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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Vydáno v:2025 IEEE 3rd International Conference on Power Science and Technology (ICPST) s. 1860 - 1868
Hlavní autoři: Liu, Yutao, Wu, Guihong, Wang, Xuan
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 16.05.2025
Témata:
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
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Shrnutí: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.
DOI:10.1109/ICPST65050.2025.11089039