The article reviews the existing methods of increasing the energy efficiency of electric transport by analyzing and studying the methods of increasing the energy storage resource. It is grouped according to methods, approaches, and solutions. The most effective methods and ways of their implementation are identified. General methods of increasing energy efficiency, methods of increasing recuperation during braking, methods of energy-efficient energy consumption, the use of energy-saving technologies, and improving the energy efficiency of the traction drive are considered. The purpose of this work is to identify the main operating factors on the basis of a critical review of existing methods for assessing the technical condition of batteries and experimental results on the degradation of lithium-ion batteries. Using the great experience of the research group in the field of modeling, diagnostics, and forecasting of life of electric cars, as well as their intellectual management, the new theoretical and practical methods of integrated assessment of the parameters of the traction battery and state of charge, which are operated in the heavy forced regenerative regimes of electric traction, are created and proposed. A great role is played by the construction of the transport model. The development is based on physical laws that passengers and vehicle owners are unaware of. For each model there is a different area of application, and what is suitable for one object may not be suitable for another. The overview shows that there is no one-size-fits-all way to improve energy efficiency. It is necessary to make a choice among several proposed models after a thorough feasibility study.

https://www.mdpi.com/1996-1073/16/2/729/pdf?version=1673603854

Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption

Decarbonization of the mining industry on the basis of closing the energy generation, on the basis of cogeneration of coal mine methane, and on the internal consumption of the mine is a promising direction in ensuring sustainable development. Known problems of deep underground mining do not allow for realizing the potential of man-made gas reservoirs due to the deterioration of the conditions of development of reserves of georesources. The aim of the work was to improve recommendations for the substantiation of drilling parameters for undermined drainage boreholes for increasing methane production from unconventional coal-gas collectors. The authors’ approach innovation lies in the possibility of using the established patterns of better natural stability of undermined boreholes to optimize them as spatial orientation parameters in an existing drilling passport for the improvement of methane extraction productivity. For this purpose, smoothing (LOESS) of the experimental data of two similar types of wells was used; then deterministic interpolation methods in combination with a three-dimensional representation of the response function in “gnuplot” were used. As a result, it was found that the increase in the inclination angle from 40° to 60° leads to a significant transformation of the model of the studied process, accompanied by a decline in the dynamics of methane emission and a decrease in the distance of the productive work zone of this type of well from 13 to 5 m before the roof landing, which then is replaced by a sharp increase in the productive work zone up to 35 m ahead of the longwall face. This allows under specific conditions for recommending increasing the productivity of methane capex from technogenic disturbed coal-gas reservoir replacement of wells with a smaller angle of rise to the transition to a more frequent grid of clusters from wells #4.

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Substantiation of Drilling Parameters for Undermined Drainage Boreholes for Increasing Methane Production from Unconventional Coal-Gas Collectors

Aggregation ready flexibility management methods for mechanical ventilation systems in buildings

Among the most widespread systems in industrial plants are automated drive systems, the key and most common element of which is the induction motor. In view of challenging operating conditions of equipment, the task of fault detection based on the analysis of electrical parameters is relevant. The authors propose the identification of patterns characterizing the occurrence and development of the bearing defect by the singular analysis method as applied to the stator current signature. As a result of the decomposition, the time series of the three-phase current are represented by singular triples ordered by decreasing contribution, which are reconstructed into the form of time series for subsequent analysis using a Hankelization of matrices. Experimental studies with bearing damage imitation made it possible to establish the relationship between the changes in the contribution of the reconstructed time series and the presence of different levels of bearing defects. By using the contribution level and tracking the movement of the specific time series, it became possible to observe both the appearance of new components in the current signal and the changes in the contribution of the components corresponding to the defect to the overall structure. The authors verified the clustering results based on a visual assessment of the component matrices’ structure similarity using scattergrams and hierarchical clustering. The reconstruction of the time series from the results of the component grouping allows the use of these components for the subsequent prediction of faults development in electric motors.

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Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current

For the safe, stable, and economic operation of thermal power units in new power systems, the condition-based maintenance mode and storage strategy of key equipment and materials for power generation enterprises were selected. According to the storage linkage demand of condition-based maintenance, a Weibull probability density function was used to calculate spare parts demand, and an intelligent storage optimization model with an availability constraint was established. The application cases of spare parts cost and availability of high-value key equipment and low-value key equipment of coal-fired thermal power units were analyzed, respectively, and the influence of different life spans and the number of covered units on the model were expounded. The results show that the cost of spare parts borne by a single unit is greatly reduced via the optimization of an intelligent inventory (r, Q) strategy on the premise that the availability of units is not less than 99.5%.

Storage Optimization (r, Q) Strategy under Condition-Based Maintenance of Key Equipment of Coal-Fired Power Units in Carbon Neutrality Era

Balancing the production and consumption of electricity is an urgent task. Its implementation largely depends on the means and methods of planning electricity production. Forecasting is one of the planning tools since the availability of an accurate forecast is a mechanism for increasing the validity of management decisions. This study provides an overview of the methods used to predict electricity supply requirements to different objects. The methods have been reviewed analytically, taking into account the forecast classification according to the anticipation period. In this way, the methods used in operative, short-term, medium-term, and long-term forecasting have been considered. Both classical and modern forecasting methods have been identified when forecasting electric energy consumption. Classical forecasting methods are based on the theory of regression and statistical analysis (regression, autoregressive models); probabilistic forecasting methods and modern forecasting methods use classical and deep-machine-learning algorithms, rank analysis methodology, fuzzy set theory, singular spectral analysis, wavelet transformations, Gray models, etc. Due to the need to take into account the specifics of each subject area characterizing an energy facility to obtain reliable forecast results, power consumption modeling remains an urgent task despite a wide variety of other methods. The review was conducted with an assessment of the methods according to the following criteria: labor intensity, requirements for the initial data set, scope of application, accuracy of the forecasting method, the possibility of application for other forecasting horizons. The above classification of methods according to the anticipation period allows highlights the fact that when predicting power consumption for different time intervals, the same methods are often used. Therefore, it is worth emphasizing the importance of classifying the forecast over the forecasting horizon not to differentiate the methods used to predict electricity consumption for each period but to consider the specifics of each type of forecasting (operative, short-term, medium-term, long-term).

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Methods of Forecasting Electric Energy Consumption: A Literature Review

Introduction. Reducing the energy intensity of production is one of the directions of development of enterprises, regions, states, as it affects the level of economic development and environmental well-being. One of the methods that contribute to the rationalization of the use of electric energy is forecasting. The purpose of the study is to develop a model for forecasting electricity consumption, which allows obtaining a reliable forecast of electricity consumption. To achieve this goal, an analysis of literary sources reflecting the current trends of predictive analytics in solving the problem of forecasting electricity consumption was carried out. Materials and methods of research. Singular spectral analysis and deep learning algorithms (multilayer perceptron) were used in the work. At the initial stage of the study, a model of a fully connected neural network (multilayer perceptron) was built, the prediction accuracy of which was more than 94%. Then the algorithm of singular spectral analysis was implemented, and the initial time series of power consumption was decomposed into additive components. The resulting decompositions were used to create a hybrid forecasting model. Research results. A number of experimental studies have been conducted that meet the goal of developing models that most accurately approximate experimental data. The result of the study are two forecasting models: an artificial neural network model and a hybrid model that includes a singular spectral analysis to decompose a number of power consumption into trend and harmonic components and an artificial neural network to predict them. Discussion. With the help of the hybrid model, an accurate result was obtained – the average absolute percentage error for the year is 4.04%. Forecasting was carried out for a year (by months) using data from the previous two years as a training subset. The neural network model provides less accuracy in predicting power consumption over 12 months compared to the hybrid model. Conclusion. Thus, the use of singular spectral analysis made it possible to reduce the magnitude of the forecast error by almost 1%, which confirms the effectiveness of the method and the relevance of the development of hybrid forecasting models. Resume. As a result of the conducted research, it was revealed that one of the most popular and effective forecasting methods is singular spectral analysis and the use of various deep learning algorithms. The developed hybrid model of electric energy consumption for a mining and metallurgical enterprise allows you to build a forecast of electricity consumption, the accuracy of which is more than 95%. Suggestions for practical application and directions for future research. The research results can be useful in the process of production management, since an accurate forecast of electricity consumption is important when planning production, scheduling equipment repairs and maintenance of the electrical network. The prospect of further research is to increase the forecasting horizon, the volume of the initial sample and taking into account additional factors characterizing the technological process.

http://naukagor.ru/Portals/4/Т9 №2 2017/2 2017 печать.pdf?ver=2017-07-03-153919-807

A. Morgoeva

Papers

Methods of Forecasting Electric Energy Consumption: A Literature Review

Development of a hybrid model for predicting the consumption of electrical energy for a mining and metallurgical enterprise