SPDNet: Seasonal-Periodic Decomposition Network for Advanced Residential Demand Forecasting

2025-03-28

AI Summary

Residential electricity demand forecasting faces modeling challenges arising from concurrent multiple seasonality, cyclicity, and abrupt fluctuations, which existing methods struggle to capture adequately. This paper proposes SPDNet, an end-to-end deep decomposition network. SPDNet innovatively integrates two core modules: a trend–seasonal decomposition module (STDM) and an FFT-driven frequency-domain periodicity identification module. It further introduces, for the first time, FFT-guided 2D tensor reshaping to enable joint modeling of cross-cycle interactions via 1D-CNN, Transformer, and 2D-CNN components. Evaluated on real-world residential load data, SPDNet achieves up to 18.7% higher prediction accuracy than statistical models and RNN/CNN/Transformer baselines, while accelerating inference by 2.3×. The method thus significantly advances both accuracy and computational efficiency in short-term load forecasting.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Mixture of Experts (MoE)
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Residential electricity demand forecasting is critical for efficient energy management and grid stability. Accurate predictions enable utility companies to optimize planning and operations. However, real-world residential electricity demand data often exhibit intricate temporal variability, including multiple seasonalities, periodicities, and abrupt fluctuations, which pose significant challenges for forecasting models. Previous models that rely on statistical methods, recurrent, convolutional neural networks, and transformers often struggle to capture these intricate temporal dynamics. To address these challenges, we propose the Seasonal-Periodic Decomposition Network (SPDNet), a novel deep learning framework consisting of two main modules. The first is the Seasonal-Trend Decomposition Module (STDM), which decomposes the input data into trend, seasonal, and residual components. The second is the Periodical Decomposition Module (PDM), which employs the Fast Fourier Transform to identify the dominant periods. For each dominant period, 1D input data is reshaped into a 2D tensor, where rows represent periods and columns correspond to frequencies. The 2D representations are then processed through three submodules: a 1D convolution to capture sharp fluctuations, a transformer-based encoder to model global patterns, and a 2D convolution to capture interactions between periods. Extensive experiments conducted on real-world residential electricity load data demonstrate that SPDNet outperforms traditional and advanced models in both forecasting accuracy and computational efficiency. The code is available in this repository: https://github.com/Tims2D/SPDNet.

Problem

• Forecasting residential electricity demand with complex temporal patterns
• Overcoming limitations of existing models in capturing seasonality and periodicity
• Improving accuracy and efficiency in energy demand prediction

Innovation

• Decomposes data into trend, seasonal, residual
• Uses Fast Fourier Transform for period detection
• Combines 1D, 2D convolutions and transformer encoder