Comparative Analysis of Deep Learning Techniques Based COVID-19 Impact Assessment on Electricity Consumption in Distribution Network
Energy is a fundamental human need for several activities. Energy can be impacted by quite a number of factors ranging from technical to social and environmental. The impact of COVID-19 outbreak on the energy sector is enormous with serious global socioeconomic disruptions affecting all economic se...
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| Format: | Article |
| Language: | English |
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Faculty of Engineering and Technology
2023-09-01
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| Series: | Nigerian Journal of Technological Development |
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| Online Access: | https://journal.njtd.com.ng/index.php/njtd/article/view/1375 |
| _version_ | 1797678065158979584 |
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| author | A. O. Amole S. Oladipo D. Ighravwe K. A. Makinde J. Ajibola |
| author_facet | A. O. Amole S. Oladipo D. Ighravwe K. A. Makinde J. Ajibola |
| author_sort | A. O. Amole |
| collection | DOAJ |
| description |
Energy is a fundamental human need for several activities. Energy can be impacted by quite a number of factors ranging from technical to social and environmental. The impact of COVID-19 outbreak on the energy sector is enormous with serious global socioeconomic disruptions affecting all economic sectors, including tourism, industry, higher education, and electricity industry. Based on the unstructured data obtained from Eko Electricity Distribution Company this paper proposes three deep learning (DL) models namely: Long Short-Term Memory (LSTM), Simple Recurrent Neural Network (SimpleRNN), and Gated Recurrent Unit (GRU) were used to analyse the effect of COVID-19 pandemic on energy consumption and predict future energy consumption in various district in Lagos, Nigeria. The models were evaluated using the following performance metrics namely: Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). On the overall, the lowest MAPE, MAE, RMSE, and MSE of 0.120, 71.073, 93.981, and 8832.466 were obtained for LSTM in Orile, SRNN in Ijora, GRU in Ijora, and GRU in Ijora, respectively. Generally, the GRU performed better in predicting the energy consumption in most of the districts of the case study than the LSTM and SimpleRNN. Hence, it can be considered the optimal model for energy consumption prediction in the case study. The importance of having this model is that they can help the government and other stakeholder in economic planning.
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| first_indexed | 2024-03-11T22:53:59Z |
| format | Article |
| id | doaj.art-ffc01d394d164086bd969217a483359c |
| institution | Directory Open Access Journal |
| issn | 2437-2110 |
| language | English |
| last_indexed | 2024-03-11T22:53:59Z |
| publishDate | 2023-09-01 |
| publisher | Faculty of Engineering and Technology |
| record_format | Article |
| series | Nigerian Journal of Technological Development |
| spelling | doaj.art-ffc01d394d164086bd969217a483359c2023-09-21T21:28:44ZengFaculty of Engineering and TechnologyNigerian Journal of Technological Development2437-21102023-09-01203Comparative Analysis of Deep Learning Techniques Based COVID-19 Impact Assessment on Electricity Consumption in Distribution NetworkA. O. Amole0S. OladipoD. IghravweK. A. MakindeJ. AjibolaBells University of Technology Energy is a fundamental human need for several activities. Energy can be impacted by quite a number of factors ranging from technical to social and environmental. The impact of COVID-19 outbreak on the energy sector is enormous with serious global socioeconomic disruptions affecting all economic sectors, including tourism, industry, higher education, and electricity industry. Based on the unstructured data obtained from Eko Electricity Distribution Company this paper proposes three deep learning (DL) models namely: Long Short-Term Memory (LSTM), Simple Recurrent Neural Network (SimpleRNN), and Gated Recurrent Unit (GRU) were used to analyse the effect of COVID-19 pandemic on energy consumption and predict future energy consumption in various district in Lagos, Nigeria. The models were evaluated using the following performance metrics namely: Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). On the overall, the lowest MAPE, MAE, RMSE, and MSE of 0.120, 71.073, 93.981, and 8832.466 were obtained for LSTM in Orile, SRNN in Ijora, GRU in Ijora, and GRU in Ijora, respectively. Generally, the GRU performed better in predicting the energy consumption in most of the districts of the case study than the LSTM and SimpleRNN. Hence, it can be considered the optimal model for energy consumption prediction in the case study. The importance of having this model is that they can help the government and other stakeholder in economic planning. https://journal.njtd.com.ng/index.php/njtd/article/view/1375COVID-19Energy ConsumptionDistribution Network |
| spellingShingle | A. O. Amole S. Oladipo D. Ighravwe K. A. Makinde J. Ajibola Comparative Analysis of Deep Learning Techniques Based COVID-19 Impact Assessment on Electricity Consumption in Distribution Network Nigerian Journal of Technological Development COVID-19 Energy Consumption Distribution Network |
| title | Comparative Analysis of Deep Learning Techniques Based COVID-19 Impact Assessment on Electricity Consumption in Distribution Network |
| title_full | Comparative Analysis of Deep Learning Techniques Based COVID-19 Impact Assessment on Electricity Consumption in Distribution Network |
| title_fullStr | Comparative Analysis of Deep Learning Techniques Based COVID-19 Impact Assessment on Electricity Consumption in Distribution Network |
| title_full_unstemmed | Comparative Analysis of Deep Learning Techniques Based COVID-19 Impact Assessment on Electricity Consumption in Distribution Network |
| title_short | Comparative Analysis of Deep Learning Techniques Based COVID-19 Impact Assessment on Electricity Consumption in Distribution Network |
| title_sort | comparative analysis of deep learning techniques based covid 19 impact assessment on electricity consumption in distribution network |
| topic | COVID-19 Energy Consumption Distribution Network |
| url | https://journal.njtd.com.ng/index.php/njtd/article/view/1375 |
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