A multi-component hybrid model for renewable energy integrated grid power load forecasting: DWT, SAE, and FA-LSTM.

As renewable penetration deepens and grid operating conditions grow more intricate, load data exhibit significant non-stationarity and multi-scale characteristics, leading to certain limitations in modeling and forecasting accuracy of traditional methods. To tackle this issue, this research suggests a power load forecasting (PLF) approach that utilizes the discrete wavelet transform (DWT), stacked autoencoder (SAE), and long short-term memory network (LSTM) optimized by firefly algorithm (FA). Firstly, DWT is used to perform multi-scale decomposition on the raw power load series, separating the signal into a high-frequency residual and a low-frequency trend. Then, SAE is applied to denoise the decomposed high-frequency components, effectively filtering out residual noise and improving data quality. Finally, FA is employed to optimize the key parameters of LSTM, achieving accurate modeling of features in different frequency bands. Empirical results indicate that the suggested approach reaches mean absolute percentage errors (MAPE) of 2.540%, 12.90%, and 26.110% in forecasting for Fujian, Xinjiang, and Gansu regions, respectively, while the best MAPE of other comparison models is 4.370%, 43.270%, and 37.330%. This significantly improves the accuracy and robustness of PLF in different regions, providing a new idea and method for power data forecasting. Code is available at https://github.com/Siri-scut/DWT-SAE-FA-LSTM.git.