Work

BWO–ICEEMDAN–iTransformer: A Short-Term Load Forecasting Model for Power Systems with Parameter Optimization

Year: 2025

Type: article

Abstract: Maintaining the equilibrium between electricity supply and demand remains a central concern in power systems. A demand response program can adjust the power load demand from the demand side to promote the balance of supply and demand. Load forecasting can facilitate the implementation of this program. However, as electricity consumption patterns become more diverse, the resulting load data grows increasingly irregular, making precise forecasting more difficult. Therefore, this paper developed a specialized forecasting scheme. First, the parameters of improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) were optimized using beluga whale optimization (BWO). Then, the nonlinear power load data were decomposed into multiple subsequences using ICEEMDAN. Finally, each subsequence was independently predicted using the iTransformer model, and the overall forecast was derived by integrating these individual predictions. Data from Singapore was selected for validation. The results showed that the BWO–ICEEMDAN–iTransformer model outperformed the other comparison models, with an R2 of 0.9873, RMSE of 48.0014, and MAE of 66.2221.

Source: Algorithms

DOI: 10.3390/a18050243

Authors: Danqi Zheng, Jiyun Qin, Zhen Liu, Qinglei Zhang, Jianguo Duan, Ying Zhou

Institution: Shanghai Maritime University

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,930