## 1 Introduction
[17] provided the Machine Learning Emissions Calculator, which relies on self-reporting. The tool can estimate the carbon footprint of a Graphics Processing Unit (GPU) compute by specifying hardware types, hours used, cloud providers, and regions. However, depending on where and how energy is sourced, stored, and delivered, the rise of compute-intensive AI research can have significant, negative environmental effects. Attention was first drawn to the environmental impact of AI research by the seminal work of [PERSON] et al. [18], which provided a high-level estimation of the financial and environmental costs. In reference [19], the authors developed the carbon-tracker tool for monitoring and predicting the energy consumption and carbon footprint of the MLA in the training phase. Additionally, in reference [20], the authors proposed a tool (neuralPower) based on a layer-based predictive framework to estimate the energy consumption of convolutional neural networks. [PERSON] et al. [21] assessed image classification model accuracy as a function of model size and gigaflops required during inference. They also measured the average power draw required during inference on GPUs as a function of batch size. [PERSON] et al. [22] proposed SyNERGY, which is a detailed measurement of energy (i.e., at specific layers) and a predictive framework for deep neural networks on embedded platforms. Their measurement framework provides an accurate breakdown of actual power consumption and performance across all layers of neural networks. [PERSON] et al. [23] developed an energy-sensitive measurement algorithm for convolutional neural networks (CNNs) that directly uses the energy consumption of a CNN to guide the sizing process. The energy estimation methodology uses parameters extrapolated from real measurements. The study carried out by [PERSON] et al. [24] explores the tradeoff between accuracy and energy consumption in machine learning models. They propose a genetic improvement approach to optimize the accuracy-energy tradeoff, showing promising results on several benchmark datasets. The work of [PERSON] et al. [25] proposes a platform-aware model adaptation method called Chamnet, which optimizes the network architecture to fit the specific hardware platform, reducing energy consumption and improving efficiency. They achieved significant improvements in accuracy and energy efficiency compared to other state-of-the-art methods. [PERSON] et al. [26] also investigated the energy consumption of machine learning algorithms, proposing a search-based optimization method for the energy reduction of ubiquitous algorithms such as k-means, logistic regression, and decision trees. They showed that their approach can reduce energy consumption while maintaining the accuracy of the models. [PERSON] et al. [16] presented an estimation framework for energy consumption in machine learning, which can be used to estimate energy usage before running a particular model. They provided a thorough analysis of the energy consumption of various machine learning models, highlighting the importance of energy optimization in machine learning. Motivated by the aforementioned problems and limitations of past publications, it is crucial to conduct more thorough investigations into hyperparameters, as they play a significant role in the tradeoffs between performance and energy consumption and develop more precise and energy-efficient models. The present study focuses on exploring the impact of hyperparameters on the energy consumption and performance of machine learning algorithms used for cell traffic prediction. This paper proposes an original method based on hyperparameter optimization and grid search. We specified a set of values for each hyperparameter (number of layers, number of neurons per layer, algorithm optimizer, batch size, and dropout rate). We then trained our model for each combination of hyperparameters and explored the energy consumption and performance results. The best hyperparameters can then be selected based on the minimal objective function.