Improving the criteria of electricity consumptionforecasting in petrochemical industrial units based ondeep learning
Accurate forecasting of electricity consumption in petrochemical industrial units is essential for optimizing energy management and ensuring operational efficiency. This study presents a novel deep learning framework that integrates advanced feature engineering and Long Short-Term Memory (LSTM) net...
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| Format: | Article |
| Language: | English |
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OICC Press
2025-06-01
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| Series: | Majlesi Journal of Electrical Engineering |
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| Online Access: | https://oiccpress.com/mjee/article/view/16937 |
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| author | Ehsan Tavakoli Garmaserh Mehran Emadi |
| author_facet | Ehsan Tavakoli Garmaserh Mehran Emadi |
| author_sort | Ehsan Tavakoli Garmaserh |
| collection | DOAJ |
| description |
Accurate forecasting of electricity consumption in petrochemical industrial units is essential for optimizing energy management and ensuring operational efficiency. This study presents a novel deep learning framework that integrates advanced feature engineering and Long Short-Term Memory (LSTM) networks to address the challenges posed by irregular seasonal trends and dynamic consumption patterns. Key innovations include the use of Fourier Transform-based feature extraction for enhanced data representation and a hybrid genetic-sparse matrix optimization technique for feature selection, ensuring high predictive performance. The proposed method effectively mitigates issues related to data irregularities through preprocessing techniques, resulting in improved accuracy and stability in both univariate and multivariate time series forecasting scenarios. Experimental evaluations using benchmark datasets demonstrate significant improvements, achieving a Root Mean Square Error (RMSE) of 0.0693 and a Mean Absolute Percentage Error (MAPE) reduction of over 15% compared to state-of-the-art methods. These results highlight the robustness and practical applicability of the proposed framework for industrial energy consumption forecasting and sustainable energy management.
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| format | Article |
| id | doaj-art-e4c283160cad4475af782daabd6ef254 |
| institution | DOAJ |
| issn | 2345-377X 2345-3796 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | OICC Press |
| record_format | Article |
| series | Majlesi Journal of Electrical Engineering |
| spelling | doaj-art-e4c283160cad4475af782daabd6ef2542025-08-20T03:11:42ZengOICC PressMajlesi Journal of Electrical Engineering2345-377X2345-37962025-06-01192 (June 2025)10.57647/j.mjee.2025.1902.41Improving the criteria of electricity consumptionforecasting in petrochemical industrial units based ondeep learningEhsan Tavakoli GarmaserhMehran Emadi Accurate forecasting of electricity consumption in petrochemical industrial units is essential for optimizing energy management and ensuring operational efficiency. This study presents a novel deep learning framework that integrates advanced feature engineering and Long Short-Term Memory (LSTM) networks to address the challenges posed by irregular seasonal trends and dynamic consumption patterns. Key innovations include the use of Fourier Transform-based feature extraction for enhanced data representation and a hybrid genetic-sparse matrix optimization technique for feature selection, ensuring high predictive performance. The proposed method effectively mitigates issues related to data irregularities through preprocessing techniques, resulting in improved accuracy and stability in both univariate and multivariate time series forecasting scenarios. Experimental evaluations using benchmark datasets demonstrate significant improvements, achieving a Root Mean Square Error (RMSE) of 0.0693 and a Mean Absolute Percentage Error (MAPE) reduction of over 15% compared to state-of-the-art methods. These results highlight the robustness and practical applicability of the proposed framework for industrial energy consumption forecasting and sustainable energy management. https://oiccpress.com/mjee/article/view/16937Electricity Consumption ForecastingPetrochemical Industrial UnitsDeep LearningLong Short-Term Memory |
| spellingShingle | Ehsan Tavakoli Garmaserh Mehran Emadi Improving the criteria of electricity consumptionforecasting in petrochemical industrial units based ondeep learning Majlesi Journal of Electrical Engineering Electricity Consumption Forecasting Petrochemical Industrial Units Deep Learning Long Short-Term Memory |
| title | Improving the criteria of electricity consumptionforecasting in petrochemical industrial units based ondeep learning |
| title_full | Improving the criteria of electricity consumptionforecasting in petrochemical industrial units based ondeep learning |
| title_fullStr | Improving the criteria of electricity consumptionforecasting in petrochemical industrial units based ondeep learning |
| title_full_unstemmed | Improving the criteria of electricity consumptionforecasting in petrochemical industrial units based ondeep learning |
| title_short | Improving the criteria of electricity consumptionforecasting in petrochemical industrial units based ondeep learning |
| title_sort | improving the criteria of electricity consumptionforecasting in petrochemical industrial units based ondeep learning |
| topic | Electricity Consumption Forecasting Petrochemical Industrial Units Deep Learning Long Short-Term Memory |
| url | https://oiccpress.com/mjee/article/view/16937 |
| work_keys_str_mv | AT ehsantavakoligarmaserh improvingthecriteriaofelectricityconsumptionforecastinginpetrochemicalindustrialunitsbasedondeeplearning AT mehranemadi improvingthecriteriaofelectricityconsumptionforecastinginpetrochemicalindustrialunitsbasedondeeplearning |