Comparison of Several Simplistic High-Level Approaches for Estimating the Global Energy and Electricity Use of ICT Networks and Data Centers

Abstract: Currently the global energy and electricity use of ICT networks and data centers are estimated and predicted by several different top-down approaches. It has not been investigated which prediction approach best answers to the 5G, Artificial Intelligence and Internet of Things megatrends which are expected to emerge until 2030 and beyond. The analysis of the potential correlation between storage volume, communication volume and computations (instructions, operations, bits) is also lacking. The present research shows that several different activity metrics (AM) – e.g. data traffic, subscribers, capita, operations – have and can be been used. First the global baseline electricity evolution (TWh) for 2010, 2015 and 2020 for networks of fixed, mobile and data centers is set based on literature. Then the respective AM – e.g. data traffic associated with each network are identified. Then the following are proposed: Compound Aggregated Growth Rate (CAGR) for each AM, CAGR for TWh/AM and the resulting TWh values for 2025 and 2030. The results show that AMs based on data traffic are best suited for predicting future TWh usage of networks. Data traffic is a more robust (scientific) AM to be used for prediction than subscribers as the latter is a more variable and less definable concept. Nevertheless, subscriber based AM are more uncertain than data traffic AM as the subscriber is neither a welldefined unit, nor related to the network equipment which handle the data. Despite large non-chaotic uncertainties, data traffic is a better AM than subscribers for expressing the energy evolution of ICT Networks and Data Centers. Topdown/high-level models based on data traffic are sensitive to the amount of traffic however also to the development of future electricity intensity. For the first time the primary energy use of computing, resulting from total global instructions and energy per instruction, is estimated.

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