5G Power Private Network Slice Resource Forecasting Based on BiLSTM-Attention With SI-MACP

5G private power networks require high levels of real-time performance and reliability; however, traditional threshold-based scaling methods struggle with the unpredictable bursts and periodic fluctuations in traffic. This paper proposes a BiLSTM-Attention model that incorporates Swarm Intelligence-based Multi-Agent Collaborative Prediction (SI-MACP), named BiLSTM-Attention-SI-MACP, for resource forecasting in these networks. The proposed framework combines bidirectional LSTM networks with attention mechanisms to capture multivariate resource dependencies. Meanwhile, the SI-MACP mechanism utilizes principles of swarm intelligence through distributed task decomposition, privacy-preserving local modeling, and the collaborative aggregation of parameters and predictions. Experimental results demonstrate significant improvements, including 32% MAE reduction, 35% higher resource utilization, and 99.9% SLA compliance, particularly benefiting latency-sensitive services like differential protection.