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...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Control and Optimization |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666720725000049 |
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| author | Marzia Ahmed Mohd Herwan Sulaiman Md. Maruf Hassan Md. Atikur Rahaman Mohammad Bin Amin |
| author_facet | Marzia Ahmed Mohd Herwan Sulaiman Md. Maruf Hassan Md. Atikur Rahaman Mohammad Bin Amin |
| author_sort | Marzia Ahmed |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-c14b30299ce048cc997b983a09ace28d |
| institution | Kabale University |
| issn | 2666-7207 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Control and Optimization |
| spelling | doaj-art-c14b30299ce048cc997b983a09ace28d2025-01-23T05:27:50ZengElsevierResults in Control and Optimization2666-72072025-03-0118100518Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machineMarzia Ahmed0Mohd Herwan Sulaiman1Md. Maruf Hassan2Md. Atikur Rahaman3Mohammad Bin Amin4Department of Software Engineering, Daffodil International University, Dhaka, 1341, Bangladesh; Corresponding author.Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, 26600, Pahang, MalaysiaDepartment of CSE, Southeast University, Dhaka, BangladeshSchool of Economics and Management, Jiujiang University, 551 Qianjin Donglu, Jiujiang, Jiangxi 332005, PR ChinaDoctoral School of Management and Business, Faculty of Economics and Business, University of Debrecen, Debrecen, HungaryAccurate 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.http://www.sciencedirect.com/science/article/pii/S2666720725000049Variants of barnacle optimizerGooseneck barnacle optimizerEnergy consumption forecastingPower distributionMachine learning |
| spellingShingle | Marzia Ahmed Mohd Herwan Sulaiman Md. Maruf Hassan Md. Atikur Rahaman Mohammad Bin Amin Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine Results in Control and Optimization Variants of barnacle optimizer Gooseneck barnacle optimizer Energy consumption forecasting Power distribution Machine learning |
| 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 | Variants of barnacle optimizer Gooseneck barnacle optimizer Energy consumption forecasting Power distribution Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666720725000049 |
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