A Medium- and Long-Term Residential Load Forecasting Method Based on Discrete Cosine Transform-FEDformer

Dengao Li (), Qi Liu, Ding Feng and Zhichao Chen
Additional contact information
Dengao Li: Shanxi Energy Internet Research Institute, Taiyuan 030000, China
Qi Liu: Shanxi Energy Internet Research Institute, Taiyuan 030000, China
Ding Feng: Shanxi Energy Internet Research Institute, Taiyuan 030000, China
Zhichao Chen: Shanxi Energy Internet Research Institute, Taiyuan 030000, China

Energies, 2024, vol. 17, issue 15, 1-14

Abstract: Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading to poor predictive accuracy. Therefore, this paper proposes a medium- and long-term residential load forecasting method based on FEDformer, aiming to capture long-term temporal dependencies of load data in the frequency domain while considering factors such as electricity prices and temperature, ultimately improving the accuracy of medium- and long-term load forecasting. The proposed model employs Discrete Cosine Transform (DCT) for frequency domain transformation of time-series data to address the Gibbs phenomenon caused by the use of Discrete Fourier Transform (DFT) in FEDformer. Additionally, causal convolution and attention mechanisms are applied in the frequency domain to enhance the model’s capability to capture long-term dependencies. The model is evaluated using real-world load data from power systems, and experimental results demonstrate that the proposed model effectively learns the temporal and nonlinear characteristics of load data. Compared to other baseline models, DCTformer improves prediction accuracy by 37.5% in terms of MSE, 26.9% in terms of MAE, and 26.24% in terms of RMSE.

Keywords: attention mechanism; causal convolution; convolutional neural network; discrete cosine transform; FEDformer model; load forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/15/3676/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/15/3676/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:15:p:3676-:d:1442940

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().