Frequency-Enhanced Dual-Stream Parallel Network for Multienergy Load Forecasting

Accurate and efficient multienergy load forecasting is fundamental for the secure and economic operation of integrated energy systems (IESs). However, current methods face several critical challenges as follows. 1) Insufficient perception of low-energy transient features leads to significant deviations in marginal scenario predictions. 2) Existing architectures struggle to simultaneously capture long-term trends and short-term dynamics efficiently. 3) Many high-accuracy models suffer from high computational complexity, limiting their practical deployment. To overcome these limitations, we propose the frequency-enhanced seasonal-trend recurrent neural network (FESTRNN), which incorporates two newly designed modules: the energy-enhanced spectral module to amplify critical frequency signals, and the dual-stream collaborative temporal forecasting module to efficiently model multiscale temporal dependencies. Experimental results on the ASU dataset demonstrate that FESTRNN achieves state-of-the-art accuracy across various forecasting horizons with significantly lower computational costs. This study provides a reliable basis for real-time scheduling in IES, offering strong practical value.