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.

翻译：住宅用电需求预测对于高效能源管理和电网稳定至关重要。准确的预测使电力公司能够优化规划和运营。然而，现实世界的住宅用电需求数据通常表现出复杂的时间变异性，包括多重季节性、周期性和突发性波动，这对预测模型构成了重大挑战。以往依赖统计方法、循环神经网络、卷积神经网络和Transformer的模型往往难以捕捉这些复杂的时间动态。为应对这些挑战，我们提出了季节-周期分解网络（SPDNet），这是一种新颖的深度学习框架，由两个主要模块组成。第一个是季节-趋势分解模块（STDM），它将输入数据分解为趋势、季节和残差分量。第二个是周期分解模块（PDM），它采用快速傅里叶变换来识别主导周期。对于每个主导周期，一维输入数据被重塑为二维张量，其中行代表周期，列对应频率。然后，二维表示通过三个子模块进行处理：一维卷积用于捕捉急剧波动，基于Transformer的编码器用于建模全局模式，以及二维卷积用于捕捉周期之间的相互作用。在真实住宅电力负荷数据上进行的大量实验表明，SPDNet在预测精度和计算效率方面均优于传统和先进模型。代码可在以下仓库获取：https://github.com/Tims2D/SPDNet。