This research addresses the increasing need for efficient energy management in residential settings in response to the increasing global energy demands, focusing on the integration of artificial intelligence to identify energy burdens. We employ and compare some machine learning models, like Decision Trees, K-nearest neighbors, and Feedforward Neural Networks, with a primary focus on electrical current as a key parameter. The Fine K-NN model shows notable efficiency, achieving an accuracy of 99.1% in the identification of active household appliances using a single sensor. Our methodology encompasses rigorous data acquisition and preprocessing under controlled experimental conditions, ensuring the integrity and reliability of our results. This study contributes to the field by illustrating the effectiveness of specific AI models in energy management under controlled conditions, paving the way for future advancements in AI-driven energy conservation strategies.

This paper presents a fault-diagnosis methodology for detecting progressive faults in quadrotor drones using a controlled Neural ODE network and a GRU recurrent baseline. Training and evaluation data were obtained from a numerical simulation of the physical system, including vehicle dynamics, battery electrical behavior, motor thermal response, and randomly scheduled long-duration degradations. The fault modes considered are battery degradation, loss of motor effectiveness, and overheating. Simulated telemetry was corrupted with measurement noise and slow sensor drift to approximate realistic operating conditions. The analysis compares a continuous latent-space formulation with a discrete recurrent architecture under the same temporal-window representation, class definitions, and evaluation criteria. The proposed framework provides a reproducible basis for studying early incipient fault diagnosis in unmanned aerial vehicles prior to experimental validation using flight logs.