Formation of metallurgical enterprise sustainable development portfolio using the method of analyzing hierarchies

Молоканова, В М; Орлюк, Олена; Petrenko, V.; Butnik-Syverskyi, O.B.; Khomenko, V.L.

doi:10.33271/nvngu/2020-2/131

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…The peculiarities of electricity consumption by ferrous metallurgy enterprises include: a large number of electrical equipment that is used in the implementation of the technological process in each unit; great variety of types and capacities of electricity receivers; relatively weak links between the mutual influence of electricity receivers in the implementation of the technological process; a large number of electrical equipment that participates in ensuring the technological process in each unit and creates a conditionally constant load, also depends on the intensity of the technological process; factors those randomly affect the modes and volume of electricity consumption; a large number of hours of use of maximum electric power; large electrical intensity of the final product types; the possibility of changing the modes of operation and composition of equipment in the unit, product range and other systematically acting factors [3].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Forecasting of Energy Consumption of a Metallurgical Enterprise

Bakurova

Yuskiv

Shyrokorad

et al. 2021

ITSSI

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The subject of the research is the methods of constructing and training neural networks as a nonlinear modeling apparatus for solving the problem of predicting the energy consumption of metallurgical enterprises. The purpose of this work is to develop a model for forecasting the consumption of the power system of a metallurgical enterprise and its experimental testing on the data available for research of PJSC "Dneprospetsstal". The following tasks have been solved: analysis of the time series of power consumption; building a model with the help of which data on electricity consumption for a historical period is processed; building the most accurate forecast of the actual amount of electricity for the day ahead; assessment of the forecast quality. Methods used: time series analysis, neural network modeling, short-term forecasting of energy consumption in the metallurgical industry. The results obtained: to develop a model for predicting the energy consumption of a metallurgical enterprise based on artificial neural networks, the MATLAB complex with the Neural Network Toolbox was chosen. When conducting experiments, based on the available statistical data of a metallurgical enterprise, a selection of architectures and algorithms for learning neural networks was carried out. The best results were shown by the feedforward and backpropagation network, architecture with nonlinear autoregressive and learning algorithms: Levenberg-Marquard nonlinear optimization, Bayesian Regularization method and conjugate gradient method. Another approach, deep learning, is also considered, namely the neural network with long short-term memory LSTM and the adam learning algorithm. Such a deep neural network allows you to process large amounts of input information in a short time and build dependencies with uninformative input information. The LSTM network turned out to be the most effective among the considered neural networks, for which the indicator of the maximum prediction error had the minimum value. Conclusions: analysis of forecasting results using the developed models showed that the chosen approach with experimentally selected architectures and learning algorithms meets the necessary requirements for forecast accuracy when developing a forecasting model based on artificial neural networks. The use of models will allow automating high-precision operational hourly forecasting of energy consumption in market conditions. Keywords: energy consumption; forecasting; artificial neural network; time series.

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Section: Introductionmentioning

confidence: 99%

Neural Network Forecasting of Energy Consumption of a Metallurgical Enterprise

Bakurova

Yuskiv

Shyrokorad

et al. 2021

ITSSI

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show abstract

“…The work [9] presents conceptual provisions for managing project portfolios in an organization and formalizing the methodological foundations for creating a value-oriented portfolio of projects for the development of metallurgical enterprises. Project portfolio management involves both analyzing all projects of the organization separately, and analyzing the characteristics of the entire set of projects implemented in the organization of projects and form a balanced portfolio [10]. The formalization of the main stages and methods of forming a portfolio of projects for a metallurgical enterprise taking into account its sustainable development strategy are given in [11].…”

Section: Introductionmentioning

confidence: 99%

Predictive adaptation methodology for portfolio management of energy saving projects at metallurgical enterprises

Кійко¹

2020

soi

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A methodology for managing portfolios of energy saving projects at metallurgical enterprises is proposed. It forms a paradigm of predictive adaptation based on interconnected adaptive systems for planning, monitoring and managing changes and allows for forecasting energy consumption for complex technological processes and production, as well as modeling and evaluating the quality of the fuel and energy balance, in conditions of limited resources and risks, to form and select energy saving projects for implementation, while coordinating the priorities of the business strategy and the energy efficiency strategy of the metallurgical enterprise. A multi-level model of energy consumption of a metallurgical enterprise has been developed, which makes it possible to assess the effectiveness of energy-saving projects selected for implementation, objectively assess the share of each energy resource in the total flow, determine the energy intensity of a separate production, workshop, and the entire enterprise, and adjust the strategic direction in energy management. Using the model, it is possible to solve a number of tasks, including assessing the rationality and efficiency of the existing energy consumption structure at the enterprise, predicting the expected levels of energy consumption when changing the technology, product range and quality, and comparing various technologies and equipment in terms of energy efficiency, optimal management of energy flows taking into account changes in production conditions. The basic requirements for the organization of a planning and management system for a portfolio of energy saving projects at a metallurgical enterprise are formulated. The block diagram of a computer system for planning and managing a portfolio of energy saving projects at PJSC "Dneprospetsstal" is presented. The developed system of energy saving processes should be built into the system of enterprise processes, correspond to the strategic goals of the enterprise and the characteristics of the production process. The scheme of energy costs planning process implemented at the enterprise was considered.

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The development of energy consumption forecasting model for a metallurgical enterprise

et al. 2021

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An up-to-date issue of a modern metallurgical enterprise is the increase of its energy efficiency, which is related, first of all, with energy saving. Therefore, the purpose of this paper is to develop a model for forecasting the metallurgical enterprise power system consumption and its experimental testing based on the PJSC “Electrometallurgical plant “Dniprospetsstal” named after A. M. Kuzmin data. In order to build a forecasting model, a neural network apparatus in the MATLAB system was used and it was done in two stages. At the first stage, as an experiments series result, the optimal architecture and algorithm of neural network training were determined. In the second stage, the dependence of the modeling graphs load error from the influence of daily consumption graphs is identified. The MATLAB software package has been adapted for the needs of “Dniprospetsstal” named after A. M. Kuzmin. Neural networks designed in this way can be used to solve applied issues of electrometallurgy, in particular, the long-term estimation of time series of hourly power for the 24 hours ahead.

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Formation of metallurgical enterprise sustainable development portfolio using the method of analyzing hierarchies

Cited by 4 publications

References 5 publications

Neural Network Forecasting of Energy Consumption of a Metallurgical Enterprise

Neural Network Forecasting of Energy Consumption of a Metallurgical Enterprise

Predictive adaptation methodology for portfolio management of energy saving projects at metallurgical enterprises

The development of energy consumption forecasting model for a metallurgical enterprise

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