Interpretable and Accurate Multi-Energy Loads Forecasting via Continuous-Time Modeling

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Abstract
High-precision interpretable forecasting of multi-energy loads is crucial for planning and optimizing Integrated Energy Systems (IES). Current methods have three key limitations: 1) reliance on closed-box models obscures feature interactions; 2) traditional modal decomposition methods ignore cross-modal correlations and fail to efficiently extract global cycle-trend characteristics; 3) limitations in sampling frequency prevent capturing fast dynamic characteristics of energy loads. To address these, we propose a dual resolution channel multi-period cross reconstruction parallel closed-form continuous-time network (DMP-PCFC) for multi-energy load forecasting. This method includes a multi-period cross-reconstruction network which equivalent to the signal sifter for decoupling original sequences and nonlinear mapping across periods, and models hidden states of energy loads using neuron state iterative differential equations. A parallel Closed-form continuous-time neural network (PCFC) based on chunked parallelization reduces inference time from $O(T)$ (change with input sequence length) to $O(P)$ (fixed cycle length). To overcome inherent sampling resolution limitations, a dual-resolution channel model with weight-sharing PCFC is used for simultaneous inference. Extensive experiments and continuous wavelet transform (CWT) on the IES dataset at Arizona State University’s Tempe campus show that DMP-PCFC offers higher prediction accuracy and greater interpretability than existing methods, providing a new paradigm for downstream tasks based on other research methods, making it valuable for IES engineering applications. The code is publicly available at https://github.com/nuist-xf/DMP-PCFC
Funding Information
  • State Grid Corporation of China Project (5108-202335449A-3-2-ZN)

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