Short-Term Power Load Forecasting based on Multi-Factor Similar Day Selection and Multi-Strategy Improved Whale Optimization Algorithm
Short-term power load is susceptible to the meteorological environment and has obvious cyclical changes. To further improve accuracy, a short-term power load forecasting method based on multi-factor similar day selection and multi-strategy improved whale optimization algorithm (MSI-WOA) is proposed....
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| Vydané v: | 2025 IEEE 3rd International Conference on Power Science and Technology (ICPST) s. 1860 - 1868 |
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| Jazyk: | English |
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16.05.2025
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| Abstract | Short-term power load is susceptible to the meteorological environment and has obvious cyclical changes. To further improve accuracy, a short-term power load forecasting method based on multi-factor similar day selection and multi-strategy improved whale optimization algorithm (MSI-WOA) is proposed. Firstly, a similar day selection method is established by considering meteorological characteristics and time-cycle factors. Secondly, 16-dimensional input features are constructed, which contain meteorological, time-cycle, and similar day load data. Then, chaotic inverse learning is used to obtain a better initial solution. Nonlinear convergence factors, dynamic inertia weights, adaptive variational perturbations, and simulated annealing strategy are introduced to enhance the global and local search capabilities of the traditional whale optimization algorithm. MSI-WOA is used to optimize the hyperparameters of gated recurrent unit (GRU), such as the number of neuron nodes of two layers, iteration number, and learning rate, to obtain the optimal forecasting model. Finally, after the verification of actual cases and comparative experimental analysis, the proposed method has a better prediction effect. |
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| AbstractList | Short-term power load is susceptible to the meteorological environment and has obvious cyclical changes. To further improve accuracy, a short-term power load forecasting method based on multi-factor similar day selection and multi-strategy improved whale optimization algorithm (MSI-WOA) is proposed. Firstly, a similar day selection method is established by considering meteorological characteristics and time-cycle factors. Secondly, 16-dimensional input features are constructed, which contain meteorological, time-cycle, and similar day load data. Then, chaotic inverse learning is used to obtain a better initial solution. Nonlinear convergence factors, dynamic inertia weights, adaptive variational perturbations, and simulated annealing strategy are introduced to enhance the global and local search capabilities of the traditional whale optimization algorithm. MSI-WOA is used to optimize the hyperparameters of gated recurrent unit (GRU), such as the number of neuron nodes of two layers, iteration number, and learning rate, to obtain the optimal forecasting model. Finally, after the verification of actual cases and comparative experimental analysis, the proposed method has a better prediction effect. |
| Author | Liu, Yutao Wu, Guihong Wang, Xuan |
| Author_xml | – sequence: 1 givenname: Yutao surname: Liu fullname: Liu, Yutao email: liuyt_sgcc@126.com organization: Power Dispatching Control Center,State Grid Shanghai Municipal Electric Power Company,Shanghai,China – sequence: 2 givenname: Guihong surname: Wu fullname: Wu, Guihong email: ghwuee@163.com organization: Power Dispatching Control Center,State Grid Shanghai Municipal Electric Power Company,Shanghai,China – sequence: 3 givenname: Xuan surname: Wang fullname: Wang, Xuan email: 15810256393@yeah.net organization: Power Dispatching Control Center,State Grid Shanghai Municipal Electric Power Company,Shanghai,China |
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| Snippet | Short-term power load is susceptible to the meteorological environment and has obvious cyclical changes. To further improve accuracy, a short-term power load... |
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| StartPage | 1860 |
| SubjectTerms | Accuracy Convergence Forecasting gated recurrent unit Heuristic algorithms Load forecasting Load modeling multi-strategy improved WOA Optimization Predictive models short-term load forecasting similar day Simulated annealing Whale optimization algorithms |
| Title | Short-Term Power Load Forecasting based on Multi-Factor Similar Day Selection and Multi-Strategy Improved Whale Optimization Algorithm |
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