Research on renewable energy power demand forecasting method based on IWOA-SA-BILSTM modeling

Abstract

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This paper introduces a novel coupling method to enhance the precision of short- and medium-term renewable energy power load demand forecasting. Firstly, the Tent chaotic mapping incorporates the standard WOA and modifies its internal convergence factor to a nonlinear convergence mode, resulting in an improved IWOA. It is used for the weight optimization part of BILSTM. Then, the SA is introduced to optimize the learning rate, the number of nodes in hidden layers 1 and 2, and the number of iterations of BILSTM, constructing an IWOA-SA-BILSTM prediction model. Finally, through case analysis, the prediction model proposed in this paper has the highest improvement of 76.7%, 74.5%, and 45.9% in terms of Mean Absolute Error, Root Mean Square Error, and R2, respectively, compared to other optimal benchmark models, proving the effectiveness of the model.

Keywords

• bi-directional long and short-term memory neural networks
• whale optimization algorithm
• multidimensional feature analysis
• simulated annealing algorithm
• load demand forecasting