Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine
Accurate energy consumption forecasting is critical for efficient power distribution management. This study presents a novel approach for optimal allocation forecasting of energy consumption in a power distribution company, utilizing the Least Squares Support Vector Machine (LSSVM) optimized by nove...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier B.V.
2025
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/43804/ http://umpir.ump.edu.my/id/eprint/43804/1/Daily%20allocation%20of%20energy%20consumption%20forecasting.pdf |
| _version_ | 1848826963606634496 |
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| author | Ahmed, Marzia Mohd Herwan, Sulaiman Hassan, Md Maruf Rahaman, Md Atikur Mohammad, Amin |
| author_facet | Ahmed, Marzia Mohd Herwan, Sulaiman Hassan, Md Maruf Rahaman, Md Atikur Mohammad, Amin |
| author_sort | Ahmed, Marzia |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Accurate energy consumption forecasting is critical for efficient power distribution management. This study presents a novel approach for optimal allocation forecasting of energy consumption in a power distribution company, utilizing the Least Squares Support Vector Machine (LSSVM) optimized by novel variants of the Barnacle Mating Optimizer (BMO) such as the new Gooseneck Barnacle Optimizer and Selective Opposition-based constrained BMO. The optimized LSSVM hyper-parameters, specifically the regularization parameter (γ) and the kernel parameter (σ2), were applied to test data to enhance accuracy guided by the Mean Absolute Prediction Error (MAPE), ensuring precise alignment of forecasted values with actual energy consumption data. The results indicate that the novel gooseneck barnacle base-optimized LSSVM provides a robust and reliable solution with accuracy 99.98% for daily energy consumption for allocation forecasting, making it a valuable tool for power distribution companies aiming to optimize their resource allocation and planning processes. |
| first_indexed | 2025-11-15T03:53:10Z |
| format | Article |
| id | ump-43804 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:53:10Z |
| publishDate | 2025 |
| publisher | Elsevier B.V. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-438042025-02-13T08:38:23Z http://umpir.ump.edu.my/id/eprint/43804/ Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine Ahmed, Marzia Mohd Herwan, Sulaiman Hassan, Md Maruf Rahaman, Md Atikur Mohammad, Amin T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Accurate energy consumption forecasting is critical for efficient power distribution management. This study presents a novel approach for optimal allocation forecasting of energy consumption in a power distribution company, utilizing the Least Squares Support Vector Machine (LSSVM) optimized by novel variants of the Barnacle Mating Optimizer (BMO) such as the new Gooseneck Barnacle Optimizer and Selective Opposition-based constrained BMO. The optimized LSSVM hyper-parameters, specifically the regularization parameter (γ) and the kernel parameter (σ2), were applied to test data to enhance accuracy guided by the Mean Absolute Prediction Error (MAPE), ensuring precise alignment of forecasted values with actual energy consumption data. The results indicate that the novel gooseneck barnacle base-optimized LSSVM provides a robust and reliable solution with accuracy 99.98% for daily energy consumption for allocation forecasting, making it a valuable tool for power distribution companies aiming to optimize their resource allocation and planning processes. Elsevier B.V. 2025 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/43804/1/Daily%20allocation%20of%20energy%20consumption%20forecasting.pdf Ahmed, Marzia and Mohd Herwan, Sulaiman and Hassan, Md Maruf and Rahaman, Md Atikur and Mohammad, Amin (2025) Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine. Results in Control and Optimization, 18 (100518). pp. 1-13. ISSN 2666-7207. (Published) https://doi.org/10.1016/j.rico.2025.100518 https://doi.org/10.1016/j.rico.2025.100518 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Ahmed, Marzia Mohd Herwan, Sulaiman Hassan, Md Maruf Rahaman, Md Atikur Mohammad, Amin Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine |
| title | Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine |
| title_full | Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine |
| title_fullStr | Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine |
| title_full_unstemmed | Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine |
| title_short | Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine |
| title_sort | daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/43804/ http://umpir.ump.edu.my/id/eprint/43804/ http://umpir.ump.edu.my/id/eprint/43804/ http://umpir.ump.edu.my/id/eprint/43804/1/Daily%20allocation%20of%20energy%20consumption%20forecasting.pdf |