Journal articles: '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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Niu, Dongxiao, Ling Ji, Yongli Wang, and Da Liu. "Echo state network with wavelet in load forecasting." Kybernetes 41, no. 10 (October 12, 2012): 1557–70. http://dx.doi.org/10.1108/03684921211276747.

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Dan, Jingpei, Wenbo Guo, Weiren Shi, Bin Fang, and Tingping Zhang. "Deterministic Echo State Networks Based Stock Price Forecasting." Abstract and Applied Analysis 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/137148.

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Echo state networks (ESNs), as efficient and powerful computational models for approximating nonlinear dynamical systems, have been successfully applied in financial time series forecasting. Reservoir constructions in standard ESNs rely on trials and errors in real applications due to a series of randomized model building stages. A novel form of ESN with deterministically constructed reservoir is competitive with standard ESN by minimal complexity and possibility of optimizations for ESN specifications. In this paper, forecasting performances of deterministic ESNs are investigated in stock price prediction applications. The experiment results on two benchmark datasets (Shanghai Composite Index and S&P500) demonstrate that deterministic ESNs outperform standard ESN in both accuracy and efficiency, which indicate the prospect of deterministic ESNs for financial prediction.

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Alomar, Miquel L., Vincent Canals, Nicolas Perez-Mora, Víctor Martínez-Moll, and Josep L. Rosselló. "FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting." Computational Intelligence and Neuroscience 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/3917892.

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Hardware implementation of artificial neural networks (ANNs) allows exploiting the inherent parallelism of these systems. Nevertheless, they require a large amount of resources in terms of area and power dissipation. Recently, Reservoir Computing (RC) has arisen as a strategic technique to design recurrent neural networks (RNNs) with simple learning capabilities. In this work, we show a new approach to implement RC systems with digital gates. The proposed method is based on the use of probabilistic computing concepts to reduce the hardware required to implement different arithmetic operations. The result is the development of a highly functional system with low hardware resources. The presented methodology is applied to chaotic time-series forecasting.

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Dezhi Li, Wilson Wang, and Fathy Ismail. "Fuzzy Neural Network Technique for System State Forecasting." IEEE Transactions on Cybernetics 43, no. 5 (October 2013): 1484–94. http://dx.doi.org/10.1109/tcyb.2013.2259229.

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Lees, Matthew J. "Data-based mechanistic modelling and forecasting of hydrological systems." Journal of Hydroinformatics 2, no. 1 (January 1, 2000): 15–34. http://dx.doi.org/10.2166/hydro.2000.0003.

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The paper presents a data-driven approach to the modelling and forecasting of hydrological systems based on nonlinear time-series analysis. Time varying parameters are estimated using a combined Kalman filter and fixed-interval-smoother, and state-dependent parameter relations are identified leading to nonlinear extensions to common time-series models such as the autoregressive exogenous (ARX) and general transfer function (TF). This nonlinear time-series technique is used as part of a data-based mechanistic modelling methodology where models are objectively identified from the data, but are only accepted as a reasonable representation of the system if they have a valid mechanistic interpretation. To this end it is shown that the TF model can represent a general linear storage model that subsumes many common hydrological flow forecasting models, and that the rainfall-runoff process can be represented using a nonlinear input transformation in combination with a TF model. One advantage of the forecasting models produced is that the Kalman filter can be used for real-time state updating leading to improved forecasts and an estimate of associated forecast uncertainty. Rainfall-runoff and flood routing case studies are included to demonstrate the power of the modelling and forecasting methods. One important conclusion is that optimal system identification techniques are required to objectively identify parallel flow pathways.

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NaitMalek, Youssef, Mehdi Najib, Mohamed Bakhouya, and Mohamed Essaaidi. "Embedded Real-time Battery State-of-Charge Forecasting in Micro-Grid Systems." Ecological Complexity 45 (January 2021): 100903. http://dx.doi.org/10.1016/j.ecocom.2020.100903.

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Yamashkin, Anatoliy, Stanislav Yamashkin, Vladimir Erofeev, and Anna Piksaykina. "Geodiagnostics of lithogydrogenic systems for forecasting exoggeodynamic processes." MATEC Web of Conferences 265 (2019): 03008. http://dx.doi.org/10.1051/matecconf/201926503008.

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The landscape indication, based on the automated analysis of remote sensing data, is one of the key methods of research and mapping of lithohydrogene geosystems. The article describes a set of methods for effective detection of types of lithohydrogene systems, including a set of modules for identifying dynamic and invariant descriptors of the territory; assessment of geophysical diversity of landscapes; analysis of the geophysical shell through the calculation of the descriptors of the neighborhood; ensemble-analysis of remote sensing data for monitoring the state of geosystems and forecasting of natural processes. The system of methods for detecting types of landscapes made it possible to conduct geodiagnostics of lithohydrogene systems of the Privolzhskaya Upland and the marginal part of the Oka-Don lowland reservoir within the boundaries of the Republic of Mordovia in order to predict the development of exogeodynamic processes.

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Umgiesser, Georg, Marco Bajo, Christian Ferrarin, Andrea Cucco, Piero Lionello, Davide Zanchettin, Alvise Papa, et al. "The prediction of floods in Venice: methods, models and uncertainty (review article)." Natural Hazards and Earth System Sciences 21, no. 8 (September 1, 2021): 2679–704. http://dx.doi.org/10.5194/nhess-21-2679-2021.

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Abstract. This paper reviews the state of the art in storm surge forecasting and its particular application in the northern Adriatic Sea. The city of Venice already depends on operational storm surge forecasting systems to warn the population and economy of imminent flood threats, as well as help to protect the extensive cultural heritage. This will be more important in the future, with the new mobile barriers called MOSE (MOdulo Sperimentale Elettromeccanico, Experimental Electromechanical Module) that will be completed by 2021. The barriers will depend on accurate storm surge forecasting to control their operation. In this paper, the physics behind the flooding of Venice is discussed, and the state of the art of storm surge forecasting in Europe is reviewed. The challenges for the surge forecasting systems are analyzed, especially in view of uncertainty. This includes consideration of selected historic extreme events that were particularly difficult to forecast. Four potential improvements are identified: (1) improve meteorological forecasts, (2) develop ensemble forecasting, (3) assimilation of water level measurements and (4) develop a multimodel approach.

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Sun, Guang, Jingjing Lin, Chen Yang, Xiangyang Yin, Ziyu Li, Peng Guo, Junqi Sun, Xiaoping Fan, and Bin Pan. "Stock Price Forecasting: An Echo State Network Approach." Computer Systems Science and Engineering 36, no. 3 (2021): 509–20. http://dx.doi.org/10.32604/csse.2021.014189.

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Sokolova, Ekaterina, and Andrey Sokolov. "The problem of forecasting emergency situations of technical objects." MATEC Web of Conferences 298 (2019): 00052. http://dx.doi.org/10.1051/matecconf/201929800052.

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The problem of edge state forecasting has a wide field of application. To begin with, it arises when one develops a technical diagnostics system as the problem of forecasting emergency situations of technical objects. When the ecological system is concerned it appears as the problem of forecasting unfavorable development of the ecological situation. In case of investment analysis it evolves as the problem of forecasting the risks of no profit. In medical diagnostic automated systems it is the forecasting disease progression and transition.

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WANG, Mingzhe, and Min GUO. "Traffic State Forecasting of Typical Roads in Beijing." Journal of Transportation Systems Engineering and Information Technology 13, no. 2 (April 2013): 191–98. http://dx.doi.org/10.1016/s1570-6672(13)60107-5.

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Chen, Liang, and Jijun Zhang. "A Forecast Model of City Natural Gas Daily Load Based on Data Mining." Scientific Programming 2022 (March 11, 2022): 1–14. http://dx.doi.org/10.1155/2022/1562544.

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Data mining technology is more and more widely used in the daily load forecasting of natural gas systems. It is still difficult to carry out high-precision, timely intraday load forecasting and intraday load dynamic characteristics clustering for natural gas systems. Based on data mining technology, this paper proposes a stable intraday load forecasting method for the natural gas flow state-space model. The load sensitivity under the current operating conditions of the system is obtained by calculation; the sample space of the state space is established through data processing; the partitions under different clustering radii are calculated; and the best intraday load flow is obtained through the state space effectiveness evaluation method. The experimental results show that the model load forecasting accuracy and relative error reached 98.5% and 0.026, respectively, which solved the problem of processing the long-term accumulated historical data of gas intra-day load. At the same time, the amount of data calculation was reduced by 33.6%, which effectively promoted the quantification of intraday load influencing factors and qualitative analysis.

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Кладченко, Ірина. "ПРОГНОЗУВАННЯ ТРАЄКТОРІЇ НАЦІОНАЛЬНОГО ЕКОНОМІЧНОГО РОЗВИТКУ МЕТОДАМИ ГАРМОНІЙНОГО І СПЕКТРАЛЬНОГО АНАЛІЗУ." Economical 1, no. 1(22) (2020): 115–31. http://dx.doi.org/10.31474/1680-0044-2020-1(22)-115-131.

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Improving of the methodological tools for the national economies behavior’s forecasting in the context of increasing the validity and analytical characteristics of state economic strategies in conditions of high volatility, lack of trend stability and non-stationary dynamics of external and internal socio-economic processes by implementing interdisciplinary methods of Fourier analysis and their adaptation to the specifics of the socio-economic systems’ functioning and development. Methodology. The forecasting’s targeting as an important independent stage in the process of analytical assessment of the balance of development is performed on the basis of structuring, division into stages, systematization, and grouping. Justification of the interdisciplinary approach for forecasting of national economic system’s development is carried out by methods of analysis and synthesis, comparison and practical testing. Establishment of the economic dynamics’ structural regularities and forecasting of a trajectory of national economy development are executed by harmonic and spectral analysis of the dynamic systems’ fluctuating processes. The results. The paper’s attention is focused on the specific features of macroeconomic dynamics’ time series as a basis for the forecasting of national economies development, namely their short duration, non-stationary, aperiodic, polyharmonic and their impact on the formation of adequate methodological support for forecasting. The possibility and efficiency of spectral and harmonic methods using for analysis oscillating processes of national economic system’s development are substantiated. A harmonic model of Ukraine's economic development’s trajectory during 1991-2020 is formed, which allowed to analyze the fluctuating component of the macroeconomic indicators’ dynamics on the basis of actual data that included all the initial information contained in the time series. By distinguishing economic cycles, their amplitude-frequency characteristics, the current phase of Ukrainian economy’s development is characterized. On the basis of the economic dynamics’ model, being used the indicator of annual GDP growth, forecasting is executed and short-term, average-term and long-term tendencies Ukraine’s economy’s development are established. Scientific novelty. The extending of theoretical and methodological tools for forecasting of main trends in national economies, based on harmonic and spectral analysis, is allowed to form a structural approach to the analysis of economic dynamics in the context of selection of its decisive harmonics and basing on their characteristics to make conclusions about the current level and projected national economic systems’ development. Practical significance. The adapted and regulated procedure of harmonic and spectral analysis of socio-economic systems’ oscillating processes became the basis for forecasting the level, rates and proportions of national economic systems development.

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Henonin, Justine, Beniamino Russo, Ole Mark, and Philippe Gourbesville. "Real-time urban flood forecasting and modelling – a state of the art." Journal of Hydroinformatics 15, no. 3 (February 11, 2013): 717–36. http://dx.doi.org/10.2166/hydro.2013.132.

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All urban drainage networks are designed to manage a maximum rainfall. This situation implies an accepted flood risk for any greater rainfall event. This risk is often underestimated as factors such as city growth and climate change are ignored. But even major structural changes cannot guarantee that urban drainage networks would cope with all future rain events. Thus, being able to forecast urban flooding in real time is one of the main issues of integrated flood risk management. Runoff and hydraulic models can be essential elements of flood forecast systems, as an active part of the system or as studying tools. This paper gives an overview of current available options for pluvial flood modelling in urban areas, from basic estimations with a one-dimensional urban drainage model to detailed flood process representation with one dimensional–two dimensional hydrodynamic coupled models. Each type of modelling solution is described with pros and cons regarding urban flood analysis. The paper then elaborates on real-time flood forecast systems and the influence of their main components. A classification of real-time urban flood systems is given based on the use of urban models, i.e. empirical scenarios, pre-simulated scenarios and real-time simulations. A review of existing operational systems is done using this classification.

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Trierweiler Ribeiro, Gabriel, João Guilherme Sauer, Naylene Fraccanabbia, Viviana Cocco Mariani, and Leandro dos Santos Coelho. "Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting." Energies 13, no. 9 (May 11, 2020): 2390. http://dx.doi.org/10.3390/en13092390.

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Load forecasting impacts directly financial returns and information in electrical systems planning. A promising approach to load forecasting is the Echo State Network (ESN), a recurrent neural network for the processing of temporal dependencies. The low computational cost and powerful performance of ESN make it widely used in a range of applications including forecasting tasks and nonlinear modeling. This paper presents a Bayesian optimization algorithm (BOA) of ESN hyperparameters in load forecasting with its main contributions including helping the selection of optimization algorithms for tuning ESN to solve real-world forecasting problems, as well as the evaluation of the performance of Bayesian optimization with different acquisition function settings. For this purpose, the ESN hyperparameters were set as variables to be optimized. Then, the adopted BOA employs a probabilist model using Gaussian process to find the best set of ESN hyperparameters using three different options of acquisition function and a surrogate utility function. Finally, the optimized hyperparameters are used by the ESN for predictions. Two datasets have been used to test the effectiveness of the proposed forecasting ESN model using BOA approaches, one from Poland and another from Brazil. The results of optimization statistics, convergence curves, execution time profile, and the hyperparameters’ best solution frequencies indicate that each problem requires a different setting for the BOA. Simulation results are promising in terms of short-term load forecasting quality and low error predictions may be achieved, given the correct options settings are used. Furthermore, since there is not an optimal global optimization solution known for real-world problems, correlations among certain values of hyperparameters are useful to guide the selection of such a solution.

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Nar, Melek, and Seher Arslankaya. "Passenger demand forecasting for railway systems." Open Chemistry 20, no. 1 (January 1, 2022): 105–19. http://dx.doi.org/10.1515/chem-2022-0124.

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Abstract The rapid increase of the population and the number of motor vehicles brought about the transportation problem today. It has brought the efforts of the operators to determine the headway of the vehicles during the day in order to minimize the waiting times of the passengers at the stops and increase the satisfaction of the passengers, taking into account the passenger demand. Nowadays, especially during the current pandemic period (COVID-19), passenger demand forecasting becomes much more significant, so that measures can be taken and headway planning can be made to adjust the social distance by identifying the number of passengers in advance. In this study, the significance of demand forecasting in the railway sector is considered, and the study tackles the issue in two stages: on line and station basis that make the study different from others. In the first stage of the study, passenger demand forecasting is made on line basis with statistical techniques such as regression analysis and simple average, the mean absolute percentage error values are calculated and compared. Regression analysis is conducted with SPSS Statistics 21.0 programme. In the second stage of the study, passenger demand forecasting is made with artificial neural network and machine learning (ML) algorithms technique on station basis and the error values (mean absolute error, BIAS, mean squared error, mean absolute percentage error, and root mean squared error) are compared. As a result of the study, while the best demand forecasting method is simple average on line basis, it is seen that the most successful and reliable results for demand forecasting on station basis are obtained through decision tree, which is one of the ML algorithms.

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Jumaev, O. A., J. T. Nazarov, G. B. Makhmudov, M. T. Ismoilov, and M. F. Shermuradova. "Intelligent control systems using algorithms of the entropie potential method." Journal of Physics: Conference Series 2094, no. 2 (November 1, 2021): 022030. http://dx.doi.org/10.1088/1742-6596/2094/2/022030.

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Abstract As part of neural network systems, an artificial neural network can perform various functions like diagnostics of technological equipment, control of moving objects and technological processes, forecasting situations, as well as assessing the state and monitoring of technological processes.

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Otradskaya, Tatyana, Nikolay Rudnichenko, Denis Shibaev, Natalia Shibaeva, and Vladimir Vychuzhanin. "DATA CONTROL IN THE DIAGNOSTICS AND FORECASTING THE STATE OF COMPLEX TECHNICAL SYSTEMS." Herald of Advanced Information Technology 2, no. 3 (June 19, 2019): 183–96. http://dx.doi.org/10.15276/hait.03.2019.2.

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Kozitsin, Viacheslav, Iurii Katser, and Dmitry Lakontsev. "Online Forecasting and Anomaly Detection Based on the ARIMA Model." Applied Sciences 11, no. 7 (April 2, 2021): 3194. http://dx.doi.org/10.3390/app11073194.

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Real-time diagnostics of complex technical systems such as power plants are critical to keep the system in its working state. An ideal diagnostic system must detect any fault in advance and predict the future state of the technical system, so predictive algorithms are used in the diagnostics. This paper proposes a novel, computationally simple algorithm based on the Auto-Regressive Integrated Moving Average model to solve anomaly detection and forecasting problems. The good performance of the proposed algorithm was confirmed in numerous numerical experiments for both anomaly detection and forecasting problems. Moreover, a description of the Autoregressive Integrated Moving Average Fault Detection (ARIMAFD) library, which includes the proposed algorithms, is provided in this paper. The developed algorithm proves to be an efficient algorithm and can be applied to problems related to anomaly detection and technological parameter forecasting in real diagnostic systems.

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Shchemelinin, D. "Mathematical Models and Methods for Monitoring and Predicting the State of Globally Distributed Computing Systems." Proceedings of Telecommunication Universities 7, no. 3 (October 6, 2021): 73–78. http://dx.doi.org/10.31854/1813-324x-2021-7-3-73-78.

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Monitoring events and predicting the behavior of a dynamic information system are becoming increasingly important due to the globalization of cloud services and a sharp increase in the volume of processed data. Well-known monitoring systems are used for the timely detection and prompt correction of the anomaly, which require new, more effective and proactive forecasting tools. At the CMG-2013 conference, a method for predicting memory leaks in Java applications was presented, which allows IT teams to automatically release resources by safely restarting services when a certain critical threshold value is reached. Article’s solution implements a simple linear mathematical model for describing the historical trend function. However, in practice, the degradation of memory and other computational resources may not occur gradually, but very quickly, depending on the workload, and therefore, solving the forecasting problem using linear methods is not effective enough.

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Podryadchikova, Ekaterina, Larisa Gileva, Aleksey Dubrovsky, and Elena Lobanova. "Use of geoinformation systems for forecasting and recognizing crisis situations in agriculture." E3S Web of Conferences 296 (2021): 03004. http://dx.doi.org/10.1051/e3sconf/202129603004.

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This article covers the necessity of use of geoinformation systems for forecasting and recognizing crisis situations in agriculture. The method of creation of thematic maps was offered as a tool by the authors of the article, three thematic maps were obtained for functional zoning by the indicators of: financial state of agricultural production, ecological state expressed in monetary equivalent and forecast of crisis states in agriculture. The given method was proved within the boundaries of the 121 village councils of the central part of Novosibirsk region for 64 agricultural companies of different forms of organization.

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Rosselló, Josep L., Miquel L. Alomar, Antoni Morro, Antoni Oliver, and Vincent Canals. "High-Density Liquid-State Machine Circuitry for Time-Series Forecasting." International Journal of Neural Systems 26, no. 05 (June 8, 2016): 1550036. http://dx.doi.org/10.1142/s0129065715500367.

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Spiking neural networks (SNN) are the last neural network generation that try to mimic the real behavior of biological neurons. Although most research in this area is done through software applications, it is in hardware implementations in which the intrinsic parallelism of these computing systems are more efficiently exploited. Liquid state machines (LSM) have arisen as a strategic technique to implement recurrent designs of SNN with a simple learning methodology. In this work, we show a new low-cost methodology to implement high-density LSM by using Boolean gates. The proposed method is based on the use of probabilistic computing concepts to reduce hardware requirements, thus considerably increasing the neuron count per chip. The result is a highly functional system that is applied to high-speed time series forecasting.

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Yang, Jin, Yun Jie Li, and Qin Li. "Immune-Based Systems on Network Security Situation Awareness and Risk Prediction Model." Applied Mechanics and Materials 643 (September 2014): 99–104. http://dx.doi.org/10.4028/www.scientific.net/amm.643.99.

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In this paper, the process of the developments and changes of the network intrusion behaviors were analyzed. An improved epidemic spreading model was proposed to study the mechanisms of aggressive behaviors spreading, to predict the future course of an outbreak and to evaluate strategies to control a network epidemic. Based on Artificial Immune Systems, the concepts and formal definitions of immune cells were given. And in this paper, the forecasting algorithm based on Markov chain theory was proposed to improve the precision of network risk forecasting. The data of the Memory cells were analyzed directly and kinds of state-spaces were formed, which can be used to predict the risk of network situation by analyzing the cells status and the classification of optimal state. Experimental results show that the proposed model has the features of real-time processing for network situation awareness.

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Kushner, Guriy Alekseevich, and Victor Andreevich Mamontov. "Estimating efficiency of forecasting technical conditions of ship propulsion systems." Vestnik of Astrakhan State Technical University. Series: Marine engineering and technologies 2021, no. 4 (November 30, 2021): 27–33. http://dx.doi.org/10.24143/2073-1574-2021-4-27-33.

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The article considers an approach to assessing the effectiveness of the most common methods of predicting the technical conditions and failure with reference to the ship shafting. There have been analyzed the main factors in operation of the ship shaft line, which cause the change in its technical state. It has been found that a special feature of some loads acting on the propeller shaft is their stochastic or changing nature over time, which hampers predicting the technical state of the shafting and its units. The features of stochastic and extrapolation forecasting methods have been analyzed. The possibility of using statistical methods in conditions of mass standard production of shafting units with a relatively short regulated service life is estimated. An extrapolation method is proposed for predicting the maximum permissible clearance of stern tube bearings. The case of accumulating samples of measuring results of the propeller shaft sagging in the given time intervals is considered, the approximating functions are constructed. The criteria for the reliability of the results of extrapolation methods for predicting the wear of stern tube bearings are determined. There have been developed the proposals for adapting the causal method as an alternative to the extrapolation method. A schematic diagram of a system for the ship shafting failure predicting has been developed using the registration and analysis of vibration parameters, which serves as the basis for constructing a regression model of damage accumulation. The proposed forecasting system allows studying the actual operating conditions of the shafting, defining the actual external loads and the regularities of their occurrence, measuring deformations and stresses, and determining quantitative indicators of the reliability of the shafting during normal operation and special operating modes, for example, with vibration resonance. The theoretical basis of the algorithm for calculating and registering loads affecting the service life of shafts is proposed.

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35

Fildes, Robert. "Quantitative Forecasting-The State of the Art: Econometric Models." Journal of the Operational Research Society 36, no. 7 (July 1985): 549. http://dx.doi.org/10.2307/2582473.

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Fildes, Robert. "Quantitative Forecasting—the State of the Art: Econometric Models." Journal of the Operational Research Society 36, no. 7 (July 1985): 549–80. http://dx.doi.org/10.1057/jors.1985.99.

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Gharehchopoghi, Farhad Soleimanian, Freshte Dabaghchi Mokri, and Maryam Molany. "A New Approach in Short-Term Prediction of the Electrical Charge with Regression Models A Case Study." International Journal of Applied Metaheuristic Computing 4, no. 3 (July 2013): 34–46. http://dx.doi.org/10.4018/ijamc.2013070103.

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The accuracy of forecasting of electrical load for the electricity industry has a vital significance in the renewal of economic structure as well as various equations including: purchasing and producing energy, load fluctuation, and the development of infrastructures. Its short-term forecasting has a significant role in designing and utilizing power systems and in the distribution systems and having a variety of systems used to maintain security potentials for the system. In this paper, we attempted to carry out a short-term forecasting of electrical distribution company in west Azerbaijan state in Iran's electricity in a few days on the basis of regression multi linear model. This forecasting which was done during a three-day period is and categorized weekdays into three groups including working days, weekends, and holidays was carried out in an hourly manner. This model regardless of parameters like humidity, wind velocity, daylight time, etc. by minimizing the forecasting error managed to maximize the reliability of the results as well as the safety potential of the system. In this model the only influential parameter on the forecasting was the reliance of the forecasting day on previous days. The main purpose of the present study was to maximize the accuracy and reliability of forecasting for certain days (religious holidays, national holidays …). In this paper, the authors managed to decrease the error of forecasting for particular and regular off days to a great extent.

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Makshanov, Andrey V., and Alexander A. Musaev. "CHAOTIC PROCESSES FORECASTING BASED ON PRECEDENTIAL DATA ANALYSIS." Bulletin of the Saint Petersburg State Institute of Technology (Technical University) 55 (2020): 85–90. http://dx.doi.org/10.36807/1998-9849-2020-55-81-85-90.

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The problem of predicting of chaotic processes – which are typical for evolution of parameters of unstable systems – is considered. Examples of such systems are gas, hydro, and thermodynamic media whose state dynamics are described by a system of open nonlinear equations. In that case, the traditional methods of extrapolation approach are ineffective. The multiplicity of bifurcation points leads to the fact that even minor perturbations can radically change the dynamics of the observed process. In this regard, the article presents precedential forecasting algorithms. The proposed method is based on a combination of statistical analysis and machine learning techniques.

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Geetha, Sreenath Jayakumar, Saikat Chakrabarti, and Ketan Rajawat. "Asynchronous Hierarchical Forecasting-Aided State Estimator With Sub-Area Data Validation for Power Systems." IEEE Sensors Journal 21, no. 2 (January 15, 2021): 2124–33. http://dx.doi.org/10.1109/jsen.2020.3017920.

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Guo, Ziyu, Shang Li, Xiaodong Wang, and Wei Heng. "Distributed Point-Based Gaussian Approximation Filtering for Forecasting-Aided State Estimation in Power Systems." IEEE Transactions on Power Systems 31, no. 4 (July 2016): 2597–608. http://dx.doi.org/10.1109/tpwrs.2015.2477285.

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Huang, Manyun, Zhinong Wei, and Yuzhang Lin. "Forecasting-aided state estimation based on deep learning for hybrid AC/DC distribution systems." Applied Energy 306 (January 2022): 118119. http://dx.doi.org/10.1016/j.apenergy.2021.118119.

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Chatzidakis, Stylianos, Miltiadis Alamaniotis, and Lefteri H. Tsoukalas. "Creep Rupture Forecasting." International Journal of Monitoring and Surveillance Technologies Research 2, no. 2 (April 2014): 1–25. http://dx.doi.org/10.4018/ijmstr.2014040101.

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Creep rupture is becoming increasingly one of the most important problems affecting behavior and performance of power production systems operating in high temperature environments and potentially under irradiation as is the case of nuclear reactors. Creep rupture forecasting and estimation of the useful life is required to avoid unanticipated component failure and cost ineffective operation. Despite the rigorous investigations of creep mechanisms and their effect on component lifetime, experimental data are sparse rendering the time to rupture prediction a rather difficult problem. An approach for performing creep rupture forecasting that exploits the unique characteristics of machine learning algorithms is proposed herein. The approach seeks to introduce a mechanism that will synergistically exploit recent findings in creep rupture with the state-of-the-art computational paradigm of machine learning. In this study, three machine learning algorithms, namely General Regression Neural Networks, Artificial Neural Networks and Gaussian Processes, were employed to capture the underlying trends and provide creep rupture forecasting. The current implementation is demonstrated and evaluated on actual experimental creep rupture data. Results show that the Gaussian process model based on the Matérn kernel achieved the best overall prediction performance (56.38%). Significant dependencies exist on the number of training data, neural network size, kernel selection and whether interpolation or extrapolation is performed.

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Abed, Areej Adnan, Iurii Repilo, Ruslan Zhyvotovskyi, Andrii Shyshatskyi, Spartak Hohoniants, Serhii Kravchenko, Iryna Zhyvylo, Mykola Dieniezhkin, Nadiia Protas, and Oleksandr Shcheptsov. "Improvement of the method of estimation and forecasting of the state of the monitoring object in intelligent decision support systems." Eastern-European Journal of Enterprise Technologies 4, no. 3(112) (August 31, 2021): 43–55. http://dx.doi.org/10.15587/1729-4061.2021.237996.

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In order to objectively and completely analyze the state of the monitored object with the required level of efficiency, the method for estimating and forecasting the state of the monitored object in intelligent decision support systems was improved. The essence of the method is to provide an analysis of the current state of the monitored object and short-term forecasting of the state of the monitored object. Objective and complete analysis is achieved using advanced fuzzy temporal models of the object state, taking into account the type of uncertainty and noise of initial data. The novelty of the method is the use of an improved procedure for processing initial data in conditions of uncertainty, an improved procedure for training artificial neural networks and an improved procedure for topological analysis of the structure of fuzzy cognitive models. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function and the architecture of individual elements and the architecture of the artificial neural network as a whole. The procedure of forecasting the state of the monitored object allows for multidimensional analysis, accounting and indirect influence of all components of the multidimensional time series with their different time shifts relative to each other in conditions of uncertainty. The method allows increasing the efficiency of data processing at the level of 12–18 % using additional advanced procedures. The proposed method can be used in decision support systems of automated control systems (ACS DSS) for artillery units, special-purpose geographic information systems. It can also be used in ACS DSS for aviation and air defense and ACS DSS for logistics of the Armed Forces of Ukraine

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Judd, Kevin, Carolyn A. Reynolds, Thomas E. Rosmond, and Leonard A. Smith. "The Geometry of Model Error." Journal of the Atmospheric Sciences 65, no. 6 (June 1, 2008): 1749–72. http://dx.doi.org/10.1175/2007jas2327.1.

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Abstract This paper investigates the nature of model error in complex deterministic nonlinear systems such as weather forecasting models. Forecasting systems incorporate two components, a forecast model and a data assimilation method. The latter projects a collection of observations of reality into a model state. Key features of model error can be understood in terms of geometric properties of the data projection and a model attracting manifold. Model error can be resolved into two components: a projection error, which can be understood as the model’s attractor being in the wrong location given the data projection, and direction error, which can be understood as the trajectories of the model moving in the wrong direction compared to the projection of reality into model space. This investigation introduces some new tools and concepts, including the shadowing filter, causal and noncausal shadow analyses, and various geometric diagnostics. Various properties of forecast errors and model errors are described with reference to low-dimensional systems, like Lorenz’s equations; then, an operational weather forecasting system is shown to have the same predicted behavior. The concepts and tools introduced show promise for the diagnosis of model error and the improvement of ensemble forecasting systems.

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Sotnyk, M., V. Moskalenko, A. Sokhan, and D. Sukhostavets. "FORECASTING THE VIBRATIONAL STATE OF AN ELECTRIC PUMP AGGREGATE." Electromechanical and energy saving systems 3, no. 51 (September 29, 2020): 19–25. http://dx.doi.org/10.30929/2072-2052.2020.3.51.19-25.

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Purpose. The operation of electromechanical systems (EMS) in off-design modes and in which centrifugal pumps are used is accompanied by a number of negative factors, a special place among which is occupied by excessive blade vibration of the pump, which negatively affects its operational characteristics and causes a reduction in the service life of the main EMS units. Thus, an urgent task is to improve the operating characteristics of the pump as a component of EMS, which, by increasing the energy efficiency of the EMS working process and/or reducing the total cost of the life cycle of the pumps in their composition, will ultimately have a significant economic effect. Methodology. Experimental research of working process of an electric pump aggregate type D according to DSTU 6134:2009 and ISO 10816-3:2014. Results. Based on the experimental research results of vibration state of the pump D2000-100-2 bearing shell, which operates as part of the EMS, and the intensity of fluid pressure pulsations at its outlet, the limit root mean square value (RMS) of the pressure pulsation amplitude (∆Р ≥ 35,8 kPa and/or 3,4 % Н) is set at which an excess of the established ISO 10816: 3-2014 limit RMS of vibration velocity of the pump bearing shell ( V  2,8 mm/s ) and also is determined correlation coefficient ( / л k V Р ), which characterizes the RMS of the vibration velocity of the pump bearing shell at the blade frequency ( Vл ) depending on the RMS amplitude of the blade pressure pulsations (∆Р). Practical value. Since the number and systematic of experimental researches of the effect of pump parameters on the intensity of its blade vibration is complicated by the high cost of their implementation, therefore, it is advisable in further researches to use the RMS amplitude of blade pressure pulsations as an indirect indicator of the RMS vibration velocity of the pump bearing shell at the blade frequency. Conclusion. The intensity of pressure pulsations and influence of main parameters of the pump on their amplitude, with sufficient accuracy for engineering calculations can be determined by numerical modeling of the unsteady fluid flow in the flowing part of the pump. Figures 5, tables 2, references 10.

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Astrein, VV, SI Kondratyev, and AL Boran-Keshishyan. "Multicriteria assessment of optimal forecasting models in decision support systems to ensure the navigation safety." Journal of Physics: Conference Series 2061, no. 1 (October 1, 2021): 012108. http://dx.doi.org/10.1088/1742-6596/2061/1/012108.

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Abstract When developing the Decision Support System (DSS), the operability of the internal motion and maneuvering control systems of the vessel is characterized by a large number of parameters, which should be monitored in order to achieve the expected results. The task is to develop an appropriate methodology for automatic monitoring of these systems, which, using a minimum set of sensors, makes it possible to predict the state of the vessel and change the dangerous state to the safe one. To implement the method of automatic monitoring, a set of statistical control tools is selected depending on the alleged violations and the level of correlation of parameters. The uncorrelated parameters are monitored by instruments based on the Shewhart map [1], the correlated parameters are monitored on the basis of Hotelling statistics [2]. This approach makes it possible to diagnose the pre-emergency and emergency states of ship control systems in on-line mode. The method used for multi-level integrated monitoring of the technical state of control systems in on-line mode can improve the reliability of of identification of the technical state of vessel subsystems and expandthe scope of application of monitoring and diagnostics tools. The data obtained can become the basis for the development of rational decisions in the DSS at the level of control subsystems for the vessel transfer from the dangerous to safe state.

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Milić, Miljana, Jelena Milojković, Ivan Marković, and Petar Nikolić. "Concurrent, Performance-Based Methodology for Increasing the Accuracy and Certainty of Short-Term Neural Prediction Systems." Computational Intelligence and Neuroscience 2019 (April 1, 2019): 1–12. http://dx.doi.org/10.1155/2019/9323482.

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Accurate prediction of the short time series with highly irregular behavior is a challenging task found in many areas of modern science. Such data fluctuations are not systematic and hardly predictable. In recent years, artificial neural networks have widely been exploited for those purposes. Although it is possible to model nonlinear behavior of short time series by using ANNs, very often they are not able to handle all events equally well. Therefore, alternative approaches have to be applied. In this study, a new, concurrent, performance-based methodology that combines best ANN topologies in order to decrease the forecasting errors and increase the forecasting certainty is proposed. The proposed approach is verified on three different data sets: the Serbian Gross National Income time series, the municipal traffic flow for a particular observation point, and the daily electric load consumption time series. It is shown that the method can significantly increase the forecasting accuracy of the individual networks, regardless of their topologies, which makes the methodology more applicable. For quantitative comparison of the accuracy of the proposed methodology with that of similar methodologies, a series of additional forecasting experiments that include a state-of-the-art ARIMA modelling and a combination of ANN and linear regression forecasting have been conducted.

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Gustavsen, G. W. "Forecasting ability of theory-constrained two-stage demand systems." European Review of Agriculture Economics 30, no. 4 (December 1, 2003): 539–58. http://dx.doi.org/10.1093/erae/30.4.539.

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Wu, Xinle, Dalin Zhang, Chenjuan Guo, Chaoyang He, Bin Yang, and Christian S. Jensen. "AutoCTS." Proceedings of the VLDB Endowment 15, no. 4 (December 2021): 971–83. http://dx.doi.org/10.14778/3503585.3503604.

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Correlated time series (CTS) forecasting plays an essential role in many cyber-physical systems, where multiple sensors emit time series that capture interconnected processes. Solutions based on deep learning that deliver state-of-the-art CTS forecasting performance employ a variety of spatio-temporal (ST) blocks that are able to model temporal dependencies and spatial correlations among time series. However, two challenges remain. First, ST-blocks are designed manually, which is time consuming and costly. Second, existing forecasting models simply stack the same ST-blocks multiple times, which limits the model potential. To address these challenges, we propose AutoCTS that is able to automatically identify highly competitive ST-blocks as well as forecasting models with heterogeneous ST-blocks connected using diverse topologies, as opposed to the same ST-blocks connected using simple stacking. Specifically, we design both a micro and a macro search space to model possible architectures of ST-blocks and the connections among heterogeneous ST-blocks, and we provide a search strategy that is able to jointly explore the search spaces to identify optimal forecasting models. Extensive experiments on eight commonly used CTS forecasting benchmark datasets justify our design choices and demonstrate that AutoCTS is capable of automatically discovering forecasting models that outperform state-of-the-art human-designed models.

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Löwe, Roland, Peter Steen Mikkelsen, Michael R. Rasmussen, and Henrik Madsen. "State-space adjustment of radar rainfall and skill score evaluation of stochastic volume forecasts in urban drainage systems." Water Science and Technology 68, no. 3 (August 1, 2013): 584–90. http://dx.doi.org/10.2166/wst.2013.284.

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Merging of radar rainfall data with rain gauge measurements is a common approach to overcome problems in deriving rain intensities from radar measurements. We extend an existing approach for adjustment of C-band radar data using state-space models and use the resulting rainfall intensities as input for forecasting outflow from two catchments in the Copenhagen area. Stochastic grey-box models are applied to create the runoff forecasts, providing us with not only a point forecast but also a quantification of the forecast uncertainty. Evaluating the results, we can show that using the adjusted radar data improves runoff forecasts compared with using the original radar data and that rain gauge measurements as forecast input are also outperformed. Combining the data merging approach with short-term rainfall forecasting algorithms may result in further improved runoff forecasts that can be used in real time control.

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

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