A Novel Electrical Load Forecasting Model for Extreme Weather Events Based on Improved Gated Spiking Neural P Systems and Frequency Enhanced Channel Attention Mechanism

Abstract

Accurate short-term load forecasting (LF) under extreme weather is vital for the sustainable development of energy systems. This paper proposes a basic framework for future load forecasting researches of sustainable energy systems under extreme weather events and provides new direction for membrane computing model in terms of load forecasting. Inspired by nonlinear spiking mechanisms in nonlinear spiking neural P systems, the gated spiking neural P (GSNP) model is a new recurrent-like network. In this study, we develop an innovative membrane computing model, termed frequency attention temporal convolutional network-load forecasting-frequency attention gated spiking neural P (FATCN-LF-FAGSNP) model. Frequency enhanced channel attention mechanism (FECAM) is utilized to enhance the features extraction ability of temporal convolutional network (TCN) and improve prediction ability of GSNP systems. FATCN fully extracts the temporal relationship of features, the features of each channel interact with each frequency component to learn more temporal information effectively and comprehensively in frequency domain. Moreover, adding FECAM to extract features from the data fully reveals the relationship between influencing factors and the load series, which improves the quality of data features and the forecasting accuracy of the FAGSNP model. Then inspired by the interaction mechanism of impulses between biological neuronal cells, FAGSNP is able to consider the load variability and effectively predict load trends. In addition, to address load prediction challenges posed by extreme weather and promote the sustainable development of power systems, the proposed model integrates many models to solve this problem. First, optimized variational mode decomposition (VMD) is used to decompose the load series and the sub-sequences are combined with relevant features, to form the different input sequences of the prediction model. Then, FATCN-LF-FAGSNP model is developed to accurately forecast each high frequency component. Subsequently inverted Transformer model and Informer model are utilized to predict low frequency components and residual component, respectively. Finally all predicted components are reconstructed to get the final predicted results. We conducted extensive comparative experiments with ten baseline models on three real-world datasets, compared with GSNP model and TCN-GSNP model, the coefficient of determination (R2) of the FATCN-LF-FAGSNP model increases and mean absolute percentage error (MAPE), mean absolute error (MAE) and relative absolute error (RAE) reduce, the LF accuracy (measured by R2) of the proposed hybrid model gets 99.7% in seasonal LF task. In addition, the proposed hybrid model gets the best in MAPE, MAE, R2 and RAE metrics in all cases, which demonstrates the effectiveness of the proposed model in LF tasks under both extreme weather scenarios and seasonal prediction scenarios.

Keywords

• Short-term load forecasting
• extreme weather events
• gated spiking neural P
• frequency enhanced channel attention mechanism
• temporal convolutional network
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