Global control of electrical supply: A variational mode decomposition-aided deep learning model for energy consumption prediction
Global energy consumption has increased significantly in recent decades due to changes in the industrial and economic sectors. Accurate demand estimates are critical for decision-makers to save operation and maintenance costs, improve energy reliability, and make informed decisions for future develo...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723012192 |
| _version_ | 1797378659776987136 |
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| author | Abul Abrar Masrur Ahmed Nadjem Bailek Laith Abualigah Kada Bouchouicha Alban Kuriqi Alireza Sharifi Pooya Sareh Abdullah Mohammad Ghazi Al khatib Pradeep Mishra Ilhami Colak El-Sayed M. El-kenawy |
| author_facet | Abul Abrar Masrur Ahmed Nadjem Bailek Laith Abualigah Kada Bouchouicha Alban Kuriqi Alireza Sharifi Pooya Sareh Abdullah Mohammad Ghazi Al khatib Pradeep Mishra Ilhami Colak El-Sayed M. El-kenawy |
| author_sort | Abul Abrar Masrur Ahmed |
| collection | DOAJ |
| description | Global energy consumption has increased significantly in recent decades due to changes in the industrial and economic sectors. Accurate demand estimates are critical for decision-makers to save operation and maintenance costs, improve energy reliability, and make informed decisions for future development. This study evaluates a newly proposed soft technique called Variational Mode Decomposition (VMD) to improve the accuracy of power consumption forecasts. To validate the experimental results, we compared the predicted energy consumption values with measured values from five geographically diverse countries, including developed and developing countries. The study examined different time horizons and performed seasonal evaluations. The VMD-BiGRU and VMD-LSTM models show consistent and superior prediction accuracy, outperforming other models by 20% to 50% on all evaluation measures. In addition, we evaluated the efficiency of VMD-based models over different forecast horizons and find that they are most effective for short- to medium-term forecasts (1 to 12 months). For longer-term forecasts, we recommend combining VMD with specialized techniques. Overall, this study recommends using VMD to forecast electricity consumption in different regions, emphasizing carefully considering forecast horizons for optimal results. |
| first_indexed | 2024-03-08T20:10:52Z |
| format | Article |
| id | doaj.art-267441cc4cc74703a09e58678ed8b82a |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-08T20:10:52Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-267441cc4cc74703a09e58678ed8b82a2023-12-23T05:21:31ZengElsevierEnergy Reports2352-48472023-11-011021522165Global control of electrical supply: A variational mode decomposition-aided deep learning model for energy consumption predictionAbul Abrar Masrur Ahmed0Nadjem Bailek1Laith Abualigah2Kada Bouchouicha3Alban Kuriqi4Alireza Sharifi5Pooya Sareh6Abdullah Mohammad Ghazi Al khatib7Pradeep Mishra8Ilhami Colak9El-Sayed M. El-kenawy10Department of Infrastructure Engineering, The University of Melbourne, Victoria 3010, AustraliaEnergies and Materials Research Laboratory, Faculty of Sciences and Technology, University of Tamanghasset, Algeria; Laboratory of Mathematics Modeling and Applications, Department of Mathematics and Computer Science, Faculty of Matter Sciences, Mathematics, and Computer Science, Ahmed Draia University of Adrar, Adrar 01000, AlgeriaComputer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan; Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon; Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan; MEU Research Unit, Middle East University, Amman 11831, Jordan; Applied science research center, Applied science private university, Amman 11931, Jordan; School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia; School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, MalaysiaCentre de Développement des Energies Renouvelables, CDER, BP 62 Route de l’Observatoire, Bouzaréah 16340, Algiers, AlgeriaCERIS, Instituto Superior Tecnico, Universidade de Lisboa, Lisbon, Portugal; Civil Engineering Department, University of Business and Technology, 10000 Pristina, KosovoDepartment of Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, 16785-136, Tehran, IranSchool of Engineering, University of Liverpool, Liverpool, L69 3GH, UK; School of Engineering, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK; Corresponding author.Department of Banking and Insurance, Faculty of Economics, Damascus University, Damascus, Syrian Arab RepublicCollege of Agriculture, Rewa, Jawaharlal Nehru Krishi Vishwavidyalaya, 486001, IndiaEngineering and Architecture Faculty, Nisantasi University, Istanbul, TurkeyDepartment of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, EgyptGlobal energy consumption has increased significantly in recent decades due to changes in the industrial and economic sectors. Accurate demand estimates are critical for decision-makers to save operation and maintenance costs, improve energy reliability, and make informed decisions for future development. This study evaluates a newly proposed soft technique called Variational Mode Decomposition (VMD) to improve the accuracy of power consumption forecasts. To validate the experimental results, we compared the predicted energy consumption values with measured values from five geographically diverse countries, including developed and developing countries. The study examined different time horizons and performed seasonal evaluations. The VMD-BiGRU and VMD-LSTM models show consistent and superior prediction accuracy, outperforming other models by 20% to 50% on all evaluation measures. In addition, we evaluated the efficiency of VMD-based models over different forecast horizons and find that they are most effective for short- to medium-term forecasts (1 to 12 months). For longer-term forecasts, we recommend combining VMD with specialized techniques. Overall, this study recommends using VMD to forecast electricity consumption in different regions, emphasizing carefully considering forecast horizons for optimal results.http://www.sciencedirect.com/science/article/pii/S2352484723012192Global energy consumptionSoft computing modelsEnergy consumption forecastingDeep learningLong-term predictions |
| spellingShingle | Abul Abrar Masrur Ahmed Nadjem Bailek Laith Abualigah Kada Bouchouicha Alban Kuriqi Alireza Sharifi Pooya Sareh Abdullah Mohammad Ghazi Al khatib Pradeep Mishra Ilhami Colak El-Sayed M. El-kenawy Global control of electrical supply: A variational mode decomposition-aided deep learning model for energy consumption prediction Energy Reports Global energy consumption Soft computing models Energy consumption forecasting Deep learning Long-term predictions |
| title | Global control of electrical supply: A variational mode decomposition-aided deep learning model for energy consumption prediction |
| title_full | Global control of electrical supply: A variational mode decomposition-aided deep learning model for energy consumption prediction |
| title_fullStr | Global control of electrical supply: A variational mode decomposition-aided deep learning model for energy consumption prediction |
| title_full_unstemmed | Global control of electrical supply: A variational mode decomposition-aided deep learning model for energy consumption prediction |
| title_short | Global control of electrical supply: A variational mode decomposition-aided deep learning model for energy consumption prediction |
| title_sort | global control of electrical supply a variational mode decomposition aided deep learning model for energy consumption prediction |
| topic | Global energy consumption Soft computing models Energy consumption forecasting Deep learning Long-term predictions |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723012192 |
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