## 1 Introduction  
As the demands for artificial intelligence (AI) services are tremendously increasing, interest in high-performance computing clusters is also growing [1]. Various organizations and institutes have recently deployed HPC clusters, which include hundreds or thousands of computing nodes, for AI service processing. The core computing device in these clusters is the graphics processing unit (GPU), capable of accelerating computation speed for data-rich and parallel task processing by exploiting thousands of small multi-cores (e.g., CUDA cores in NVIDIA [2] and Stream Processors in AMD [3]). Recently, NVIDIA launched GPU-based AI hardware platforms, such as DGX-A100, DGX-H100, and DGX-GH200. These have been deployed as AI development solutions in cloud data centers by major vendors including Microsoft, Google, and Amazon [4]. Along with the high computational capacity of GPU-based clusters, a new challenge has emerged: the issue of energy consumption [5,6]. Despite ongoing improvements in the performance-per-watt of GPU devices, the energy consumption costs incurred by modern GPU clusters remain a significant obstacle to the expansion of next-generation AI technology across various fields. For example, a single NVIDIA H100 rack, consisting of eight Hopper-based SXM cards, may require a total thermal design power (TDP) of 5600 W (700 \(\times\) 8 W) for full workload. In this scenario, the rack could lead to energy consumption of up to 130 kWh per day, corresponding to an electricity usage cost of more than an average of USD 15.6 in Nevada, USA, as of 2023. The exploitation of renewable energy generation has recently emerged as an attractive solution for reducing energy consumption costs in commercial clusters and data centers [7; 8]. Photovoltaic and wind energy are the most popular options in both research and industrial fields, due to their abundant availability as natural resources and the significant improvements in the efficiency of solar panels and wind turbines. The main challenge in efficiently utilizing renewable energies lies in accurately predicting future renewable energy generation, even amidst uncertainty and irregularity. To address this, there has been a surge in studies aiming to adopt AI-based methods for renewable energy prediction. [PERSON] et al. [9] propose an Echo State Network-Convolutional Neural Network (CNN) model for accurate renewable energy prediction. They linearly connect the ESN to CNN layers using residual connections, efficiently avoiding the vanishing gradient problem and reducing error rates in predictions. [PERSON] et al. [10] present a combination of deep learning models including Extreme Learning Machines, CNN, and Bidirectional Long Short-Term Memory to accurately forecast the different frequency series found in wind energy data. They apply a secondary decomposition method to enhance the prediction accuracy of low-frequency components. [PERSON] et al. [11] introduce a new forecasting method for power generation curves, based on both Graph Convolutional Network (GCN) and Long Short-Term Memory models (LSTM). They employ GCN to capture spatial correlations among multiple renewable energy sources and use LSTM to track dynamic behaviors in power generation. Although state-of-the-art studies on renewable energy, including those mentioned above, yield impressive results, it is crucial to note that they concentrate solely on AI-based methods for predicting renewable energy generation. They do not investigate the use of renewable energy as a means to reduce energy costs associated with processing deep learning jobs in GPU-based clusters. Note that research investigating power and energy consumption behaviors in modern clusters due to deep learning (DL) job processing is in its early stages. [PERSON] et al. [12] propose an energy-aware DL job scheduler to reduce the energy consumption of CNN inference services on GPU devices.