Colocation Datacenter Customer Power Usage Forecasting Using Synthetic Data and Integration of Macroeconomic Indicators

TL;DR: This work proposes an innovative power forecasting system that takes into account both internal and external demand signals, leading to more accurate and realistic predictions, and synthesized a comprehensive dataset tailored to the data center environment using the small dataset provided by S&P Global.

Abstract: Colocation data centers play a pivotal role in the digital era, serving as the backbone for a diverse range of businesses, from startups to large enterprises. The global data center construction market, which reached an approximate value of US ${\$}$218.88 billion in 2021, is experiencing unprecedented growth. This surge can be attributed to the ever-increasing volume of data, propelled by economic advancements and population expansion. Consequently, long-term private equity firms and real estate investment trusts (REITs) are increasingly drawn to data center investments for their attributes of transparency and accountability. However, data centers face challenges in terms of project lead times and budget constraints, particularly during expansion phases. In response to these challenges, we propose an innovative power forecasting system that takes into account both internal and external demand signals, leading to more accurate and realistic predictions. These predictions serve as valuable guides for resource allocation and utilization. The system lever- ages a combination of data generation, deep learning (DL), and time-series analysis methods, and later, we use it in our future work to proactively address issues such as supply shortages, ensure the maintenance of Service Level Agreements (SLAs), and optimize resource usage. We synthesized a comprehensive dataset tailored to the data center environment using the small dataset provided by S&P Global. This dataset was enriched with macroeconomic data to capture external influences accurately. Subsequently, we conducted a rigorous evaluation, testing various machine learning models, including linear models, transformer-based models, and a multivariate LSTM model. Our experiments revealed that the PatchTST model outperformed the others, providing the most reliable and precise results. The implementation of advanced analytics further enhances energy efficiency, optimizes equipment utilization, and maximizes the effective utilization of floor space within data centers. Furthermore, efficient resource allocation, guided by the power forecasting system, ensures that customer demands are met promptly and effectively.