Many researchers have taken an interest in climate change and environmental issues, as they have a significant impact on people’s well-being. Artificial Intelligence have been integrated in the field of energy, which is considered as an essential resource to our daily lives, to predict energy consumption and manage energy resources in order to attain a sustainable and durable future. The development of machine learning models has become an essential part of this process. However, these models are often viewed as black boxes, making it difficult to gain insights from them. Explainable Artificial Intelligence (XAI) has emerged to address this concern. In this paper, our goal is to develop an explanatory approach that explains each part of the machine learning pipeline, from the collection of data to the building of models. Machine learning models and explainable artificial intelligence (XAI) tools will be used to achieve this. Three datasets, namely electrical energy consumption, solar radiation production and weather, have been used to build models using SVR, MLP and XGB. The most influential features that affect the increase in energy consumption have been retrieved using SHAP, LIME and Eli5.