NEURAL NETWORK FORECASTING OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE
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 p...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Kharkiv National University of Radio Electronics
2021-03-01
|
| Series: | Сучасний стан наукових досліджень та технологій в промисловості |
| Subjects: | |
| Online Access: | https://itssi-journal.com/index.php/ittsi/article/view/255 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850045368072404992 |
|---|---|
| author | Anna Bakurova Olesia Yuskiv Dima Shyrokorad Anton Riabenko Elina Tereschenko |
| author_facet | Anna Bakurova Olesia Yuskiv Dima Shyrokorad Anton Riabenko Elina Tereschenko |
| author_sort | Anna Bakurova |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c67dfc25977944ac8deec041c5b555d3 |
| institution | DOAJ |
| issn | 2522-9818 2524-2296 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | Kharkiv National University of Radio Electronics |
| record_format | Article |
| series | Сучасний стан наукових досліджень та технологій в промисловості |
| spelling | doaj-art-c67dfc25977944ac8deec041c5b555d32025-08-20T02:54:42ZengKharkiv National University of Radio ElectronicsСучасний стан наукових досліджень та технологій в промисловості2522-98182524-22962021-03-011 (15)10.30837/ITSSI.2021.15.014NEURAL NETWORK FORECASTING OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISEAnna Bakurova0Olesia Yuskiv1Dima Shyrokorad2Anton Riabenko3Elina Tereschenko4National University "Zaporizhzhia Polytechnic"National Aerospace University "Kharkiv Aviation Institute"National Aerospace University "Kharkiv Aviation Institute"National Aerospace University "Kharkiv Aviation Institute"National Aerospace University "Kharkiv Aviation Institute"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.https://itssi-journal.com/index.php/ittsi/article/view/255energy consumptionforecastingartificial neural networktime series |
| spellingShingle | Anna Bakurova Olesia Yuskiv Dima Shyrokorad Anton Riabenko Elina Tereschenko NEURAL NETWORK FORECASTING OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE Сучасний стан наукових досліджень та технологій в промисловості energy consumption forecasting artificial neural network time series |
| title | NEURAL NETWORK FORECASTING OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE |
| title_full | NEURAL NETWORK FORECASTING OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE |
| title_fullStr | NEURAL NETWORK FORECASTING OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE |
| title_full_unstemmed | NEURAL NETWORK FORECASTING OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE |
| title_short | NEURAL NETWORK FORECASTING OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE |
| title_sort | neural network forecasting of energy consumption of a metallurgical enterprise |
| topic | energy consumption forecasting artificial neural network time series |
| url | https://itssi-journal.com/index.php/ittsi/article/view/255 |
| work_keys_str_mv | AT annabakurova neuralnetworkforecastingofenergyconsumptionofametallurgicalenterprise AT olesiayuskiv neuralnetworkforecastingofenergyconsumptionofametallurgicalenterprise AT dimashyrokorad neuralnetworkforecastingofenergyconsumptionofametallurgicalenterprise AT antonriabenko neuralnetworkforecastingofenergyconsumptionofametallurgicalenterprise AT elinatereschenko neuralnetworkforecastingofenergyconsumptionofametallurgicalenterprise |