## 1 Introduction  
With the rapid development of information technology, Internet Data Centers (IDCs) have become critical infrastructure underpinning the digital transformation of modern society. Cloud computing, big data analytics, and artificial intelligence applications all heavily depend on the computational and storage capabilities provided by IDCs. However, the issue of high energy consumption in IDC equipment has become increasingly prominent. Statistics indicate that global data centers consumed approximately 1-2% of total global electricity in 2018 [1, 2], a proportion that continues to grow. Against the backdrop of global carbon neutrality goals, the IDC industry--recognizedas an energy-intensive sector--has faced increasing scrutiny for its energy-saving and emission-reduction challenges. Optimizing resource scheduling and energy management in IDC equipment through intelligent approaches has become a pressing challenge, particularly in terms of reducing operational costs and carbon emissions. However, key metrics of IDC equipment--such as energy consumption, device load, and environmental parameters--often exhibit complex, nonlinear, and time-varying characteristics [3], which pose significant challenges to data center operation and maintenance management. Therefore, data-driven prediction technologies have emerged as a major solution for achieving more precise resource scheduling and energy optimization. [PERSON] et al. [4] proposed a machine learning-based approach to optimize data center energy consumption, implementing a low-energy scheduling strategy, whereas [PERSON] et al. [5] applied long short-term memory (LSTM) neural networks to predict energy consumption in IDC air conditioning chillers. [PERSON] et al. [3] employed gated recurrent unit (GRU) networks to build a forecasting model for data center energy use. [PERSON] et al. [6] introduced a model predictive control (MPC) framework for uninterruptible power supply (UPS) units in IDC power systems, improving both UPS utilization and IDC profitability through comprehensive load analysis. [PERSON] et al. [7] proposed a mixed-integer programming (MIP)-based solution for IDC energy management by collaboratively optimizing dynamic voltage and frequency scaling (DVFS) and data center service chaining (DCSC), thereby achieving reduced power consumption and cost savings in conjunction with electricity market prices. Although the aforementioned studies demonstrate certain predictive and cost-reduction effects, the adopted model algorithms are relatively simplistic, enabling only single functionalities. This leads to insufficient judgment capabilities for abnormal conditions in IDC equipment, making it difficult to promptly detect anomalous energy consumption caused by equipment failures within the algorithm. [PERSON] et al. [8] applied an improved Levenberg-Marquardt (LM)-algorithm-based backpropagation (BP) neural network for reactive power prediction in power grids, achieving accurate high-resistance single-phase open-circuit fault identification. [PERSON] et al. [9] established a backpropagation (BP) neural network-based lifespan prediction model for glass fiber-reinforced plastics, providing relatively accurate predictions for experimental data. [PERSON] et al. [10] conducted research on identifying formation lithology based on BP neural networks, applied to the discrimination of formation lithology in logging data. Their method achieved a 20% improvement in accuracy compared to traditional cross-plot methods. To address the limitations identified in previous studies, this work proposes a bidirectional cross-attention LSTM-Informer with uncertainty-aware multi-task learning framework (BiCA-LI), aiming to jointly perform time series forecasting and anomaly detection for IDC equipment parameters. First, a dual-path parallel encoder integrating LSTM and Informer networks is introduced to separately capture short-term dynamics and long-term dependencies from time series data.