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

Yuanqi Yu; Lin Qiao; Liangliang Yu; Qun Wang; Fei Xia; Hai Yu

doi:10.1117/12.3038309

7 August 2024 Research on resource load forecasting of smart grid cloud computing center-based multi-model fusion

Yuanqi Yu, Lin Qiao, Liangliang Yu, Qun Wang, Fei Xia, Hai Yu

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 1322919 (2024) https://doi.org/10.1117/12.3038309
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuanqi Yu, Lin Qiao, Liangliang Yu, Qun Wang, Fei Xia, and Hai Yu "Research on resource load forecasting of smart grid cloud computing center-based multi-model fusion", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 1322919 (7 August 2024); https://doi.org/10.1117/12.3038309

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