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Date submitted2023-10-29
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Date accepted2024-04-08
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Date published2025-02-25
Evaluation of the impact of the distance determination function on the results of optimization of the geographical placement of renewable energy sources-based generation using a metaheuristic algorithm
- Authors:
- Andrei M. Bramm
- Stanislav A. Eroshenko
Since the United Power System was created electrical supply of remote and hard-to-reach areas remains one of the topical issues for the power industry of Russia. Nowadays, usage of various renewable energy sources to supply electricity at remote areas has become feasible alternative to usage of diesel-based generation. It becomes more suitable with world decarbonization trends, the doctrine of energy security of Russia directives, and equipment cost decreasing for renewable energy sources-based power plants construction. Geological exploration is usually conducted at remote territories, where the centralized electrical supply can not be realized. Placement of large capacity renewable energy sources-based generation at the areas of geological expeditions looks perspective due to development of industrial clusters and residential consumers of electrical energy at those territories later on. Various metaheuristic methods are used to solve the task of optimal renewable energy sources-based generation geographical placement. The efficiency of metaheuristics depends on proper tuning of that methods hyperparameters, and high quality of big amount of meteorological and climatic data. The research of the effects of the calculation methods defining distance between agents of the algorithm on the optimization of renewable generation placement results is presented in this article. Two methods were studied: Euclidean distance and haversine distance. There were two cases considered to evaluate the effects of distance calculation method change. The first one was for a photovoltaic power plant with installed capacity of 45 MW placement at the Vagaiskii district of the Tyumen region. The second one was for a wind power plant with installed capacity of 25 MW at the Tungokochenskii district of the Trans-Baikal territory. The obtained results show low effects of distance calculation method change at average but the importance of its proper choose in case of wind power optimal placement, especially for local optima’s identification.
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Date submitted2023-11-10
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Date accepted2024-06-03
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Date published2025-02-25
Enhancing the interpretability of electricity consumption forecasting models for mining enterprises using SHapley Additive exPlanations
- Authors:
- Pavel V. Matrenin
- Alina I. Stepanova
The objective of this study is to enhance user trust in electricity consumption forecasting systems for mining enterprises by applying explainable artificial intelligence methods that provide not only forecasts but also their justifications. The research object comprises a complex of mines and ore processing plants of a company purchasing electricity on the wholesale electricity and power market. Hourly electricity consumption data for two years, schedules of planned repairs and equipment shutdowns, and meteorological data were utilized. Ensemble decision trees were applied for time series forecasting, and an analysis of the impact of various factors on forecasting accuracy was conducted. An algorithm for interpreting forecast results using the SHapley Additive exPlanation method was proposed. The mean absolute percentage error was 7.84 % with consideration of meteorological factors, 7.41 % with consideration of meteorological factors and a load plan formulated by an expert, and the expert's forecast error was 9.85 %. The results indicate that the increased accuracy of electricity consumption forecasting, considering additional factors, further improves when combining machine learning methods with expert evaluation. The development of such a system is only feasible using explainable artificial intelligence models.
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Date submitted2021-05-12
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Date accepted2022-05-11
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Date published2023-07-19
Application of the cybernetic approach to price-dependent demand response for underground mining enterprise electricity consumption
The article considers a cybernetic model for the price-dependent demand response (DR) consumed by an underground mining enterprise (UGME), in particular, the main fan unit (MFU). A scheme of the model for managing the energy consumption of a MFU in the DR mode and the implementation of the cybernetic approach to the DR based on the IoT platform are proposed. The main functional requirements and the algorithm of the platform operation are described, the interaction of the platform with the UGME digital model simulator, on which the processes associated with the implementation of the technological process of ventilation and electricity demand response will be simulated in advance, is shown. The results of modeling the reduction in the load on the MFU of a mining enterprise for the day ahead are given. The presented solution makes it possible to determine in advance the necessary power consumption for the operation of the main power supply unit, manage its operation in an energy-saving mode and take into account the predicted changes in the planned one (e.g., when men hoisting along an air shaft) and unscheduled (e.g., when changing outdoor air parameters) modes. The results of the study can be used to reduce the cost of UGME without compromising the safety of technological processes, both through the implementation of energy-saving technical, technological or other measures, and with the participation of enterprises in the DR market. The proposed model ensures a guaranteed receipt of financial compensation for the UGME due to a reasonable change in the power consumption profile of the MFU during the hours of high demand for electricity, set by the system operator of the Unified Energy System.
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Date submitted2023-03-14
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Date accepted2023-06-20
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Date published2023-07-19
Forecasting planned electricity consumption for the united power system using machine learning
The paper presents the results of studies of the predictive models development based on retrospective data on planned electricity consumption in the region with a significant share of enterprises in the mineral resource complex. Since the energy intensity of the industry remains quite high, the task of rationalizing the consumption of electricity is relevant. One of the ways to improve control accuracy when planning energy costs is to forecast electrical loads. Despite the large number of scientific papers on the topic of electricity consumption forecasting, this problem remains relevant due to the changing requirements of the wholesale electricity and power market to the accuracy of forecasts. Therefore, the purpose of this study is to support management decisions in the process of planning the volume of electricity consumption. To realize this, it is necessary to create a predictive model and determine the prospective power consumption of the power system. For this purpose, the collection and analysis of initial data, their preprocessing, selection of features, creation of models, and their optimization were carried out. The created models are based on historical data on planned power consumption, power system performance (frequency), as well as meteorological data. The research methods were: ensemble methods of machine learning (random forest, gradient boosting algorithms, such as XGBoost and CatBoost) and a long short-term memory recurrent neural network model (LSTM). The models obtained as a result of the conducted studies allow creating short-term forecasts of power consumption with a fairly high precision (for a period from one day to a week). The use of models based on gradient boosting algorithms and neural network models made it possible to obtain a forecast with an error of less than 1 %, which makes it possible to recommend the models described in the paper for use in forecasting the planned electricity power consumption of united power systems.
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Date submitted2020-01-09
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Date accepted2020-01-26
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Date published2020-02-25
Mining excavator working equipment load forecasting according to a fuzzy-logistic model
- Authors:
- V. S. Velikanov
Due to the fact that the loads occurring in the working equipment of mining excavators are determined by a large number of random factors that are difficult to represent by analytical formulas, for estimating and predicting loads the models must be introduced using non-standard approaches. In this study, we used the methodology of the theory of fuzzy logic and fuzzy pluralities, which allows to overcome the difficulties associated with the incompleteness and vagueness of the data in assessing and predicting the forces encountered in the working equipment of mining excavators, as well as with the qualitative nature of these data. As a result of computer simulation in the fuzzyTECH environment, data comparable with experimental studies were obtained to determine the level of loading of the main elements of the working equipment of mining excavators. Based on a representative sample, a statistical analysis of the data was performed, as a result of which the equation of linear multiple stress regression in the handle of mining excavators was obtained, which allows to make an accurate forecast of the loading of the working equipment of the excavator.
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Date submitted2016-09-04
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Date accepted2016-11-14
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Date published2017-02-22
Methodology of reducing rock bump hazard during room and rillar mining of North Ural deep bauxite deposits
- Authors:
- D. V. Sidorov
The article describes practical experience of using room and pillar mining (RAPM) under conditions of deep horizons and dynamic overburden pressure. It was identified that methods of rock pressure control efficient at high horizons do not meet safety requirements when working at existing depths, that is explained by changes in geodynamic processes during mining. With deeper depth, the geodynamic processes become more intensive and number of pillar and roof failures increase. When working at 800 m the breakage of mine structures became massive and unpredictable, which paused a question of development and implementation of tools for compliance assessment of used elements of RAPM and mining, geological, technical and geodynamic conditions of North Ural bauxite deposits and further development of guidelines for safe mining under conditions of deep horizons and dynamic rock pressure. It describes reasons of mine structure failures in workings depending on natural and man-caused factors, determines possible hazards and objects of geomechanic support. It also includes compliance assessment of tools used for calculations of RAPM structures, forecast and measures for rock tectonic bursts at mines of OAO “Sevuralboksitruda” (SUBR). It describes modernization and development of new geomechanic support of RAPM considering natural and technogenic hazards. The article presents results of experimental testing of new parameters of RAPM construction elements of SUBR mines. It has data on industrial implementation of developed regulatory and guideline documents at these mines for identification of valid parameters of RAPM elements at deep depths.
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Date submitted2014-12-07
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Date accepted2015-02-23
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Date published2015-12-25
Bump hazard evaluation of a rock mass area as a result of its seismic acoustic activity registration
- Authors:
- V. V. Nosov
Ore production in deep rock-bump hazardous mines is closely connected with the need to in-crease workers’ safety, which demands heavy costs of taking preventive shockproof actions and applying expensive protection systems against mountain blows. The article considers a resource forecasting technique and a bump hazard evaluation method for a rock mass area, based on a mi-cromechanical model, which registers acoustic emission of heterogeneous materials, and empirical data, obtained as a result of acoustic signals registration with the help of the model, aimed at seis-mic-acoustic activity evaluation at «Taimir» and «Oktyabrsky» rock mass areas, belonging to Norylsk industrial region.
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Date submitted2009-08-02
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Date accepted2009-10-29
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Date published2010-02-01
Forecasting the power consumption of mines on the basis of stochastic time-series models
- Authors:
- A. A. Chernysh
- O. B. Shonin
The paper is devoted to building up time series models to forecast the power consumption of a mine. The results discussed are obtained using various linear filter models and artificial neural network. The wavelet transform of the raw time series is shown to be an efficient technique to increase the forecasting accuracy.