ENERGY CONSUMPTION PREDICTION FOR ROBOTIC TASK EXECUTION USING MACHINE LEARNING AND HIGH-LEVEL OPERATIONAL DATASETS - Abuzar, Muhammad (My University, 2026)
Abstract This study examines whether energy consumption can be predicted using machine learning methods on high-level operation data sets to execute robotic tasks. With the growing integration of robotic systems in industrial and service settings, there is a strong need to enhance their energy efficiency to cut down on operation costs and to promote the cause of sustainable development. A quantitative approach was used on a designed dataset of 500 observations of robotic tasks and included processing time, task, sensor, environmental and operational status indicator features. Four predictive models were created and assessed a mean-based baseline model, a linear regression model and a random forest regressor. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to evaluate model performance on a test set held out. These findings show that the three models had widely similar predictive accuracy, with the lowest MAE (0.5385 kWh) and the lowest RMSE (0.6152 kWh) values of the random forest and the baseline models respectively. Feature importance analysis of the random forest model assigned the highest variance reduction scores to processing time and accuracy, at approximately 0.284 and 0.272 respectively. These scores reflect an algorithmic bias of the random forest toward continuous variables, which accumulate importance through a greater number of candidates split points, rather than confirmed causal influence on energy consumption. All other features contributed at much lower levels. The results indicate that machine learning models do not make significant improvements compared to a basic baseline prediction when they are trained using high-level operational data. This result implies that the dataset has not enough informative predictors to enable precise energy modeling, probably due to the failure to capture the low-level physical processes that directly determine energy consumption in robotic systems with high-level task descriptors. The paper presents a practical and replicable assessment system of data-driven energy forecasting in robotics, the main features of the datasets that constrain the performance of machine learning, and the significance of more detailed, richer data to achieve successful energy prediction in robotic tasks.
Keywords: machine learning; energy consumption (energy technology); robots; robotics; forecasts; optimisation; energy; industrial automation; energy technology; distributed systems; Automation and Robotics; Master’s Programme in Smart Energy; Robotics