AI applications are pervasive today and most innovative applications embed AI solutions. With the release of the ChatGPT-4 model by OpenAI, large language models (LLM) have received increasing attention and research. However, AI methods require a significant amount of computational resources, which leads to considerable energy consumption. Taking OpenAI's GPT-3 model as an example, training it once approximately requires 1287 MWh of energy[1]. The recent recommendations from the European Commission emphasize the need to govern and reduce CO2 emissions from software assets. Therefore, it is necessary to identify tools and methods able to provide a reliable and realistic estimation of the energy consumption of AI solutions and their environmental impact.