Deep Learning-Assisted Short-Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU
Over the decades, a rapid upsurge in electricity demand has been observed due to overpopulation and technological growth. The optimum production of energy is mandatory to preserve it and improve the energy infrastructure using the power load forecasting (PLF) method. However, the complex energy syst...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/2993184 |
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| author | Fath U Min Ullah Amin Ullah Noman Khan Mi Young Lee Seungmin Rho Sung Wook Baik |
| author_facet | Fath U Min Ullah Amin Ullah Noman Khan Mi Young Lee Seungmin Rho Sung Wook Baik |
| author_sort | Fath U Min Ullah |
| collection | DOAJ |
| description | Over the decades, a rapid upsurge in electricity demand has been observed due to overpopulation and technological growth. The optimum production of energy is mandatory to preserve it and improve the energy infrastructure using the power load forecasting (PLF) method. However, the complex energy systems’ transition towards more robust and intelligent system will ensure its momentous role in the industrial and economical world. The extraction of deep knowledge from complex energy data patterns requires an efficient and computationally intelligent deep learning-based method to examine the future electricity demand. Stand by this, we propose an intelligent deep learning-based PLF method where at first the data collected from the house through meters are fed into the pre-assessment step. Next, the sequence of refined data is passed into a modified convolutional long short-term memory (ConvLSTM) network that captures the spatiotemporal correlations from the sequence and generates the feature maps. The generated feature map is forward propagated into a deep gated recurrent unit (GRU) network for learning, which provides the final PLF. We experimentally proved that the proposed method revealed promising results using mean square error (MSE) and root mean square error (RMSE) and outperformed state of the art using the competitive power load dataset.(Github Code). (Github code: https://github.com/FathUMinUllah3797/ConvLSTM-Deep_GRU). |
| format | Article |
| id | doaj-art-1b968f658419406c9d9f0107425f8fbc |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-1b968f658419406c9d9f0107425f8fbc2025-02-03T01:24:10ZengWileyComplexity1099-05262022-01-01202210.1155/2022/2993184Deep Learning-Assisted Short-Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRUFath U Min Ullah0Amin Ullah1Noman Khan2Mi Young Lee3Seungmin Rho4Sung Wook Baik5Sejong UniversityCollaborative Robotics and Intelligent Systems (CoRIS) InstituteSejong UniversitySejong UniversityDepartment of Industrial SecuritySejong UniversityOver the decades, a rapid upsurge in electricity demand has been observed due to overpopulation and technological growth. The optimum production of energy is mandatory to preserve it and improve the energy infrastructure using the power load forecasting (PLF) method. However, the complex energy systems’ transition towards more robust and intelligent system will ensure its momentous role in the industrial and economical world. The extraction of deep knowledge from complex energy data patterns requires an efficient and computationally intelligent deep learning-based method to examine the future electricity demand. Stand by this, we propose an intelligent deep learning-based PLF method where at first the data collected from the house through meters are fed into the pre-assessment step. Next, the sequence of refined data is passed into a modified convolutional long short-term memory (ConvLSTM) network that captures the spatiotemporal correlations from the sequence and generates the feature maps. The generated feature map is forward propagated into a deep gated recurrent unit (GRU) network for learning, which provides the final PLF. We experimentally proved that the proposed method revealed promising results using mean square error (MSE) and root mean square error (RMSE) and outperformed state of the art using the competitive power load dataset.(Github Code). (Github code: https://github.com/FathUMinUllah3797/ConvLSTM-Deep_GRU).http://dx.doi.org/10.1155/2022/2993184 |
| spellingShingle | Fath U Min Ullah Amin Ullah Noman Khan Mi Young Lee Seungmin Rho Sung Wook Baik Deep Learning-Assisted Short-Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU Complexity |
| title | Deep Learning-Assisted Short-Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU |
| title_full | Deep Learning-Assisted Short-Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU |
| title_fullStr | Deep Learning-Assisted Short-Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU |
| title_full_unstemmed | Deep Learning-Assisted Short-Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU |
| title_short | Deep Learning-Assisted Short-Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU |
| title_sort | deep learning assisted short term power load forecasting using deep convolutional lstm and stacked gru |
| url | http://dx.doi.org/10.1155/2022/2993184 |
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