Counterfactual Explanations (CE) is a type of eXplainable Artificial Intelligence (XAI) that addresses the common question of what options a user has to achieve a desired output. In this thesis, the authors propose utilizing the CE technique on regression tasks with a reject option to provide feedback to forklift drivers regarding their energy usage. Recent developments in CE shows that most of the research on CE focuses on classification tasks. Hence, the research question addresses how CE techniques can be used for regression tasks, particularly feedback-providing applications. The data was collected from the Toyotalab for forklifts using a Controller Area Network (CAN). Forklifts are an important component in logistics operations and the demand for them is increasing day by day due to the increasing trend of e-commerce and express delivery. Optimizing their energy usage can lead to significant energy savings. The study is in the direction of providing only the important feedback to the driver by using a reject option as a decision making system. The data is pre-processed and used to train an Machine Learning (ML) model and is used to predict future energy consumption based on the current values. Evaluation of results shows that theCE method exhibits slower performance compared to Local Interpretable Model-Agnostic Explanations(LIME) and SHapley Additive exPlanations (SHAP). However, it demonstrates a high degree of stability in its results. Additionally, the results show that if the driver follows the suggestions proposed by the reject option driven CE, substantial energy savings can be achieved. This work can also be extended to other regression applications where feedback is required.