Relevant bibliographies by topics / Forecasting of the state of systems

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Forecasting of the state of systems'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Forecasting of the state of systems"

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Kureichik, V. M., Ye S. Sinyutin, and T. G. Kaplunov. "FORECASTING THE STATE OF TECHNICAL SYSTEMS USING GENETIC ALGORITHMS." Vestnik of Ryazan State Radio Engineering University 65 (2018): 107–12. http://dx.doi.org/10.21667/1995-4565-2018-65-3-107-112.

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Musaev, A. A., and D. A. Grigoriev. "MACHINE LEARNING BASED CYBER-PHYSICAL SYSTEMS FOR FORECASTING STATE OF UNSTABLE SYSTEMS." Mathematical Methods in Technologies and Technics, no. 7 (2021): 95–103. http://dx.doi.org/10.52348/2712-8873_mmtt_2021_7_95.

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Geetha, Sreenath Jayakumar, Saikat Chakrabarti, Ketan Rajawat, and Vladimir Terzija. "An Asynchronous Decentralized Forecasting-Aided State Estimator for Power Systems." IEEE Transactions on Power Systems 34, no. 4 (July 2019): 3059–68. http://dx.doi.org/10.1109/tpwrs.2019.2896601.

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Tina, Giuseppe Marco, Cristina Ventura, Sergio Ferlito, and Saverio De Vito. "A State-of-Art-Review on Machine-Learning Based Methods for PV." Applied Sciences 11, no. 16 (August 17, 2021): 7550. http://dx.doi.org/10.3390/app11167550.

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In the current era, Artificial Intelligence (AI) is becoming increasingly pervasive with applications in several applicative fields effectively changing our daily life. In this scenario, machine learning (ML), a subset of AI techniques, provides machines with the ability to programmatically learn from data to model a system while adapting to new situations as they learn more by data they are ingesting (on-line training). During the last several years, many papers have been published concerning ML applications in the field of solar systems. This paper presents the state of the art ML models applied in solar energy’s forecasting field i.e., for solar irradiance and power production forecasting (both point and interval or probabilistic forecasting), electricity price forecasting and energy demand forecasting. Other applications of ML into the photovoltaic (PV) field taken into account are the modelling of PV modules, PV design parameter extraction, tracking the maximum power point (MPP), PV systems efficiency optimization, PV/Thermal (PV/T) and Concentrating PV (CPV) system design parameters’ optimization and efficiency improvement, anomaly detection and energy management of PV’s storage systems. While many review papers already exist in this regard, they are usually focused only on one specific topic, while in this paper are gathered all the most relevant applications of ML for solar systems in many different fields. The paper gives an overview of the most recent and promising applications of machine learning used in the field of photovoltaic systems.
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Magnusson, L., E. Källén, and J. Nycander. "Initial state perturbations in ensemble forecasting." Nonlinear Processes in Geophysics 15, no. 5 (October 21, 2008): 751–59. http://dx.doi.org/10.5194/npg-15-751-2008.

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Abstract. Due to the chaotic nature of atmospheric dynamics, numerical weather prediction systems are sensitive to errors in the initial conditions. To estimate the forecast uncertainty, forecast centres produce ensemble forecasts based on perturbed initial conditions. How to optimally perturb the initial conditions remains an open question and different methods are in use. One is the singular vector (SV) method, adapted by ECMWF, and another is the breeding vector (BV) method (previously used by NCEP). In this study we compare the two methods with a modified version of breeding vectors in a low-order dynamical system (Lorenz-63). We calculate the Empirical Orthogonal Functions (EOF) of the subspace spanned by the breeding vectors to obtain an orthogonal set of initial perturbations for the model. We will also use Normal Mode perturbations. Evaluating the results, we focus on the fastest growth of a perturbation. The results show a large improvement for the BV-EOF perturbations compared to the non-orthogonalised BV. The BV-EOF technique also shows a larger perturbation growth than the SVs of this system, except for short time-scales. The highest growth rate is found for the second BV-EOF for the long-time scale. The differences between orthogonal and non-orthogonal breeding vectors are also investigated using the ECMWF IFS-model. These results confirm the results from the Loernz-63 model regarding the dependency on orthogonalisation.
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Romanenko, Іgor, Andrii Golovanov, Vitalii Khoma, Andrii Shyshatskyi, Yevhen Demchenko, Lyubov Shabanova-Kushnarenko, Tetiana Ivakhnenko, Oleksandr Prokopenko, Oleh Havaliukh, and Dmitrо Stupak. "Development of estimation and forecasting method in intelligent decision support systems." Eastern-European Journal of Enterprise Technologies 2, no. 4 (110) (April 30, 2021): 38–47. http://dx.doi.org/10.15587/1729-4061.2021.229160.

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The method of estimation and forecasting in intelligent decision support systems is developed. The essence of the proposed method is the ability to analyze the current state of the object under analysis and the possibility of short-term forecasting of the object state. The possibility of objective and complete analysis is achieved through the use of improved fuzzy temporal models of the object state, an improved procedure for forecasting the object state and an improved procedure for training evolving artificial neural networks. The concepts of a fuzzy cognitive model, in contrast to the known fuzzy cognitive models, are connected by subsets of fuzzy influence degrees, arranged in chronological order, taking into account the time lags of the corresponding components of the multidimensional time series. This method is based on fuzzy temporal models and evolving artificial neural networks. The peculiarity of this method is the ability to take into account the type of a priori uncertainty about the state of the analyzed object (full awareness of the object state, partial awareness of the object state and complete uncertainty about the object state). The ability to clarify information about the state of the monitored object is achieved through the use of an advanced training procedure. It consists in training the synaptic weights of the artificial neural network, the type and parameters of the membership function, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The object state forecasting procedure allows conducting multidimensional analysis, consideration and indirect influence of all components of a multidimensional time series with different time shifts relative to each other under uncertainty.
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Mahdi, Qasim Abbood, Andrii Shyshatskyi, Yevgen Prokopenko, Tetiana Ivakhnenko, Dmytro Kupriyenko, Vira Golian, Roman Lazuta, Serhii Kravchenko, Nadiia Protas, and Alexander Momit. "Development of estimation and forecasting method in intelligent decision support systems." Eastern-European Journal of Enterprise Technologies 3, no. 9(111) (June 30, 2021): 51–62. http://dx.doi.org/10.15587/1729-4061.2021.232718.

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The method of estimation and forecasting in intelligent decision support systems was developed. The essence of the method is the analysis of the current state of the object and short-term forecasting of the object state. Objective and complete analysis is achieved by using improved fuzzy temporal models of the object state and an improved procedure for processing the original data under uncertainty. Also, the possibility of objective and complete analysis is achieved through an improved procedure for forecasting the object state and an improved procedure for learning evolving artificial neural networks. The concepts of fuzzy cognitive model are related by subsets of influence fuzzy degrees, arranged in chronological order, taking into account the time lags of the corresponding components of the multidimensional time series. The method is based on fuzzy temporal models and evolving artificial neural networks. The peculiarity of the method is the possibility of taking into account the type of a priori uncertainty about the object state (full awareness of the object state, partial awareness of the object state and complete uncertainty about the object state). The possibility to clarify information about the object state is achieved using an advanced training procedure. It consists in training the synaptic weights of the artificial neural network, the type and parameters of the membership function, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The object state forecasting procedure allows conducting multidimensional analysis, consideration, and indirect influence of all components of a multidimensional time series with their different time shifts relative to each other under uncertainty. The method provides an increase in data processing efficiency at the level of 15–25% using additional advanced procedures.
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Paulescu, Marius, Oana Mares, Ciprian Dughir, and Eugenia Paulescu. "Nowcasting the Output Power of PV Systems." E3S Web of Conferences 61 (2018): 00010. http://dx.doi.org/10.1051/e3sconf/20186100010.

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This paper presents an innovative procedure for nowcasting the energy production of PV systems. The procedure is relayed on a new version of two-state model for forecasting solar irradiance at ground level and a simplified description of the PV system. The results of testing the proposed procedure against on field measured data are discussed. Generally, the proposed procedure demonstrates a better performance than the main competitor based on ARIMA forecasting of the clearness index.
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Hayes, Barry Patrick, and Milan Prodanovic. "State Forecasting and Operational Planning for Distribution Network Energy Management Systems." IEEE Transactions on Smart Grid 7, no. 2 (March 2016): 1002–11. http://dx.doi.org/10.1109/tsg.2015.2489700.

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Wang, Jianzhou, Chunying Wu, and Tong Niu. "A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network." Sustainability 11, no. 2 (January 19, 2019): 526. http://dx.doi.org/10.3390/su11020526.

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Given the rapid development and wide application of wind energy, reliable and stable wind speed forecasting is of great significance in keeping the stability and security of wind power systems. However, accurate wind speed forecasting remains a great challenge due to its inherent randomness and intermittency. Most previous researches merely devote to improving the forecasting accuracy or stability while ignoring the equal significance of improving the two aspects in application. Therefore, this paper proposes a novel hybrid forecasting system containing the modules of a modified data preprocessing, multi-objective optimization, forecasting, and evaluation to achieve the wind speed forecasting with high precision and stability. The modified data preprocessing method can obtain a smoother input by decomposing and reconstructing the original wind speed series in the module of data preprocessing. Further, echo state network optimized by a multi-objective optimization algorithm is developed as a predictor in the forecasting module. Finally, eight datasets with different features are used to validate the performance of the proposed system using the evaluation module. The mean absolute percentage errors of the proposed system are 3.1490%, 3.0051%, 3.0618%, and 2.6180% in spring, summer, autumn, and winter, respectively. Moreover, the interval prediction is complemented to quantitatively characterize the uncertainty as developing intervals, and the mean average width is below 0.2 at the 95% confidence level. The results demonstrate the proposed forecasting system outperforms other comparative models considered from the forecasting accuracy and stability, which has great potential in the application of wind power systems.
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Dissertations / Theses on the topic "Forecasting of the state of systems"

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Li, Ying. "Forecasting Long Term Highway Staffing Requirements for State Transportation Agencies." UKnowledge, 2016. http://uknowledge.uky.edu/ce_etds/42.

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The transportation system is vital to the nation’s economic growth and stability, as it provides mobility for commuters while supporting the United States’ ability to compete in an increasingly competitive global economy. State Transportation Agencies across the country continue to face many challenges to repair and enhance highway infrastructure to meet the rapid increasing transportation needs. One of these challenges is maintaining an adequate and efficient agency staff. In order to effectively plan for future staffing levels, State Transportation Agencies need a method for forecasting long term staffing requirements. However, current methods in use cannot function without well-defined projects and therefore making long term forecasts is difficult. This dissertation seeks to develop a dynamic model which captures the feedback mechanisms within the system that determines highway staffing requirements. The system dynamics modeling methodology was used to build the forecasting model. The formal model was based on dynamic hypotheses derived from literature review and interviews with transportation experts. Both qualitative and quantitative data from literature, federal and state database were used to support the values and equations in the model. The model integrates State Transportation Agencies’ strategic plans, funding situations and workforce management strategies while determining future workforce requirements, and will hopefully fill the absence of long-term staffing level forecasting tools at State Transportation Agencies. By performing sensitivity simulations and statistical screening on possible drivers of the system behavior, the dynamic impacts of desired highway pavement performance level, availability of road fund and bridge fund on the required numbers of Engineers and Technicians throughout a 25-year simulation period were closely examined. Staffing strategies such as recruiting options (in-house vs. consultants) and hiring levels (entry level vs. senior level) were tested. Finally the model was calibrated using input data specific to Kentucky to simulate an expected retirement wave and search for solutions to address temporary staffing shortage.
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Quinn, Niall. "Forecasting of ocean state in a complex estuarine environment : the Solent-Southampton Water Estuarine System." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/359671/.

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Coastal flooding is a natural hazard causing devastation to many regions throughout the world, induced by the coincidence of high spring tides, large storm surges and waves. To reduce the risk posed by coastal inundation, warning systems have been developed to enable preparations to an expected threat. Although current operational predictions provide invaluable warnings, uncertainty in model formulations and input datasets, can lead to errors in forecasts. In order to provide coastal managers with the best possible information with which to make decisions, recent research has begun to focus on the movement from deterministic to probabilistic forecasting, which aims to explicitly account for uncertainty in the system. This research described the implementation of a regional tide-surge-wave model for the Solent-Southampton Water estuarine system, a region that is likely to experience increased risk of coastal flooding in the coming century. The accuracy of the model predictions were examined relative to in-situ measurements and those obtained from independent systems. Using the model, sources of error were examined and their effects upon the model predictions quantified, with particular reference made to the spatial variability throughout the region. In light of recent research, a probabilistic modelling approach, utilising a Monte Carlo technique used to provide a forecast capable of representing the uncertainty in the system, within a suitable time-frame for real-time flood forecasting that included an hourly Kalman filter data assimilation update. The findings presented in this thesis will be of interest to coastal modellers working in complex estuarine environments where the influences of tide-surge-wave interactions upon model predictions are uncertain. Furthermore, the application of a computationally efficient model, presented here, will provide a useful comparison with traditional physically-based systems to those wishing to quantify uncertainty in regions where computational resources are low
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Steed, Chad A. "Development of a geovisual analytics environment using parallel coordinates with applications to tropical cyclone trend analysis." Diss., Mississippi State : Mississippi State University, 2008. http://library.msstate.edu/etd/show.asp?etd=etd-10252008-080937.

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Mansoor, Shaheer. "System Surveillance." Thesis, Linköpings universitet, Statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-98189.

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In recent years, trade activity in stock markets has increased substantially. This is mainly attributed to the development of powerful computers and intranets connecting traders to markets across the globe. The trades have to be carried out almost instantaneously and the systems in place that handle trades are burdened with millions of transactions a day, several thousand a minute. With increasing transactions the time to execute a single trade increases, and this can be seen as an impact on the performance. There is a need to model the performance of these systems and provide forecasts to give a heads up on when a system is expected to be overwhelmed by transactions. This was done in this study, in cooperation with Cinnober Financial Technologies, a firm which provides trading solutions to stock markets. To ensure that the models developed weren‟t biased, the dataset was cleansed, i.e. operational and other transactions were removed, and only valid trade transactions remained. For this purpose, a descriptive analysis of time series along with change point detection and LOESS regression were used. State space model with Kalman Filtering was further used to develop a time varying coefficient model for the performance, and this model was applied to make forecasts. Wavelets were also used to produce forecasts, and besides this high pass filters were used to identify low performance regions. The State space model performed very well to capture the overall trend in performance and produced reliable forecasts. This can be ascribed to the property of Kalman Filter to handle noisy data well. Wavelets on the other hand didn‟t produce reliable forecasts but were more efficient in detecting regions of low performance.
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Paramygin, Vladimir A. "Towards a real-time 24/7 storm surge, inundation and 3-D baroclinic circulation forecasting system for the state of Florida." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0024729.

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Chou, Shuo-Ju. "A conceptual methodology for assessing acquisition requirements robustness against technology uncertainties." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39467.

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The lack of system capability, budget, and schedule robustness against technology performance and development uncertainties has led to major setbacks in recent acquisition programs. This lack of robustness stems from the fact that immature technologies have uncertainties in their expected performance and development times and costs that translate to variations in system effectiveness and program development budget and schedule requirements. As such, the objective of this thesis is to formulate an assessment process that better informs acquisition decision-makers of program requirements robustness against such uncertainties. To meet the stated research objective, a conceptual methodology for assessing acquisition requirements robustness against technology performance and development uncertainties was formulated. This general approach provides a structured process for integrating probabilistic and quantitative forecasting, multi-criteria decision-making, and decision-support techniques to generate the statistical data needed to quantitatively predict requirements robustness. The results of the robustness assessment indicates to the decision-makers whether or not the technology or set of technologies being developed for the program will result in system capabilities and program budget and schedule that meet decision-maker requirements and preferences. This results in a more informed and justifiable selection of program technologies during initial program definition as well as formulation of program development and risk management strategies.
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Lewe, Jung-Ho. "An Integrated Decision-Making Framework for Transportation Architectures: Application to Aviation Systems Design." Diss., Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-04132005-204114/unrestricted/Jung-Ho%5FLewe%5F200505%5Fphd.pdf.

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Thesis (Ph. D.)--Aerospace Engineering, Georgia Institute of Technology, 2005.
Amy R. Pritchett, Committee Member ; Moore, Mark D., Committee Member ; Wilhite, Alan, Committee Member ; Schrage, Daniel P., Committee Chair ; Mavris, Dimitri N., Committee Co-Chair ; DeLaurentis, Daniel A., Committee Member. Vita. Includes bibliographical references.
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Key, Peter Bernard. "Bayesian forecasting with state space models." Thesis, Royal Holloway, University of London, 1986. http://repository.royalholloway.ac.uk/items/87d86ed9-b2e7-4393-9fef-696f8c0cd147/1/.

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This thesis explores the use of State-Space models in Time Series Analysis and Forecasting, with particular reference to the Dynamic Linear Model (DLM) introduced by Harrison and Stevens. Concepts from Control Theory are employed, especially those of observability, controllability and filtering, together with Bayesian inference and classical forecasting methodology. First, properties of state-space models which depart from the usual Gaussian assumptions are examined, and the predictive consequences of such models are developed. These models can lead to new phenomena, for example it is shown that for a wide class of models which have a suitably defined steady evolution the usual properties of classical steady models (such as exponentially weighted moving averages) do not apply. Secondly, by considering the forecast functions, equivalence theorems are proved for DLMs in the steady state and stationary Box-Jenkins models. These theorems are then extended to include both time-varying and non-stationary models thus establishing a very general predictor equivalence. However it is shown that intuitively appealing DLMs which have diagonal covariance matrices are restricted by only covering part of the equivalent stability / invertibility region, and examples are given to illustrate these points. Thirdly, some problems of inference involving state-space models are looked at, and new approaches outlined. A class of collapsing procedures based upon a distance measure between posterior components is introduced. This allows the use of non-normal errors or Harrison-Stevens Class II models by condensing the normal-mixture posterior distribution to prevent an explosion of information with time, and avoids some of the problems of the Harrison-Stevens solution. Finally, some examples are given to illustrate the way in which some of these models and collapsing procedures might be used in practice.
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Fischer, Ulrike. "Forecasting in Database Systems." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-133281.

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Time series forecasting is a fundamental prerequisite for decision-making processes and crucial in a number of domains such as production planning and energy load balancing. In the past, forecasting was often performed by statistical experts in dedicated software environments outside of current database systems. However, forecasts are increasingly required by non-expert users or have to be computed fully automatically without any human intervention. Furthermore, we can observe an ever increasing data volume and the need for accurate and timely forecasts over large multi-dimensional data sets. As most data subject to analysis is stored in database management systems, a rising trend addresses the integration of forecasting inside a DBMS. Yet, many existing approaches follow a black-box style and try to keep changes to the database system as minimal as possible. While such approaches are more general and easier to realize, they miss significant opportunities for improved performance and usability. In this thesis, we introduce a novel approach that seamlessly integrates time series forecasting into a traditional database management system. In contrast to flash-back queries that allow a view on the data in the past, we have developed a Flash-Forward Database System (F2DB) that provides a view on the data in the future. It supports a new query type - a forecast query - that enables forecasting of time series data and is automatically and transparently processed by the core engine of an existing DBMS. We discuss necessary extensions to the parser, optimizer, and executor of a traditional DBMS. We furthermore introduce various optimization techniques for three different types of forecast queries: ad-hoc queries, recurring queries, and continuous queries. First, we ease the expensive model creation step of ad-hoc forecast queries by reducing the amount of processed data with traditional sampling techniques. Second, we decrease the runtime of recurring forecast queries by materializing models in a specialized index structure. However, a large number of time series as well as high model creation and maintenance costs require a careful selection of such models. Therefore, we propose a model configuration advisor that determines a set of forecast models for a given query workload and multi-dimensional data set. Finally, we extend forecast queries with continuous aspects allowing an application to register a query once at our system. As new time series values arrive, we send notifications to the application based on predefined time and accuracy constraints. All of our optimization approaches intend to increase the efficiency of forecast queries while ensuring high forecast accuracy.
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Lawson, Richard. "Adaptive state-space forecasting of gas demand." Thesis, Lancaster University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358799.

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Books on the topic "Forecasting of the state of systems"

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Ray, Ranjan. State-level food demand in India: Some evidence on rank-three demand systems. [Delhi: Centre for Development Economics, Delhi School of Economics, 1998.

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Putman, Stephen H. Integrated transportation and land use forecasting: Sensitivity tests of alternative model systems configuration. [Washington, D.C.]: U.S. Dept. of Transportation, Federal Highway Administration, Federal Transit Administration, Assistant Secretary for Transportation Policy, 2001.

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Senn, Larry. Summary report: Washington State road weather information systems. [Olympia, Wash.]: Washington State Dept. of Transportation, 2005.

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Orlova, Ekaterina. Econometric methodology of systems research. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1096421.

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The textbook presents the methodology of a comprehensive econometric approach to the analysis, modeling and forecasting of multi-level economic systems, which includes the construction and analysis of econometric models, the study of the possibility of their practical application to identify and justify economic patterns, forecasting the consequences of management decisions. Meets the requirements of the federal state educational standards of higher education of the latest generation. It is intended for bachelors, undergraduates and postgraduates of economic fields. It will also be useful for anyone who is engaged in data processing based on econometric methods and computer technologies.
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Office, General Accounting. Weather forecasting: Systems architecture needed for national weather service modernization : report to Congressional requesters. Washington, D.C: The Office, 1994.

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Garcia, Philip. The California State University System: Projections of enrollment demand, 1990 to 2005. [Long Beach, Calif.]: California State University, Office of the Chancellor, Division of Analytic Studies, 1991.

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The future of the Internet: Hearing before the Committee on Commerce, Science, and Transportation, United States Senate, One Hundred Tenth Congress, second session, April 22, 2008. Washington: U.S. G.P.O., 2012.

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Andreychikov, Aleksandr, and Ol'ga Andreychikova. Intelligent information systems and artificial intelligence methods. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1009595.

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The textbook discusses the methods of artificial intelligence and their application to solve problems from various subject areas. Methods of acquisition, representation and processing of knowledge in intelligent systems, as well as technologies for designing and implementing intelligent systems, are described. Special attention is paid to the application of intelligent systems for the selection of collective solutions, the design of complex systems( objects), the analysis and forecasting of the enterprise. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students enrolled in groups of training master's degree program "Management in technical systems", "Computer and information science", "computer science", "engineering and technology land transport", "engineering and construction technology", "Photonics, instrumentation, optical and biotechnical systems and technology", "aerospace engineering", "engineering and technologies of shipbuilding and water transport", and also in the areas of "automation of technological processes and productions", "mechatronics and robotics".
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United States. Congressional Budget Office. Alternatives for long-range ground-attack systems. Washington, D.C.]: Congress of the United States, Congressional Budget Office, 2006.

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Surface transportation system: Challenges for the future : hearing before the Subcommittee on Highways and Transit of the Committee on Transportation and Infrastructure, House of Representatives, One Hundred Tenth Congress, first session, January 24, 2007. Washington: U.S. G.P.O., 2007.

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Book chapters on the topic "Forecasting of the state of systems"

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Paulescu, Marius, Eugenia Paulescu, Paul Gravila, and Viorel Badescu. "State of the Sky Assessment." In Weather Modeling and Forecasting of PV Systems Operation, 43–88. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4649-0_3.

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Doan, Nguyen Anh Khoa, Wolfgang Polifke, and Luca Magri. "Physics-Informed Echo State Networks for Chaotic Systems Forecasting." In Lecture Notes in Computer Science, 192–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22747-0_15.

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Wang, Wilson. "An Intelligent System for Dynamic System State Forecasting." In Advances in Neural Networks – ISNN 2005, 460–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427445_75.

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Boguslavskiy, Josif A. "Identification of Parameters of Nonlinear Dynamic Systems; Smoothing, Filtration, Forecasting of State Vectors." In Dynamic Systems Models, 71–108. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-04036-3_5.

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Li, Guang, Jacques Lawarree, and Chen-Ching Liu. "State-of-the-Art of Electricity Price Forecasting in a Grid Environment." In Handbook of Power Systems II, 161–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12686-4_6.

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Korotaev, Gennady K. "Black Sea Forecasting System: Current State and Prospect." In Regional Aspects of Climate-Terrestrial-Hydrologic Interactions in Non-boreal Eastern Europe, 233–43. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2283-7_25.

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Musaev, Alexander, and Dmitry Grigoriev. "Machine Learning-Based Cyber-Physical Systems for Forecasting Short-Term State of Unstable Systems." In Cyber-Physical Systems: Intelligent Models and Algorithms, 189–200. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95116-0_16.

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Felka, Dariusz, and Jarosław Brodny. "Forecasting of Methane Hazard State in the Exploitation Wall Using Neural-Fuzzy System." In Advances in Intelligent Systems and Computing, 119–33. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15857-6_13.

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Dipti Singh and Ajay Satija. "Municipal Solid Waste Generation Forecasting for Faridabad City Located in Haryana State, India." In Advances in Intelligent Systems and Computing, 285–92. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0451-3_27.

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Gorbunov, Aleksandr P., Tatiana V. Kasaeva, Aleksandr P. Kolyadin, and Leyla D. Tokova. "Forecasting the Economic State of a Bank on the Basis of Bankruptcy Indicator." In Advances in Intelligent Systems and Computing, 639–45. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90835-9_74.

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Conference papers on the topic "Forecasting of the state of systems"

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Muralidhar, Nikhil, Sathappan Muthiah, and Naren Ramakrishnan. "DyAt Nets: Dynamic Attention Networks for State Forecasting in Cyber-Physical Systems." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/441.

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Multivariate time series forecasting is an important task in state forecasting for cyber-physical systems (CPS). State forecasting in CPS is imperative for optimal planning of system energy utility and understanding normal operational characteristics of the system thus enabling anomaly detection. Forecasting models can also be used to identify sub-optimal or worn out components and are thereby useful for overall system monitoring. Most existing work only performs single step forecasting but in CPS it is imperative to forecast the next sequence of system states (i.e curve forecasting). In this paper, we propose DyAt (Dynamic Attention) networks, a novel deep learning sequence to sequence (Seq2Seq) model with a novel hierarchical attention mechanism for long-term time series state forecasting. We evaluate our method on several CPS state forecasting and electric load forecasting tasks and find that our proposed DyAt models yield a performance improvement of at least 13.69% for the CPS state forecasting task and a performance improvement of at least 18.83% for the electric load forecasting task over other state-of-the-art forecasting baselines. We perform rigorous experimentation with several variants of the DyAt model and demonstrate that the DyAt models indeed learn better representations over the entire course of the long term forecast as compared to their counterparts with or without traditional attention mechanisms. All data and source code has been made available online.
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Satyamsetti, Vijayakrishna, Andreas Michealides, and Antonis Hadjiantonis. "Forecasting on solid state transformer applications." In 2017 International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2017. http://dx.doi.org/10.1109/iss1.2017.8389425.

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Rana, Md Masud, Rui Bo, and Bong Jun Choi. "Kalman Filter Based Electricity Market States Forecasting: A State-Space Framework." In 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2019. http://dx.doi.org/10.1109/cyber46603.2019.9066718.

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Beyaz, Erhan, Firat Tekiner, Xiao-jun Zeng, and John Keane. "Stock Price Forecasting Incorporating Market State." In 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 2018. http://dx.doi.org/10.1109/hpcc/smartcity/dss.2018.00263.

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Kurbatsky, Victor, Nikita Tomin, Denis Sidorov, and Vadim Spiryaev. "Hybrid genetic algorithms for forecasting power systems state variables." In 2013 IEEE Grenoble PowerTech. IEEE, 2013. http://dx.doi.org/10.1109/ptc.2013.6652215.

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Klochkov, Y., A. Gazizulina, N. Golovin, A. Glushkova, and Selezneva Zh. "Information model-based forecasting of technological process state." In 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS). IEEE, 2017. http://dx.doi.org/10.1109/ictus.2017.8286099.

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Hadayeghparast, Shahrzad, Amir Namavar Jahromi, and Hadis Karimipour. "A Hybrid Deep Learning-Based Power System State Forecasting." In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2020. http://dx.doi.org/10.1109/smc42975.2020.9283250.

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Hassanzadeh, M., and C. Y. Evrenosoglu. "Power system state forecasting using regression analysis." In 2012 IEEE Power & Energy Society General Meeting. New Energy Horizons - Opportunities and Challenges. IEEE, 2012. http://dx.doi.org/10.1109/pesgm.2012.6345595.

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Hong Li and Weiguo Li. "Estimation and forecasting of dynamic state estimation in power systems." In 2009 International Conference on Sustainable Power Generation and Supply. SUPERGEN 2009. IEEE, 2009. http://dx.doi.org/10.1109/supergen.2009.5348376.

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Basulaiman, Kamal, and Masoud Barati. "Sequence-to-Sequence Forecasting-aided State Estimation for Power Systems." In 2021 IEEE Texas Power and Energy Conference (TPEC). IEEE, 2021. http://dx.doi.org/10.1109/tpec51183.2021.9384984.

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Reports on the topic "Forecasting of the state of systems"

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Yu, Shu-Ling, and Jon Fricker. A Highway Travel Information System: Forecasting and Publicizing Delays in the Indiana State Highway Network. West Lafayette, IN: Purdue University, 2004. http://dx.doi.org/10.5703/1288284313126.

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BARKHATOV, NIKOLAY, and SERGEY REVUNOV. A software-computational neural network tool for predicting the electromagnetic state of the polar magnetosphere, taking into account the process that simulates its slow loading by the kinetic energy of the solar wind. SIB-Expertise, December 2021. http://dx.doi.org/10.12731/er0519.07122021.

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The auroral activity indices AU, AL, AE, introduced into geophysics at the beginning of the space era, although they have certain drawbacks, are still widely used to monitor geomagnetic activity at high latitudes. The AU index reflects the intensity of the eastern electric jet, while the AL index is determined by the intensity of the western electric jet. There are many regression relationships linking the indices of magnetic activity with a wide range of phenomena observed in the Earth's magnetosphere and atmosphere. These relationships determine the importance of monitoring and predicting geomagnetic activity for research in various areas of solar-terrestrial physics. The most dramatic phenomena in the magnetosphere and high-latitude ionosphere occur during periods of magnetospheric substorms, a sensitive indicator of which is the time variation and value of the AL index. Currently, AL index forecasting is carried out by various methods using both dynamic systems and artificial intelligence. Forecasting is based on the close relationship between the state of the magnetosphere and the parameters of the solar wind and the interplanetary magnetic field (IMF). This application proposes an algorithm for describing the process of substorm formation using an instrument in the form of an Elman-type ANN by reconstructing the AL index using the dynamics of the new integral parameter we introduced. The use of an integral parameter at the input of the ANN makes it possible to simulate the structure and intellectual properties of the biological nervous system, since in this way an additional realization of the memory of the prehistory of the modeled process is provided.
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Shifley, Stephen R. A generalized system of models forecasting Central States tree growth. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Forest Experiment Station, 1987. http://dx.doi.org/10.2737/nc-rp-279.

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Owyang, Michael T., Jeremy M. Piger, and Howard J. Wall. Forecasting National Recessions Using State Level Data. Federal Reserve Bank of St. Louis, 2012. http://dx.doi.org/10.20955/wp.2012.013.

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Monteiro, C., R. Bessa, V. Miranda, A. Botterud, J. Wang, G. Conzelmann, and INESC Porto. Wind power forecasting : state-of-the-art 2009. Office of Scientific and Technical Information (OSTI), November 2009. http://dx.doi.org/10.2172/968212.

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Fricker, Jon, and Sunil Saha. Traffic Volume Forecasting Methods for Rural State Highways. West Lafayette, IN: Purdue University, 1986. http://dx.doi.org/10.5703/1288284314120.

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Allen, Donald S., and Meenakshi Pasupathy. A State Space Forecasting Model with Fiscal and Monetary Control. Federal Reserve Bank of St. Louis, 1997. http://dx.doi.org/10.20955/wp.1997.017.

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Mendes, Joana, Jean Sumaili, Ricardo Bessa, Hrvoje Keko, Vladimiro Miranda, Audun Botterud, and Zhi Zhou. Very Short-Term Wind Power Forecasting: State-of-the-Art. Office of Scientific and Technical Information (OSTI), October 2014. http://dx.doi.org/10.2172/1158939.

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Archontoulis, Sotirios, Mark Licht, and Mitch Baum. Forecasting and Assessment of Cropping Systems in Northwest Iowa. Ames: Iowa State University, Digital Repository, 2018. http://dx.doi.org/10.31274/farmprogressreports-180814-1961.

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Archontoulis, Sotirios, and Mark Licht. Forecasting and Assessment of Cropping Systems in Northwest Iowa. Ames: Iowa State University, Digital Repository, 2017. http://dx.doi.org/10.31274/farmprogressreports-180814-1684.

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