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

• Published in: Energies (Basel) Vol. 17; no. 15; p. 3676
• Main Authors: Li, Dengao, Liu, Qi, Feng, Ding, Chen, Zhichao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-08-2024
• Subjects: Accuracy; Analysis; attention mechanism; causal convolution; Coding standards; Computational linguistics; convolutional neural network; Decomposition; discrete cosine transform; Electric power systems; Electricity; FEDformer model; Forecasting; Fourier transforms; Language processing; load forecasting; Machine learning; Methods; Natural language interfaces; Neighborhoods; Neural networks; Time series; Wavelet transforms
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17153676

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.