Robust Load Prediction of Power Network Clusters Based on Cloud-Model-Improved Transformer
This work addresses load prediction for power enterprises to guide decisions, but it is incremental as it combines existing methods for a specific domain.
The paper tackled the problem of predicting power load in network clusters by integrating the cloud model with Transformer to handle data uncertainties, achieving significantly higher accuracy than the Transformer alone on 31 real datasets.
Load data from power network clusters indicates economic development in each area, crucial for predicting regional trends and guiding power enterprise decisions. The Transformer model, a leading method for load prediction, faces challenges modeling historical data due to variables like weather, events, festivals, and data volatility. To tackle this, the cloud model's fuzzy feature is utilized to manage uncertainties effectively. Presenting an innovative approach, the Cloud Model Improved Transformer (CMIT) method integrates the Transformer model with the cloud model utilizing the particle swarm optimization algorithm, with the aim of achieving robust and precise power load predictions. Through comparative experiments conducted on 31 real datasets within a power network cluster, it is demonstrated that CMIT significantly surpasses the Transformer model in terms of prediction accuracy, thereby highlighting its effectiveness in enhancing forecasting capabilities within the power network cluster sector.