Research on resource load forecasting of smart grid cloud computing center-based multi-model fusion

With the ongoing advancement of the State Grid information system, accurate forecasting of cloud data center resource loads becomes pivotal in enhancing the efficiency of cloud computing resource utilization, minimizing power consumption, and optimizing operational costs associated with human resources. Addressing the challenges of low prediction accuracy and the intricate time series characteristics of historical data, a novel approach, ABLCL, is proposed. This method leverages a multi-model fusion strategy incorporating ARIMA, BPNN, and LSTM models, integrating time series factors. Through comparative experiments on public datasets, the efficacy and stability of the proposed model in predicting cloud computing resource loads are validated, demonstrating superior performance over alternative forecasting models.