Artificial Intelligence-Based Ultra-Short-Term Power Load Forecasting

Ruize Tian

doi:10.54254/2755-2721/2025.GL24381

Applied and Computational EngineeringOpen access

Artificial Intelligence-Based Ultra-Short-Term Power Load Forecasting

Research Article

Open Access

Artificial Intelligence-Based Ultra-Short-Term Power Load Forecasting

Ruize Tian ^1*

¹ School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China

^*Corresponding author: 3452461994@my.swjtu.edu.cn

Published on 27 June 2025

ACE Vol.172

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-221-8

ISBN (Online): 978-1-80590-222-5

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Abstract

In the context of significant renewable energy integration, power load forecasting is viewed as an essential task in energy management and power system operation and scheduling. In an effort to enhance the accuracy and precision of power load prediction, a predictive technique based on Long Short-Term Memory (LSTM) networks enhanced by the quantum-behaved particle swarm optimization (QPSO) is applied to ultra-short-term power load prediction in this paper. Initially, normalization is used to preprocess power load data before it is divided into training and testing datasets. Subsequently, global optimization of the LSTM’s essential hyperparameters and network architecture is conducted via QPSO, resulting in the development of a QPSO-LSTM forecasting model. Subsequently, the forecasting model is evaluated by employing the coefficient of determination (R²), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) as performance metrics. Finally, comparative experiments are conducted between the proposed model and traditional neural network models. The findings demonstrate that the QPSO-LSTM model offers enhanced forecasting precision and optimal fitting performance.

Keywords:

Short-term power load forecasting, Long Short-Term Memory neural network, Quantum-behaved Particle Swarm Optimization algorithm

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