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
f
irst 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, pay
load, and external factors like altitude and airspeed. These models are used to simu
late drone power consumption in a variety of scenarios, demonstrating their accu
racy 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 predic
tions 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 opera
tor (EO), and steady-state operator (SSO). Evaluation metrics are compared both
before and after normalization for seven models: Eldosuky, Eman, D’Andrea, Dor
ling, 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 notewor
thy enhancement with RMSE of 0.24303 and MAE of 0.08805. |
