## 1 Introduction  
According to Cisco's Annual Internet Report analysis and forecasts, the number of connected devices is set to increase from 700 million in 2018 to 3.2 billion by 2023 [1]. Additionally, Cisco AIR has revealed that by 2023, there will be 5.3 billion total Internet users (66% of the world's population) compared to 3.9 billion in 2018. Its rapid growth is attributed mainly to the successful development and deployment of fifth generation (5G) wireless networks [2]. The densely deployed base station (BS) network is a promising architecture for 5G and future generations, as it can improve system capacity and throughput by deploying a large number of BSs per unit area. However, this architecture has to deal with increasing energy due to network expansion, and the fact that BSs usually account for about 57% of total mobile network demand [3, 4]. In order to reduce the energy consumed by BSs, various approaches have been developed, with particular emphasis on techniques that incorporate the concept of \"sleep mode\". It takes advantage of changing traffic patterns on hourly, daily, and/or weekly basis. For this, it is possible to selectively switches lightly loaded BSs to low energy consumption modes. This approach has the potential to save a significant amount of energy. Other approaches [5] manage energy demand by monitoring the traffic load in the network and then deciding whether certain elements of the network need to be turned off/on. The purpose of sleep mode techniques is to save energy by selectively turning off BSs during \"off-peak\" hours. Traffic prediction plays an important role in the design, management, and optimization modeling of a cellular network. Cellular traffic prediction can be used to monitor and control the energy consumption of the wireless network. Currently, as part of the overall greenhouse gas (GHG) mitigation plan, predicting fourth generation (4G) long-term evolution (LTE) and 5G traffic is of great interest to the telecommunications industry. Artificial intelligence (AI) models have been widely used to predict mobile traffic, such as developing a prediction model to manage cellular network traffic [6]. Past studies have shown that deep learning techniques are capable of modeling and predicting cellular network traffic with great performance [7; 8; 9; 10; 11; 12; 13]. The huge growth in the number of machine learning algorithms (MLAs) has led to high demand for energy, very often of fossil origin. The carbon footprint of the cellular network will likely increase, as the energy sector is currently responsible for around 70% of GHG emissions [14]. In addition, electricity demand will continue to increase due to digitization. Given the exponential growth in the deployment of MLA-based systems, a large portion of electricity production would be consumed by the telecommunications sector [15]. The optimization of MLAs with the aim of further reducing energy consumption is therefore crucial and deserves significant consideration. However, this factor is not often considered by MLA developers. Model precision and the capacity to generate new data are key selection criteria during the MLA development stage. The lack of a quantification methodology for MLAs energy consumption hinders the deployment of a long-term solution to reduce GHG emissions in the Information and Telecommunications (IT) sector. Until recently, there have been few peer-reviewed publications focused on quantifying energy consumed by MLAs in the telecommunications field. Furthermore, there are very few studies that aimed to estimate the amount of energy consumed by MLAs and their environmental impacts. However, over the past three years, we have seen more and more studies focusing on this topic. One of the first studies estimating the energy consumed by MLA was carried out by [PERSON] et al. [16]. They presented a software tool that estimates energy consumption along with two use cases that enhance the study of energy consumption in machine learning. [PERSON] et al.