Power Load Forecasting of Bi-LSTM Based on Improving Whale Algorithm

doi:10.3969/j.issn.1006-2475.2025.07.011

Abstract

Abstract:
Abstract: The accuracy of the power load prediction is the key to ensure the stable operation of power system after new energy grid connection. A power load forecasting model based on Bi-LSTM neural network and attention mechanism is proposed. Aiming at the problem that it is difficult to select the optimal hyperparameters in neural network， the whale algorithm is used to optimize the hyperparameters. To solve the problem of uneven distribution of global search and local development of whale optimization algorithm， a method of adjusting the piecewise nonlinear convergence factor is proposed. To solve the insufficient late-stage development ability， a method of combining adaptive weights and random difference variation is proposed for the hyperparameters optimization. Simulation verifies the effectiveness of the proposed method in hyperparameter design， and verifies the accuracy and effectiveness of power load forecasting based on improved whale algorithm.

Key words: Key words: power load forecasting, bidirectional long-term and short-term memory neural network, hyperparameters, whale optimization algorithm, attention mechanism

CLC Number:

中图分类号：TM715

RAO Hongyu1, CHEN Xin2, CHEN Sheng3, XIA Tian3, SHEN Li4, LUY Guangqiang5. Power Load Forecasting of Bi-LSTM Based on Improving Whale Algorithm[J]. Computer and Modernization, 2025, 0(07): 77-82.

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