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  4. Vol 9, Issue 3, 2025

Power Load Forecasting Method Combining Informer Model and ACO Optimization Algorithm

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DOI: 10.23977/acss.2025.090317 | Downloads: 2 | Views: 78

Author(s)

Di Shi 1

Affiliation(s)

1 School of Computer Science and Artificial Intelligence, Beijing Technology and Business University, Beijing, China

Corresponding Author

Di Shi

ABSTRACT

This study proposes a power load forecasting method that combines the Informer model with the Ant Colony Optimization (ACO) algorithm. ACO optimizes the hyperparameters of the Informer model, significantly improving the model's accuracy and stability in power load forecasting. The Informer model utilizes its ProbSparse Attention technology to efficiently process long-term time series data and capture long-term dependencies in power load variations. ACO optimizes hyperparameter combinations through global search, avoiding the limitations of manual parameter tuning. Experimental results demonstrate that the proposed model outperforms traditional LSTM and TCN models across multiple evaluation metrics, demonstrating greater stability and prediction accuracy, particularly during peak load periods. This method provides effective technical support for smart grid scheduling and resource optimization.

KEYWORDS

Investor Sentiment, Futures Pricing Efficiency, Dual Machine Learning, Generalized Random Forest, Causal Inference

CITE THIS PAPER

Di Shi, Power Load Forecasting Method Combining Informer Model and ACO Optimization Algorithm. Advances in Computer, Signals and Systems (2025) Vol. 9: 140-149. DOI: http://dx.doi.org/10.23977/acss.2025.090317.

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