Predicting power consumption of drones using explainable optimized mathematical and machine learning models

Abstract Developing an effective energy consumption prediction model has become a central focus in drone-related research. As a result, numerous models are proposed, each differing in complexity and focusing on factors such as thrust and environmental conditions. This paper presents two mathematical models and a machine learning approach to estimate the energy consumption of drones. The Eldosuky model, the first one, examines the effects of weight, thrust, and air-rotor interaction on power consumption. The second model, Eman, takes into account the drone’s mass, payload, and external factors like altitude and airspeed. These models are used to simulate drone power consumption in a variety of scenarios, demonstrating their accuracy and utility in real-world situations. Additionally, this paper uses a random forest regressor, a machine learning model that uses actual data to validate energy predictions and simulate drone performance. Then, the accuracy of the model is improved by applying the Fick’s law algorithm optimizer, which contains three phases of motion. These phases are referred to as diffusion operator (DO), equilibrium operator (EO), and steady-state operator (SSO). Evaluation metrics are compared both before and after normalization for seven models: Eldosuky, Eman, D’Andrea, Dorling, Stolaroff, Kirchstein, and Tseng. The Eldosuky model has a lower RMSE (500.7558) and MAE (313.3711) before normalization. The Fick’s law algorithm optimizer exhibits optimal metrics following normalization, showcasing a noteworthy enhancement with RMSE of 0.24303 and MAE of 0.08805.