In order to solve the problem of increasing power load volatility and complexity during the period of constructing the new power system

which makes it difficult to forecast accurately

a fusion load forecasting model based on load secondary decomposition and feature processing is proposed. First

empirical wavelet transform (EWT) is used to perform an initial decomposition of the power load series. Sample entropy (SE) and Singular Spectrum Analysis (SSA) are then applied for secondary decomposition of high-complexity sub-series

reducing the overall complexity of the load data. For feature processing

distance correlation is employed to calculate the correlation between each sub-series and the features

and to assess redundancy between features

extracting the optimal feature set. Additionally

for temperature features

a fuzzy processing method that considers temperature accumulation effects is proposed to enhance the model's sensitivity to temperature changes. Finally

the decomposed load components and optimized feature sets are input into the crested porcupine optimizer (CPO) bidirectional temporal convolutional network-bidirectional gated recurrent unit (BiTCN-BiGRU) for prediction. Using the actual data of a local power grid for example analysis

the results show that compared with the mainstream deep learning forecasting model

feature processing method

and load decomposition method

the proposed fusion method reduces the root mean square error by up to 87.79%

32.23% and 24.22%

respectively

which indicates that the proposed method has a high load forecasting accuracy.