Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction
Energy consumption planning of an area is very important. It is essential to accurately predict the amount of short-term power required by an area using a highly effective prediction technique. The real-value-genetics-algorithm (RVGA) is the most effective technique that is currently used. However,...
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| Format: | Article |
| Language: | English |
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Universiti Utara Malaysia Press
2024
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| Online Access: | https://repo.uum.edu.my/id/eprint/30347/1/JICT%2023%2001%202024%2025-47.pdf https://doi.org/10.32890/jict2024.23.1.2 |
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| author | Musa, Wahab Ku Mahamud, Ku Ruhana Salim, Sardi Sediyono, Agung |
| author_facet | Musa, Wahab Ku Mahamud, Ku Ruhana Salim, Sardi Sediyono, Agung |
| author_sort | Musa, Wahab |
| collection | UUM |
| description | Energy consumption planning of an area is very important. It is essential to accurately predict the amount of short-term power required by an area using a highly effective prediction technique. The real-value-genetics-algorithm (RVGA) is the most effective technique that is currently used. However, the RVGA has some drawbacks, including the fact that it gets caught in premature convergence even when the search is performed over long iterations. This study proposes a hybrid prediction algorithm which comprises the RVGA and the extended-Nelder-Mead (ENM) algorithm. The ENM was implemented to speed up the search for the best among all solutions produced by the RVGA. The RVGA was configured to run under small iterations, and the ENM was used to achieve convergence. Experiments were performed on historical datasets containing the monthly electricity demand of the Gorontalo area, a region in Indonesia. The performance of the hybrid algorithm was compared to the hybrid Genetic Algorithm-Particle Swarm Optimisation (GA-PSO) and Real Coded-Genetic Algorithm (RC-GA) energy demand models based on the mean-absolute-percentage-error (MAPE), mean-square-error (MSE), root-mean-square-error (RMSE), and mean-absolute-deviation (MAD) error rates. The results showed that the proposed hybrid algorithm’s MAPE, MSE, RMSE, and MAD errors were 2.95 percent, 0.13 percent, 0.36 percent and 1.29 percent, respectively. Based on the accuracy measure obtained from this study, it implies that the RVGAENM hybrid is the best model for forecasting monthly electricity demand. |
| first_indexed | 2024-07-04T06:44:59Z |
| format | Article |
| id | uum-30347 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:44:59Z |
| publishDate | 2024 |
| publisher | Universiti Utara Malaysia Press |
| record_format | dspace |
| spelling | uum-303472024-02-01T13:55:13Z https://repo.uum.edu.my/id/eprint/30347/ Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction Musa, Wahab Ku Mahamud, Ku Ruhana Salim, Sardi Sediyono, Agung TA Engineering (General). Civil engineering (General) Energy consumption planning of an area is very important. It is essential to accurately predict the amount of short-term power required by an area using a highly effective prediction technique. The real-value-genetics-algorithm (RVGA) is the most effective technique that is currently used. However, the RVGA has some drawbacks, including the fact that it gets caught in premature convergence even when the search is performed over long iterations. This study proposes a hybrid prediction algorithm which comprises the RVGA and the extended-Nelder-Mead (ENM) algorithm. The ENM was implemented to speed up the search for the best among all solutions produced by the RVGA. The RVGA was configured to run under small iterations, and the ENM was used to achieve convergence. Experiments were performed on historical datasets containing the monthly electricity demand of the Gorontalo area, a region in Indonesia. The performance of the hybrid algorithm was compared to the hybrid Genetic Algorithm-Particle Swarm Optimisation (GA-PSO) and Real Coded-Genetic Algorithm (RC-GA) energy demand models based on the mean-absolute-percentage-error (MAPE), mean-square-error (MSE), root-mean-square-error (RMSE), and mean-absolute-deviation (MAD) error rates. The results showed that the proposed hybrid algorithm’s MAPE, MSE, RMSE, and MAD errors were 2.95 percent, 0.13 percent, 0.36 percent and 1.29 percent, respectively. Based on the accuracy measure obtained from this study, it implies that the RVGAENM hybrid is the best model for forecasting monthly electricity demand. Universiti Utara Malaysia Press 2024 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/30347/1/JICT%2023%2001%202024%2025-47.pdf Musa, Wahab and Ku Mahamud, Ku Ruhana and Salim, Sardi and Sediyono, Agung (2024) Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction. Journal of Information and Communication Technology, 23 (1). pp. 25-47. ISSN 2180-3862 https://e-journal.uum.edu.my/index.php/jict/article/view/20064 https://doi.org/10.32890/jict2024.23.1.2 https://doi.org/10.32890/jict2024.23.1.2 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Musa, Wahab Ku Mahamud, Ku Ruhana Salim, Sardi Sediyono, Agung Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction |
| title | Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction |
| title_full | Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction |
| title_fullStr | Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction |
| title_full_unstemmed | Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction |
| title_short | Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction |
| title_sort | hybrid real value genetic algorithm and extended nelder mead algorithm for short term energy demand prediction |
| topic | TA Engineering (General). Civil engineering (General) |
| url | https://repo.uum.edu.my/id/eprint/30347/1/JICT%2023%2001%202024%2025-47.pdf https://doi.org/10.32890/jict2024.23.1.2 |
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