superior to that of the LVX and ARX algorithms. When comparing the performance of PM against the secondly ranked method (ARX) on a 120 min prediction horizon, the percentage improvement on prediction accuracy measured with the root mean square error, A-region of error grid analysis (EGA), and hypoglycaemia prediction calculated by the Matthews correlation coefficient, was 18.8 % , 17.9 % , and 80.9 % , respectively. Although showing a trend towards improvement, the addition of meal absorption information did not provide clinically significant improvements. (4) Conclusion: The proposed glucose forecasting algorithm is potentially well-suited for T1D management applications which require long-term glucose predictions.
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Almarzooqi, Ameera M., Maher Maalouf, Tarek H. M. El-Fouly, Vasileios E. Katzourakis, Mohamed S. El Moursi, and Constantinos V. Chrysikopoulos. "A hybrid machine-learning model for solar irradiance forecasting." Clean Energy 8, no. 1 (January 10, 2024): 100–110. http://dx.doi.org/10.1093/ce/zkad075.
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Abstract Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic (PV) power plants. These plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power output. As the generation capacity within the electric grid increases, accurately predicting this output becomes increasingly essential, especially given the random and non-linear characteristics of solar irradiance under variable weather conditions. This study presents a novel prediction method for solar irradiance, which is directly in correlation with PV power output, targeting both short-term and medium-term forecast horizons. Our proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model. The proposed method excels in forecasting solar irradiance, especially during highly intermittent weather periods. A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable framework. We evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford, USA and compared it against three forecasting models: persistence, modified 24-hour persistence and least squares. Based on three widely accepted statistical performance metrics (root mean squared error, mean absolute error and coefficient of determination), our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.
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Fernández Pozo, Rubén, Ana Belén Rodríguez González, Mark Richard Wilby, and Juan José Vinagre Díaz. "Analysis of Extended Information Provided by Bluetooth Traffic Monitoring Systems to Enhance Short-Term Level of Service Prediction." Sensors 22, no. 12 (June 17, 2022): 4565. http://dx.doi.org/10.3390/s22124565.
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Bluetooth monitoring systems (BTMS) have opened a new era in traffic sensing, providing a reliable, economical, and easy-to-deploy solution to uniquely identify vehicles. Raw data from BTMS have traditionally been used to calculate travel time and origin–destination matrices. However, we could extend this to include other information like the number of vehicles or their residence times. This information, together with their temporal components, can be applied to the complex task of forecasting traffic. Level of service (LOS) prediction has opened a novel research line that fulfills the need to anticipate future traffic states, based on a standard link-based variable, accepted for both researchers and practitioners. In this paper, we incorporate BTMS’s extended variables and temporal information to an LOS classifier based on a Random Undersampling Boost algorithm, which is proven to efficiently respond to the data unbalance intrinsic to this problem. By using this approach, we achieve an overall recall of 87.2% for up to 15-min prediction horizons, reaching 96.6% predicting congestion, and improving the results for the intermediate traffic states, especially complex given their intrinsic instability. Additionally, we provide detailed analyses on the impact of temporal information on the LOS predictor’s performance, observing improvements up to a separation of 50 min between last features and prediction horizons. Furthermore, we study the predictor importance resulting from the classifiers to highlight those features contributing the most to the final achievements.
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Wang, Haowei, Kin On Kwok, and Steven Riley. "Forecasting influenza incidence as an ordinal variable using machine learning." Wellcome Open Research 9 (January 8, 2024): 11. http://dx.doi.org/10.12688/wellcomeopenres.19599.1.
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Background: Many mechanisms contribute to the variation in the incidence of influenza disease, such as strain evolution, the waning of immunity and changes in social mixing. Although machine learning methods have been developed for forecasting, these methods are used less commonly in influenza forecasts than statistical and mechanistic models. In this study, we applied a relatively new machine learning method, Extreme Gradient Boosting (XGBoost), to ordinal country-level influenza disease data. Methods: We developed a machine learning forecasting framework by adopting the XGBoost algorithm and training it with surveillance data for over 32 countries between 2010 and 2018 from the World Health Organisation’s FluID platform. We then used the model to predict incidence 1- to 4-week ahead. We evaluated the performance of XGBoost forecast models by comparing them with a null model and a historical average model using mean-zero error (MZE) and macro-averaged mean absolute error (mMAE). Results: The XGBoost models were consistently more accurate than the null and historical models for all forecast time horizons. For 1-week ahead predictions across test sets, the mMAE of the XGBoost model with an extending training window was reduced by 78% on average compared to the null model. Although the mMAE increased with longer prediction horizons, XGBoost models showed a 62% reduction in mMAE compared to the null model for 4-week ahead predictions. Our results highlight the potential utility of machine learning methods in forecasting infectious disease incidence when that incidence is defined as an ordinal variable. In particular, the XGBoost model can be easily extended to include more features, thus capturing complex patterns and improving forecast accuracy. Conclusion: Given that many natural extreme phenomena are often described on an ordinal scale when informing planning and response, these results motivate further investigation of using similar scales for communicating risk from infectious diseases.
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Zjavka, Ladislav. "Photovoltaic Energy All-Day and Intra-Day Forecasting Using Node by Node Developed Polynomial Networks Forming PDE Models Based on the L-Transformation." Energies 14, no. 22 (November 12, 2021): 7581. http://dx.doi.org/10.3390/en14227581.
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Forecasting Photovoltaic (PV) energy production, based on the last weather and power data only, can obtain acceptable prediction accuracy in short-time horizons. Numerical Weather Prediction (NWP) systems usually produce free forecasts of the local cloud amount each 6 h. These are considerably delayed by several hours and do not provide sufficient quality. A Differential Polynomial Neural Network (D-PNN) is a recent unconventional soft-computing technique that can model complex weather patterns. D-PNN expands the n-variable kth order Partial Differential Equation (PDE) into selected two-variable node PDEs of the first or second order. Their derivatives are easy to convert into the Laplace transforms and substitute using Operator Calculus (OC). D-PNN proves two-input nodes to insert their PDE components into its gradually expanded sum model. Its PDE representation allows for the variability and uncertainty of specific patterns in the surface layer. The proposed all-day single-model and intra-day several-step PV prediction schemes are compared and interpreted with differential and stochastic machine learning. The statistical models are evolved for the specific data time delay to predict the PV output in complete day sequences or specific hours. Spatial data from a larger territory and the initially recognized daily periods enable models to compute accurate predictions each day and compensate for unexpected pattern variations and different initial conditions. The optimal data samples, determined by the particular time shifts between the model inputs and output, are trained to predict the Clear Sky Index in the defined horizon.
Dissertations / Theses on the topic "Variable prediction horizons"
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Amor, Yasmine. "Ιntelligent apprοach fοr trafic cοngestiοn predictiοn." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMR129.
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La congestion routière constitue un défi majeur pour les zones urbaines, car le volume de véhicules continue de croître plus rapidement que la capacité globale du réseau routier. Cette croissance a des répercussions sur l'activité économique, la durabilité environnementale et la qualité de vie. Bien que des stratégies visant à atténuer la congestion routière ont connu des améliorations au cours des dernières décennies, de nombreux pays ont encore du mal à la gérer efficacement.Divers modèles ont été développés pour aborder ce problème. Cependant, les approches existantes peinent souvent à fournir des prédictions en temps réel et localisées qui peuvent s'adapter à des conditions de trafic complexes et dynamiques. La plupart de ces approches s'appuient sur des horizons de prédiction fixes et manquent de l'infrastructure intelligente nécessaire à la flexibilité. Cette thèse comble ces lacunes en proposant une approche intelligente, décentralisée et basée sur l'infrastructure pour l'estimation et la prédiction de la congestion routière.Nous commençons par étudier l'Estimation du Trafic. Nous examinons les mesures de congestion possibles et les sources de données requises pour différents contextes pouvant être étudiés. Nous établissons une relation tridimensionnelle entre ces axes. Un système de recommandation basé sur des règles est développé pour aider les chercheurs et les opérateurs du trafic à choisir les mesures de congestion les plus appropriées en fonction du contexte étudié.Nous passons ensuite à la Prédiction du Trafic, où nous introduisons notre approche DECOTRIVMS. Cette dernière utilise des panneaux intelligents à messages variables pour collecter des données detrafic en temps réel et fournir des prédictions à court terme avec des horizons de prédiction variables.Nous avons utilisé des Réseaux de Graphes avec Attention en raison de leur capacité à capturer des relations complexes et à gérer des données structurées en graphes. Ils sont bien adaptés pour modéliser les interactions entre différents segments routiers étudiés.Nous avons aussi employé des méthodes d'apprentissage en ligne, spécifiquement la Descente de Gradient Stochastique et la Descente de Gradient Adaptative. Bien que ces méthodes ont été utilisées avec succès dans divers autres domaines, leur application à la prédiction de la congestion routière reste sous-explorée. Dans notre thèse, nous visons à combler cette lacune en explorant leur efficacité dans le contexte de la prédiction de la congestion routière en temps réel.Enfin, nous validons l'efficacité de notre approche à travers deux études de cas réalisées à Mascate, Oman, et à Rouen, France. Une analyse comparative est effectuée, évaluant divers modèles de prédiction, y compris les Réseaux de Graphes avec Attention, les Réseaux de Graphes Convolutionnels et des méthodes d'apprentissage en ligne. Les résultats obtenus soulignent le potentiel de DECOTRIVMS, démontrant son efficacité pour une prédiction précise et efficace de la congestion routière dans divers contextes urbainsTraffic congestion presents a critical challenge to urban areas, as the volume of vehicles continues to grow faster than the system’s overall capacity. This growth impacts economic activity, environmental sustainability, and overall quality of life. Although strategies for mitigating traffic congestion have seen improvements over the past few decades, many cities still struggle to manage it effectively. While various models have been developed to tackle this issue, existing approaches often fall short in providing real-time, localized predictions that can adapt to complex and dynamic traffic conditions. Most rely on fixed prediction horizons and lack the intelligent infrastructure needed for flexibility. This thesis addresses these gaps by proposing an intelligent, decentralized, infrastructure-based approach for traffic congestion estimation and prediction.We start by studying Traffic Estimation. We examine the possible congestion measures and data sources required for different contexts that may be studied. We establish a three-dimensional relationship between these axes. A rule-based system is developed to assist researchers and traffic operators in recommending the most appropriate congestion measures based on the specific context under study. We then proceed to Traffic Prediction, introducing our DECentralized COngestion esTimation and pRediction model using Intelligent Variable Message Signs (DECOTRIVMS). This infrastructure-based model employs intelligent Variable Message Signs (VMSs) to collect real-time traffic data and provide short-term congestion predictions with variable prediction horizons.We use Graph Attention Networks (GATs) due to their ability to capture complex relationships and handle graph-structured data. They are well-suited for modeling interactions between different road segments. In addition to GATs, we employ online learning methods, specifically, Stochastic Gradient Descent (SGD) and ADAptive GRAdient Descent (ADAGRAD). While these methods have been successfully used in various other domains, their application in traffic congestion prediction remains under-explored. In our thesis, we aim to bridge that gap by exploring their effectiveness within the context of real-time traffic congestion forecasting.Finally, we validate our model’s effectiveness through two case studies conducted in Muscat, Oman, and Rouen, France. A comprehensive comparative analysis is performed, evaluating various prediction techniques, including GATs, Graph Convolutional Networks (GCNs), SGD and ADAGRAD. The achieved results underscore the potential of DECOTRIVMS, demonstrating its potential for accurate and effective traffic congestion prediction across diverse urban contexts
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Shekhar, Rohan Chandra. "Variable horizon model predictive control : robustness and optimality." Thesis, University of Cambridge, 2012. https://www.repository.cam.ac.uk/handle/1810/244210.
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Variable Horizon Model Predictive Control (VH-MPC) is a form of predictive control that includes the horizon length as a decision variable in the constrained optimisation problem solved at each iteration. It has been recently applied to completion problems, where the system state is to be steered to a closed set in finite time. The behaviour of the system once completion has occurred is not considered part of the control problem. This thesis is concerned with three aspects of robustness and optimality in VH-MPC completion problems. In particular, the thesis investigates robustness to well defined but unpredictable changes in system and controller parameters, robustness to bounded disturbances in the presence of certain input parameterisations to reduce computational complexity, and optimal robustness to bounded disturbances using tightened constraints. In the context of linear time invariant systems, new theoretical contributions and algorithms are developed. Firstly, changing dynamics, constraints and control objectives are addressed by introducing the notion of feasible contingencies. A novel algorithm is proposed that introduces extra prediction variables to ensure that anticipated new control objectives are always feasible, under changed system parameters. In addition, a modified constraint tightening formulation is introduced to provide robust completion in the presence of bounded disturbances. Different contingency scenarios are presented and numerical simulations demonstrate the formulation’s efficacy. Next, complexity reduction is considered, using a form of input parameterisation known as move blocking. After introducing a new notation for move blocking, algorithms are presented for designing a move-blocked VH-MPC controller. Constraints are tightened in a novel way for robustness, whilst ensuring that guarantees of recursive feasibility and finite-time completion are preserved. Simulations are used to illustrate the effect of an example blocking scheme on computation time, closed-loop cost, control inputs and state trajectories. Attention is now turned towards mitigating the effect of constraint tightening policies on a VH-MPC controller’s region of attraction. An optimisation problem is formulated to maximise the volume of an inner approximation to the region of attraction, parameterised in terms of the tightening policy. Alternative heuristic approaches are also proposed to deal with high state dimensions. Numerical examples show that the new technique produces substantially improved regions of attraction in comparison to other proposed approaches, and greatly reduces the maximum required prediction horizon length for a given application. Finally, a case study is presented to illustrate the application of the new theory developed in this thesis to a non-trivial example system. A simplified nonlinear surface excavation machine and material model is developed for this purpose. The model is stabilised with an inner-loop controller, following which a VH-MPC controller for autonomous trajectory generation is designed using a discretised, linearised model of the stabilised system. Realistic simulated trajectories are obtained from applying the controller to the stabilised system and incorporating the ideas developed in this thesis. These ideas improve the applicability and computational tractability of VH-MPC, for both traditional applications as well as those that go beyond the realm of vehicle manœuvring.
Book chapters on the topic "Variable prediction horizons"
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Huisman, Mischa, and Erjen Lefeber. "Online Motion Planning for All-Wheel Drive Autonomous Race Cars." In Lecture Notes in Mechanical Engineering, 185–92. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_27.
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AbstractThe advent of autonomous racing events, such as Formula Student Driverless Cup, requires online motion planning algorithms that push the vehicle to its limits while ensuring vehicle stability and preventing road departure. A popular method to find the optimal control input to drive at the limits of the car is Nonlinear Model Predictive Control (NMPC). However, when NMPC is used, often a trade-off has to be made between performance, accuracy, and computational complexity. In this manuscript, the principle of cascading different vehicle models is used to construct the prediction horizon. Initially, a two-track model optimizes steering and motor input, utilizing torque vectoring benefits. The horizon is then extended with a single-track model, and a lower fidelity point mass model, effectively reducing computational complexity. Furthermore, by adopting a curvilinear reference frame, a transformation towards the spatial domain is obtained, which allows us to use time as an optimization variable. A simulation study is performed for varying prediction horizon lengths which show the advantages of the cascaded vehicle model, achieving an 86% reduction in computation time with comparable lap times.
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Hatanaka, Takeshi, Teruki Yamada, Masayuki Fujita, Shigeru Morimoto, and Masayuki Okamoto. "Explicit Receding Horizon Control of Automobiles with Continuously Variable Transmissions." In Nonlinear Model Predictive Control, 561–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_46.
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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, 632–38. Cham: 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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De Nicolao, G., and R. Scattolini. "Properties of MBPC Algorithms." In Advances in Model-Based Predictive Control, 103–69. Oxford University PressOxford, 1994. http://dx.doi.org/10.1093/oso/9780198562924.003.0002.
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Abstract This paper deals with the stability properties of predictive control with terminal constraints on the output and input variables. Specifically, in predictive control with terminal constraints on the controlled variable is forced to match a reference value over a given intraval at the end of the pred1ct1on horizon, while the control variable is allowed to have a restricted number of projected control variations. Both the classical receding horizon approach, where only the first projected control increment _is applied, and the so-called intervalwise receding horizon strategy, where a number of future control increments are effectively applied, are considered. A preliminary investigation of the robustness properties of receding horizon control is performed by exploiting its strict relations with infinite horizon Linear Quadratic control.
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Lima, Rodrigo de Souza, Leonardo Azevedo Scárdua, and Gustavo Maia de Almeida. "Predicting temperatures inside a steel slab reheating furnace using Deep Learning." In A LOOK AT DEVELOPMENT. Seven Editora, 2023. http://dx.doi.org/10.56238/alookdevelopv1-016.
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Due to the complexity and high financial costs involved in production processes, the steel industry can benefit from applications of intelligent systems, capable of performing automated activities. This research paper addresses a description of the process of creating a data-driven computational system to develop a computational thermal model of a real steel plate reheating furnace. Sufficiently accurate computational models can be used in conjunction with combustion control optimization techniques, such as model-based predictive control (MPC), or even a Digital Twin of the combustion system of a plate reheating furnace. The tool can be used in predictive failure diagnosis, fundamental for the maintenance and operation teams responsible for asset management. For this development, Recurrent Artificial Neural Networks have been widely applied, validating the existence of series that have temporal links between their samples, a typical case of monitoring industrial process variables. To meet the proposed objective, the performance of models based on recurrent neural networks of the Long Short Term-Memory (LSTM), Gated Recurrent Unit (GRU), and Temporal Convolutional Network (TCN) type was analyzed. The results were evaluated under different prediction horizons, since such techniques demand models capable of accurate predictions that are several steps ahead, premised on prediction capability.
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"Cash Management." In Decision and Prediction Analysis Powered With Operations Research, 209–21. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-4179-7.ch011.
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The Miller-Orr cash management model is presented. Every day, if the cash on hand exceeds an upper limit U, enough cash is transferred into a money market to decrease the cash level to a target T. If the cash on hand is below a minimum level M, enough cash is transferred out of the money market to bring the cash level up to the target T. The cash flow is normally distributed with a zero mean and a standard deviation of $10,000. The decision variables are U and T. Minimum level M is set to $15,000. The objective is to maximize the mean profit over the time horizon, where profit is interest on the money market minus the transaction's costs. The constraints are that M <= T <= U. T and U should be round numbers with discrete options on adjustable cells, using a step size of $100.
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Martínez, Blanca, Javier Sanchis, and Sergio Garcia-Nieto. "A model independent constrained predictive control for the Furuta Pendulum." In XLIV Jornadas de Automática: libro de actas: Universidad de Zaragoza, Escuela de Ingeniería y Arquitectura, 6, 7 y 8 de septiembre de 2023, Zaragoza, 323–28. 2023rd ed. Servizo de Publicacións. Universidade da Coruña, 2023. http://dx.doi.org/10.17979/spudc.9788497498609.323.
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Este trabajo presenta el control de seguimiento de un péndulo Furuta basado en el diseño de dos lazos paralelos con restricciones, rechazo activo de perturbaciones y predicción de salidas. Para cada subsistema, se asume que la planta gobernada tiene una dinámica de primer orden más integrador (FOPI). Así, se calcula una ley de control predictivo aplicando una estrategia de horizonte deslizante en dicha planta (FOPI). La diferencia entre la dinámica real y el modelo de predicción asumido se compensa mediante el mecanismo de rechazo de perturbaciones heredado del control activo de rechazo de perturbaciones (ADRC) e incorporado en los lazos. Para el diseño del control no se realiza ninguna identificación de modelos ni linealización matemática. Además, la estrategia permite incorporar las restricciones reales del sistema. Este trabajo valida con resultados prometedores la arquitectura denominada Modified Active Disturbance Rejection Predictive Control (MADRPC) mediante la estabilización de un sistema mecánico subactuado considerando las variables restringidas y en ausencia de un modelo nominal, en contraste con el enfoque estándar en el Control Predictivo de Modelos (MPC) en espacio de estados.
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Bandyopadhyay, Arindam. "Matrix Algebra and their Application in Risk Prediction and Risk Monitoring." In Basic Statistics for Risk Management in Banks and Financial Institutions, 119–40. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192849014.003.0005.
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Matrix Algebra concept and its numerous application in the measurement of credit risk as well as market risk have been elaborated in this chapter. A matrix is a rectangular array of elements. The transition matrix derived from the concept of matrix algebra has numerous applications in predicting bond valuation, value at risk analysis, and loan portfolio monitoring. The Markov chain process is used by reputed rating agencies and also the best practiced banks to predict probabilities of rating migration including analysis of default risk. It also enables a bank to estimate PD for different horizons and derive loan level expected loss for risk provisioning. The estimated PD is also helpful to detect significant increase in credit risk and estimate unexpected loss. The variance–covariance matrix is used in solving multiple regression equations and to find out the regression coefficient. This chapter also explains its role in estimating total portfolio risk computation.
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Kumar, Rajendra, Surbhit Shukla, and C. S. Raghuvanshi. "Deep Learning Models for Predicting High and Low Tides With Gravitational Analysis." In Practice, Progress, and Proficiency in Sustainability, 35–46. IGI Global, 2023. http://dx.doi.org/10.4018/979-8-3693-1722-8.ch003.
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In this chapter, many forecasting models were established, and each model was produced in three forms, each with a distinct machine learning algorithm: regression, random forest, and artificial neural network. All of the presented models performed well in predicting the tide level at Visakhapatnam port. All models based on supervised learning had an accuracy of 85 to 9%, with a relative absolute error of 5 to 7.5. Finally, with a forecast horizon of several hours, satisfactory results were obtained, and a further specific comparison revealed that the models based on the considered machine learning algorithms outperform the auto learning integrated evaluation models with exogenous input variables in forecasting high/low tides.
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Zickler Stefan and Veloso Manuela. "Variable Level-Of-Detail Motion Planning in Environments with Poorly Predictable Bodies." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-606-5-189.
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Motion planning in dynamic environments consists of the generation of a collision-free trajectory from an initial to a goal state. When the environment contains uncertainty, preventing a perfect predictive model of its dynamics, a robot ends up only successfully executing a short part of the plan and then requires replanning, using the latest observed state of the environment. Each such replanning step is computationally expensive. Furthermore, we note that such sophisticated planning effort is unnecessary as the resulting plans are not likely to ever be fully executed, due to an unpredictable and changing environment. In this paper, we introduce the concept of Variable Level-Of-Detail (VLOD) planning, that is able to focus its search on obtaining accurate short-term results, while considering the far-future with a different level of detail, selectively ignoring the physical interactions with poorly predictable dynamic objects (e.g., other mobile bodies that are controlled by external entities). Unlike finite-horizon planning, which limits the maximum search depth, VLOD planning deals with local minima and generates full plans to the goal, while requiring much less computation than traditional planning. We contribute VLOD planning on a rich simulated physics-based planner and show results for varying LOD thresholds and replanning intervals.
Conference papers on the topic "Variable prediction horizons"
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Ngo, Tri, and Cornel Sultan. "Towards Automation of Helicopter Landings on Ship Decks Using Integer Programming and Model Predictive Control." In Vertical Flight Society 80th Annual Forum & Technology Display, 1–9. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0074-2018-12783.
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A novel method for the automated control of helicopters in landing maneuvers on ship decks is proposed, which combines integer programming and model predictive control (MPC). The helicopter is first brought sufficiently close to the landing deck using a standard MPC. In the final phase of the mission, termed the rendezvous phase, implementation of the novel control design method allows the MPC to rapidly adapt to the landing deck state via a variable prediction horizon. The control design problem includes an integer variable vector which is used to frequently estimate an appropriate prediction horizon for the MPC during the maneuver. This process mimics the manner in which human pilots act, by repeatedly estimating how long ahead during the ship landing maneuver their actions are effective to safely accomplish the mission. The performance of the proposed procedure is evaluated via numerical simulations using a nonlinear helicopter-ship dynamics interface that captures significant characteristics of the helicopter and ship dynamics, as well as the ship airwake effects. These simulations show that the proposed method can be effective in satisfying rendezvous conditions for helicopter ship landing even when the moving deck has a very high energy index.
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Xiong, Weiliang, Xiangjun Xia, Haiping Du, and Defeng He. "A Two-Stage Variable-Horizon Economic Model Predictive Control without Terminal Constraint." In 2024 IEEE 63rd Conference on Decision and Control (CDC), 4791–97. IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886727.
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Kellermann, Christoph, Eric Neumann, and Joern Ostermann. "Prediction of variable forecast horizons with artificial neural networks by embedding the temporal resolution warping." In 2022 International Conference on Control, Automation and Diagnosis (ICCAD). IEEE, 2022. http://dx.doi.org/10.1109/iccad55197.2022.9853884.
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Dussi, Simone, Ryvo Octaviano, and Pejman Shoeibi Omrani. "Bayesian Networks Applied to ESP Performance Monitoring and Forecasting." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210495-ms.
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Abstract Predicting the degradation of the Electrical Submersible Pumps (ESP) performance well in advance to avoid its failure is a rewarding yet challenging task. The complex interdependencies with well-reservoir properties and between machine components influence ESP performance and ultimately can lead to different types of failure. Predictions based on typical data-driven approaches are hampered by data quality and lack of variations in the operating conditions. In this study, Bayesian Networks (BNs), where expert knowledge can be incorporated and prediction uncertainty can be easily assessed, were tested as a potential method for performance monitoring and forecasting of a single ESP based on condition monitoring and production data. Different BNs based on auto-learned structures (relations being learned by the machine learning model) and expert specified structures were evaluated in the project. Network variables were selected from a dataset containing time-series sensors data (Pressure, Temperature, Frequency, Voltage, Current, etc.) for ESPs which degraded due to different reasons, including electrical failures and well conditions. The pump hydraulic efficiency, an indicator for the machine health, was used as a target variable. We report the results for a selected case where the ESP failed due to electrical failure (downhole ground fault). The hydraulic efficiency showed a noisy unsteady decreasing trend for several months before the failure. A sliding-window forecasting of the pump efficiency was performed with time horizons varying from few hours to several weeks. Based on only 4 sensors (current, two pressures, vibrations), the results between BNs with different structures were compared. The effects of adding additional variables (such as motor temperature or flowline pressure) to the network were also studied. A small user-defined BN was able to predict the pump hydraulic efficiency with an average absolute error ranging from 1.1% for the next 48h to 2.5% for the next 10 days. The novelty of this study is the application of BNs to ESP performance monitoring and forecasting. Within this framework, expert knowledge can be included by explicitly defining the causality between variables, suggesting a way to enhance data-driven methods. Since the prediction is probabilistic, the confidence in the predicted value can be straightforwardly assessed.
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Alevras, Ilias, Petros Karamanakos, Stefanos Manias, and Ralph Kennel. "Variable switching point predictive torque control with extended prediction horizon." In 2015 IEEE International Conference on Industrial Technology (ICIT). IEEE, 2015. http://dx.doi.org/10.1109/icit.2015.7125445.
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Li, Jiahui, Jian Zhang, and Bo Wang. "Cooperative Control Strategy for Variable Speed Limit and Dynamic Hard Shoulder Running of Highway On-Ramp Merging Area." In 2024 International Conference on Smart Transportation Interdisciplinary Studies. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2025. https://doi.org/10.4271/2025-01-7207.
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<div class="section abstract"><div class="htmlview paragraph">To meet the traffic control demands of highway merging areas and address the accuracy error of traffic flow prediction models, a cooperative control strategy based on adaptive prediction horizon Model Predictive Control (MPC) has been proposed for variable speed limits (VSL) and dynamic hard shoulder running (HSR). Firstly, the METANET model was improved based on the characteristics of merging areas and the impact of cooperative control strategy. Secondly, to mitigate the negative impact of the METANET prediction errors on control effectiveness, a fuzzy rule-based adaptive prediction horizon controller is designed. Thirdly, a cooperative control strategy for VSL and dynamic HSR is formulated under the MPC framework, aiming to optimize Total Time Spent(TTS)and Total Travel Distance (TTD), using genetic algorithms equipped with sliding time windows for resolution. Finally, using actual traffic flow data from Changtai Highway, simulation experiments are conducted, involving four scenarios: HSR-VSL control, VSL-only control, HSR-only control, and no control. In the cooperative control scenario, both adaptive and fixed prediction horizon approaches are considered. Results show that the proposed HSR-VSL control strategy with fixed prediction horizon reduces the total travel time and mainline density by 20.02% and 10.78% respectively, outperforming single strategies (only HSR or VSL). Compared to a fixed prediction horizon, the VSL-HSR with adaptive prediction horizon delivers even better results, reducing total travel time and mainline density by 24.53% and 12.94% respectively, proving the effectiveness of the cooperative control strategy and the adaptive prediction horizon controller.</div></div>
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González, Cristóbal, and Alejandro Angulo. "Multistep–Finite–Control–Set Model Predictive Control with Variable–Step Prediction Horizon." In 2023 IEEE 8th Southern Power Electronics Conference (SPEC). IEEE, 2023. http://dx.doi.org/10.1109/spec56436.2023.10408051.
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Ali, Ahmed M., and Dirk Söffker. "Real-Time Applicable Power Management of Multi-Source Fuel Cell Vehicles Using Situation-Based Model Predictive Control." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22383.
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Abstract Power management in all-electric powertrains has a significant potential to optimally handle the limited energy and power density of electric power sources. Situation-based power management strategies (SB-PMSs), defining optimized solutions related to specific vehicle situations, offer the ability to reduce computational requirements and enhance the solution optimality of simple rule-based algorithms. Moreover, the local optimality of SB-PMSs can be addressed by considering online optimization of the situated solutions for limited horizons. This paper presents a novel PMSs using model predictive control (MPC) to define optimal control strategies based on situated solutions for fuel cell hybrid vehicles. Vehicle states are defined in terms of multiple characteristic variables and power management decisions are optimized offline for each vehicle states. Prediction of vehicle states is conducted using statistical predictive model based on state transitions in a number of driving cycles. Preoptimized solutions related to predicted states are iterated online to achieve better optimality over the look-ahead horizon. Results analysis from online testing revealed the ability of SB-MPC to improve the optimality of situation-based solutions and hence reduce total energy cost in different driving cycles.
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Lee, Tae-Kyung, and Zoran S. Filipi. "Control Oriented Modeling and Nonlinear Model Predictive Control of Advanced SI Engine System." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4024.
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Control oriented model (COM) using crank-angle resolved flame propagation simulation and nonlinear model predictive control (NMPC) methodology for the purpose of transient control of HDOF engines are proposed in this paper. The nonlinear nature of the combustion process has been a challenge in building a reliable COM and engine simulation. Artificial neural networks (ANNs) are subsequently trained on the data generated with a quasi-D combustion model to create fast surrogate combustion models. System dynamics are augmented by manifold and actuator dynamics models. Then, NMPC for an internal combustion (IC) engine with a dual-independent variable valve timing (VVT) system is designed to achieve fast torque responses, to eliminate exhaust emissions penalty, and to track the optimal actuator response closely. The NMPC significantly improves engine dynamics and minimizes excursions of in-cylinder variables under highly transient operation. Dead-beat like control is achieved with selected prediction horizon and control horizon in the NMPC.
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Fernando, Eranga, Syed Imtiaz, Salim Ahmed, Kevin Murrant, Robert Gash, Mohammed Islam, and Hasanat Zaman. "Obstacle Avoidance Nonlinear Model Predictive Controller for Autonomous Surface Vessels With Variable Sampling Time Prediction." In ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/omae2024-126778.
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Abstract This study proposes an innovative nonlinear model predictive control (NMPC) algorithm developed for obstacle avoidance in trajectory tracking of autonomous surface vessels (ASV). The proposed algorithm extends the prediction horizon to enhance situational awareness and enable the controller to calculate the best control actions. The novelty of the proposed algorithm is that it modifies the duration of the prediction by dynamically varying the prediction sampling time. The controller scans along the reference trajectory for potential obstacles and adjusts the prediction sampling time based on the vessel speed and the obstacle size. The simulation results show that the proposed algorithm improves the consistency of the execution time compared to the conventional NMPC and exhibits improved trajectory tracking with less speed variations.
Reports on the topic "Variable prediction horizons"
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Clements, Michael, Robert W. Rich, and Joseph Tracy. An Investigation into the Uncertainty Revision Process of Professional Forecasters. Federal Reserve Bank of Cleveland, September 2024. http://dx.doi.org/10.26509/frbc-wp-202419.
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Following Manzan (2021), this paper examines how professional forecasters revise their fixed-event uncertainty (variance) forecasts and tests the Bayesian learning prediction that variance forecasts should decrease as the horizon shortens. We show that Manzan's (2021) use of first moment "efficiency" tests are not applicable to studying revisions of variance forecasts. Instead, we employ monotonicity tests developed by Patton and Timmermann (2012) in the first application of these tests to second moments of survey expectations. We find strong evidence that the variance forecasts are consistent with the Bayesian learning prediction of declining monotonicity
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Shaver, Greg, and Miles Droege. Develop and Deploy a Safe Truck Platoon Testing Protocol for the Purdue ARPA-E Project in Indiana. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317314.
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Hilly terrain poses challenges to truck platoons using fixed set speed cruise control. Driving the front truck efficiently on hilly terrain improves both trucks fuel economies and improves gap maintenance between the trucks. An experimentally-validated simulation model was used to show fuel savings for the platoon of 12.3% when the front truck uses long horizon predictive cruise control (LH-PCC), 8.7% when the front truck uses flexible set speed cruise control, and only 1.2% when the front truck uses fixed set speed cruise control. Purdue, Peloton, and Cummins have jointly configured two Peterbilt 579 trucks for relevant combinations of: (1) coordinated shifting, (2) constant or variable platoon gap controls, (3) flexible or constant speed setpoint cruise control of the front trucks, and (4) long-horizon predictive cruise control (LHPCC) of the front truck. Confirmation of this functionality during platooning was demonstrated at the Continental Test track in Uvalde, Texas. In Indiana, on-road experiments were limited to single truck operation with long-horizon predictive cruise control, flexible set speed cruise control, and constant setpoint cruise control. Data from all of the above was used to improve the fidelity of simulations used to arrive at the fuel savings and gap control findings for hilly terrain per what is summarized in the findings section. Additionally, in early summer 2020, Purdue submitted to, and received improvement from, INDOT for a safe truck platoon testing protocol (located in this report’s appendix), which could not be implemented in Indiana before the end of the project because of COVID-19. Presentations of the subject matter at COMVEC, MAASTO, Purdue Road School, and the Work Truck Show are listed in the appendix.