A new approach to seasonal energy consumption forecasting using temporal convolutional networks
There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature. It is well known that energy forecasting plays a crucial role in several applications in smart grids, including demand-side...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123023004231 |
| _version_ | 1797682444988579840 |
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| author | Abdul Khalique Shaikh Amril Nazir Nadia Khalique Abdul Salam Shah Naresh Adhikari |
| author_facet | Abdul Khalique Shaikh Amril Nazir Nadia Khalique Abdul Salam Shah Naresh Adhikari |
| author_sort | Abdul Khalique Shaikh |
| collection | DOAJ |
| description | There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature. It is well known that energy forecasting plays a crucial role in several applications in smart grids, including demand-side management, optimum dispatch, and load shedding. A significant challenge in smart grid models is managing forecasts efficiently while ensuring the slightest feasible prediction error. A type of artificial neural networks such as recurrent neural networks, are frequently used to forecast time series data. However, due to certain limitations like vanishing gradients and lack of memory retention of recurrent neural networks, sequential data should be modeled using convolutional networks. The reason is that they have strong capabilities to solve complex problems better than recurrent neural networks. In this research, a temporal convolutional network is proposed to handle seasonal short-term energy forecasting. The proposed temporal convolutional network computes outputs in parallel, reducing the computation time compared to the recurrent neural networks. Further performance comparison with the traditional long short-term memory in terms of MAD and sMAPE has proved that the proposed model has outperformed the recurrent neural network. |
| first_indexed | 2024-03-11T23:59:49Z |
| format | Article |
| id | doaj.art-2188ec619f134429833d9d1213f56543 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-03-11T23:59:49Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-2188ec619f134429833d9d1213f565432023-09-18T04:30:40ZengElsevierResults in Engineering2590-12302023-09-0119101296A new approach to seasonal energy consumption forecasting using temporal convolutional networksAbdul Khalique Shaikh0Amril Nazir1Nadia Khalique2Abdul Salam Shah3Naresh Adhikari4Department of Information Systems, Sultan Qaboos University, Muscat, 123, Oman; Corresponding author.Department of Information Systems and Technology Management, Zayed University, Abu Dhabi, 144534, United Arab EmiratesDepartment of Information Systems, Sultan Qaboos University, Muscat, 123, OmanDepartment of Computer Engineering, University of Kuala Lumpur (UniKl-MIIT), Kuala Lumpur, 50250, MalaysiaDepartment of Computer Science, Slippery Rock University, 1 Morrow Way, Slippery Rock, PA 16057, USAThere has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature. It is well known that energy forecasting plays a crucial role in several applications in smart grids, including demand-side management, optimum dispatch, and load shedding. A significant challenge in smart grid models is managing forecasts efficiently while ensuring the slightest feasible prediction error. A type of artificial neural networks such as recurrent neural networks, are frequently used to forecast time series data. However, due to certain limitations like vanishing gradients and lack of memory retention of recurrent neural networks, sequential data should be modeled using convolutional networks. The reason is that they have strong capabilities to solve complex problems better than recurrent neural networks. In this research, a temporal convolutional network is proposed to handle seasonal short-term energy forecasting. The proposed temporal convolutional network computes outputs in parallel, reducing the computation time compared to the recurrent neural networks. Further performance comparison with the traditional long short-term memory in terms of MAD and sMAPE has proved that the proposed model has outperformed the recurrent neural network.http://www.sciencedirect.com/science/article/pii/S2590123023004231Energy forecastingSeasonal energySmart gridsTemporal convolutional networks |
| spellingShingle | Abdul Khalique Shaikh Amril Nazir Nadia Khalique Abdul Salam Shah Naresh Adhikari A new approach to seasonal energy consumption forecasting using temporal convolutional networks Results in Engineering Energy forecasting Seasonal energy Smart grids Temporal convolutional networks |
| title | A new approach to seasonal energy consumption forecasting using temporal convolutional networks |
| title_full | A new approach to seasonal energy consumption forecasting using temporal convolutional networks |
| title_fullStr | A new approach to seasonal energy consumption forecasting using temporal convolutional networks |
| title_full_unstemmed | A new approach to seasonal energy consumption forecasting using temporal convolutional networks |
| title_short | A new approach to seasonal energy consumption forecasting using temporal convolutional networks |
| title_sort | new approach to seasonal energy consumption forecasting using temporal convolutional networks |
| topic | Energy forecasting Seasonal energy Smart grids Temporal convolutional networks |
| url | http://www.sciencedirect.com/science/article/pii/S2590123023004231 |
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