.png){kind=icon}
.png){kind=icon}
Return
Improved composite model using metaheuristic optimization algorithm for short-term power load forecasting
.png){kind=icon}
PRE
AI
DOI：10.1016/j.epsr.2024.111330.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Accurate short-term electric load forecasting contributes to operational efficiency, grid stability, and profitability in power systems and energy markets. With increasing complexity in grid operations due to renewable integration and demand fluctuations, enhancing forecasting precision, particularly at 15-minute intervals, has become essential. In this study, we propose a composite model, TCN-Self-Attention-BILSTM (TSAB), designed to improve load prediction accuracy by integrating multiple advanced neural network architectures. Specifically, the Temporal Convolutional Network (TCN) captures long-term dependencies, while a self-attention mechanism dynamically emphasizes key features, and the Bidirectional Long Short-Term Memory Network (BILSTM) establishes robust temporal relationships. To optimize the hyperparameters of TSAB efficiently, we introduce the Enhanced Triangular Topology Aggregation Optimizer (ETTAO), a novel approach for rapid and effective tuning for composite models such as TSAB. Additionally, to evaluate model predication accuracy and hyperparameter optimization, we present a new objective function that combines multiple evaluation metrics based on their physical significance, balancing model performance across key aspects of accuracy. Experimental validation on two benchmark datasets demonstrates that TSAB outperforms conventional models, including LSTM, TCN, and CNN-BILSTM-Attention, in both feature extraction and predictive accuracy. Together, TSAB, ETTAO, and the new evaluation function offer a comprehensive approach to improving prediction accuracy, hyperparameter optimization tuning, and model evaluation, contributing to more effective and reliable short-term load forecasting in the electric power sector, with implications for enhanced operational decision-making in the electric power sector.
Keywords:
Short-term load forecasting
BILSTM
Self-attention
TCN
Optimization algorithm
TSAB
ETTAO
Journal
IF:
4.2
Papers:
1.1W
Citations:
2.2W