The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption
In article the analysis of electric power consumption and problems of power saving on coal mines are considered. Nowadays the share of conditionally constant costs of electric power for providing safe working conditions underground on coal mines is big. Therefore, the power efficiency of underground...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2017-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://doi.org/10.1051/e3sconf/20172102002 |
| _version_ | 1819147883817992192 |
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| author | Efremenko Vladimir Belyaevsky Roman Skrebneva Evgeniya |
| author_facet | Efremenko Vladimir Belyaevsky Roman Skrebneva Evgeniya |
| author_sort | Efremenko Vladimir |
| collection | DOAJ |
| description | In article the analysis of electric power consumption and problems of power saving on coal mines are considered. Nowadays the share of conditionally constant costs of electric power for providing safe working conditions underground on coal mines is big. Therefore, the power efficiency of underground coal mining depends on electric power expense of the main technological processes and size of conditionally constant costs. The important direction of increase of power efficiency of coal mining is forecasting of a power consumption and monitoring of electric power expense. One of the main approaches to reducing of electric power costs is increase in accuracy of the enterprise demand in the wholesale electric power market. It is offered to use artificial neural networks to forecasting of day-ahead power consumption with hourly breakdown. At the same time use of neural and indistinct (hybrid) systems on the principles of fuzzy logic, neural networks and genetic algorithms is more preferable. This model allows to do exact short-term forecasts at a small array of input data. A set of the input parameters characterizing mining-and-geological and technological features of the enterprise is offered. |
| first_indexed | 2024-12-22T13:36:54Z |
| format | Article |
| id | doaj.art-fe8848ec0bdd4885b4658d305be7a388 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-22T13:36:54Z |
| publishDate | 2017-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-fe8848ec0bdd4885b4658d305be7a3882022-12-21T18:24:02ZengEDP SciencesE3S Web of Conferences2267-12422017-01-01210200210.1051/e3sconf/20172102002e3sconf_2iims2017_02002The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power ConsumptionEfremenko VladimirBelyaevsky RomanSkrebneva EvgeniyaIn article the analysis of electric power consumption and problems of power saving on coal mines are considered. Nowadays the share of conditionally constant costs of electric power for providing safe working conditions underground on coal mines is big. Therefore, the power efficiency of underground coal mining depends on electric power expense of the main technological processes and size of conditionally constant costs. The important direction of increase of power efficiency of coal mining is forecasting of a power consumption and monitoring of electric power expense. One of the main approaches to reducing of electric power costs is increase in accuracy of the enterprise demand in the wholesale electric power market. It is offered to use artificial neural networks to forecasting of day-ahead power consumption with hourly breakdown. At the same time use of neural and indistinct (hybrid) systems on the principles of fuzzy logic, neural networks and genetic algorithms is more preferable. This model allows to do exact short-term forecasts at a small array of input data. A set of the input parameters characterizing mining-and-geological and technological features of the enterprise is offered.https://doi.org/10.1051/e3sconf/20172102002 |
| spellingShingle | Efremenko Vladimir Belyaevsky Roman Skrebneva Evgeniya The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption E3S Web of Conferences |
| title | The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption |
| title_full | The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption |
| title_fullStr | The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption |
| title_full_unstemmed | The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption |
| title_short | The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption |
| title_sort | increase of power efficiency of underground coal mining by the forecasting of electric power consumption |
| url | https://doi.org/10.1051/e3sconf/20172102002 |
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