Multivariate bidirectional gate recurrent unit for improving accuracy of energy prediction
Energy prediction is an important process in energy management, especially regarding demand response. Energy predictions are often carried out for load forecasting or energy generation forecasting of renewable energy. This paper explains the implementation of multi-variables in the development of re...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | ICT Express |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405959524001255 |
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| author | Quota Alief Sias Rahma Gantassi Yonghoon Choi |
| author_facet | Quota Alief Sias Rahma Gantassi Yonghoon Choi |
| author_sort | Quota Alief Sias |
| collection | DOAJ |
| description | Energy prediction is an important process in energy management, especially regarding demand response. Energy predictions are often carried out for load forecasting or energy generation forecasting of renewable energy. This paper explains the implementation of multi-variables in the development of recurrence neural network models to predict load energy and generation energy. The proposed main model is a multi-variate bidirectional GRU combined with a periodic feature pattern. The proposed model will also be compared with the fundamental bidirectional models of the GRU and LSTM models. For load prediction, the variables used are all energy supply data and periodic features. Meanwhile, for photovoltaic generation energy predictions, additional weather data is used because energy generation is very dependent on solar radiation and ambient conditions. Load prediction data is built using daily and hourly energy prediction data. Meanwhile, solar energy prediction is constructed with data every minute. The results show that the proposed model obtains the best prediction results for all test data on a daily, hourly, or minute basis. The model also shows the fastest execution time performance compared to other models. |
| format | Article |
| id | doaj-art-2925f3b78f0146a4a0797c249c2ad574 |
| institution | OA Journals |
| issn | 2405-9595 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | ICT Express |
| spelling | doaj-art-2925f3b78f0146a4a0797c249c2ad5742025-08-20T02:13:52ZengElsevierICT Express2405-95952025-02-01111808610.1016/j.icte.2024.10.002Multivariate bidirectional gate recurrent unit for improving accuracy of energy predictionQuota Alief Sias0Rahma Gantassi1Yonghoon Choi2Department of Electrical Engineering, Chonnam National University, Gwangju 61186, South KoreaDepartment of Electrical Engineering, Chonnam National University, Gwangju 61186, South KoreaCorresponding author.; Department of Electrical Engineering, Chonnam National University, Gwangju 61186, South KoreaEnergy prediction is an important process in energy management, especially regarding demand response. Energy predictions are often carried out for load forecasting or energy generation forecasting of renewable energy. This paper explains the implementation of multi-variables in the development of recurrence neural network models to predict load energy and generation energy. The proposed main model is a multi-variate bidirectional GRU combined with a periodic feature pattern. The proposed model will also be compared with the fundamental bidirectional models of the GRU and LSTM models. For load prediction, the variables used are all energy supply data and periodic features. Meanwhile, for photovoltaic generation energy predictions, additional weather data is used because energy generation is very dependent on solar radiation and ambient conditions. Load prediction data is built using daily and hourly energy prediction data. Meanwhile, solar energy prediction is constructed with data every minute. The results show that the proposed model obtains the best prediction results for all test data on a daily, hourly, or minute basis. The model also shows the fastest execution time performance compared to other models.http://www.sciencedirect.com/science/article/pii/S2405959524001255Energy predictionLoadMultivariatePhotovoltaic |
| spellingShingle | Quota Alief Sias Rahma Gantassi Yonghoon Choi Multivariate bidirectional gate recurrent unit for improving accuracy of energy prediction ICT Express Energy prediction Load Multivariate Photovoltaic |
| title | Multivariate bidirectional gate recurrent unit for improving accuracy of energy prediction |
| title_full | Multivariate bidirectional gate recurrent unit for improving accuracy of energy prediction |
| title_fullStr | Multivariate bidirectional gate recurrent unit for improving accuracy of energy prediction |
| title_full_unstemmed | Multivariate bidirectional gate recurrent unit for improving accuracy of energy prediction |
| title_short | Multivariate bidirectional gate recurrent unit for improving accuracy of energy prediction |
| title_sort | multivariate bidirectional gate recurrent unit for improving accuracy of energy prediction |
| topic | Energy prediction Load Multivariate Photovoltaic |
| url | http://www.sciencedirect.com/science/article/pii/S2405959524001255 |
| work_keys_str_mv | AT quotaaliefsias multivariatebidirectionalgaterecurrentunitforimprovingaccuracyofenergyprediction AT rahmagantassi multivariatebidirectionalgaterecurrentunitforimprovingaccuracyofenergyprediction AT yonghoonchoi multivariatebidirectionalgaterecurrentunitforimprovingaccuracyofenergyprediction |