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

• URL: http://arxiv.org/abs/2503.22485v1
• Date: Fri, 28 Mar 2025 14:51:38 GMT
• Title: SPDNet: Seasonal-Periodic Decomposition Network for Advanced Residential Demand Forecasting
• Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan,
• Abstract summary: Seasonal-Periodic Decomposition Network (SPDNet) is a novel deep learning framework consisting of two main modules.<n>SPDNet outperforms traditional and advanced models in both forecasting accuracy and computational efficiency.
• Score: 0.6554326244334868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Times2D: Multi-Period Decomposition and Derivative Mapping for General Time Series Forecasting [0.6554326244334868]
  Time series forecasting is an important application in various domains such as energy management, traffic planning, financial markets, meteorology, and medicine. Previous models that rely on 1D time series representations usually struggle with complex temporal variations. This study introduces the Times2D method that transforms the 1D time series into 2D space.
  arXiv  Detail & Related papers  (2025-03-31T18:08:30Z)
• Timer-XL: Long-Context Transformers for Unified Time Series Forecasting [67.83502953961505]
  We present Timer-XL, a causal Transformer for unified time series forecasting.<n>Based on large-scale pre-training, Timer-XL achieves state-of-the-art zero-shot performance.
  arXiv  Detail & Related papers  (2024-10-07T07:27:39Z)
• sTransformer: A Modular Approach for Extracting Inter-Sequential and Temporal Information for Time-Series Forecasting [6.434378359932152]
  We review and categorize existing Transformer-based models into two main types: (1) modifications to the model structure and (2) modifications to the input data. We propose $textbfsTransformer$, which introduces the Sequence and Temporal Convolutional Network (STCN) to fully capture both sequential and temporal information. We compare our model with linear models and existing forecasting models on long-term time-series forecasting, achieving new state-of-the-art results.
  arXiv  Detail & Related papers  (2024-08-19T06:23:41Z)
• FAITH: Frequency-domain Attention In Two Horizons for Time Series Forecasting [13.253624747448935]
  Time Series Forecasting plays a crucial role in various fields such as industrial equipment maintenance, meteorology, energy consumption, traffic flow and financial investment. Current deep learning-based predictive models often exhibit a significant deviation between their forecasting outcomes and the ground truth. We propose a novel model Frequency-domain Attention In Two Horizons, which decomposes time series into trend and seasonal components.
  arXiv  Detail & Related papers  (2024-05-22T02:37:02Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting [50.23240107430597]
  We design a special Transformer, i.e., Channel Aligned Robust Blend Transformer (CARD for short), that addresses key shortcomings of CI type Transformer in time series forecasting. First, CARD introduces a channel-aligned attention structure that allows it to capture both temporal correlations among signals. Second, in order to efficiently utilize the multi-scale knowledge, we design a token blend module to generate tokens with different resolutions. Third, we introduce a robust loss function for time series forecasting to alleviate the potential overfitting issue.
  arXiv  Detail & Related papers  (2023-05-20T05:16:31Z)
• DuETT: Dual Event Time Transformer for Electronic Health Records [14.520791492631114]
  We introduce the DuETT architecture, an extension of Transformers designed to attend over both time and event type dimensions. DuETT uses an aggregated input where sparse time series are transformed into a regular sequence with fixed length. Our model outperforms state-of-the-art deep learning models on multiple downstream tasks from the MIMIC-IV and PhysioNet-2012 EHR datasets.
  arXiv  Detail & Related papers  (2023-04-25T17:47:48Z)
• FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification [53.55504611255664]
  FormerTime is a hierarchical representation model for improving the classification capacity for the multivariate time series classification task. It exhibits three aspects of merits: (1) learning hierarchical multi-scale representations from time series data, (2) inheriting the strength of both transformers and convolutional networks, and (3) tacking the efficiency challenges incurred by the self-attention mechanism.
  arXiv  Detail & Related papers  (2023-02-20T07:46:14Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis [80.56913334060404]
  Time series analysis is of immense importance in applications, such as weather forecasting, anomaly detection, and action recognition. Previous methods attempt to accomplish this directly from the 1D time series. We ravel out the complex temporal variations into the multiple intraperiod- and interperiod-variations.
  arXiv  Detail & Related papers  (2022-10-05T12:19:51Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)
• Pattern-based Long Short-term Memory for Mid-term Electrical Load Forecasting [0.0]
  This work presents a network for forecasting a monthly electricity demand time series with a one-year horizon. The novelty of this work is the use of pattern representation of the seasonal time series as an alternative to decomposition. A simulation study performed on the monthly electricity demand time series for 35 European countries confirmed the high performance of the proposed model.
  arXiv  Detail & Related papers  (2020-04-22T08:39:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.