A power prediction approach for a solar-powered aerial vehicle enhanced by stacked machine learning technique

Neha Sehrawat; Sahil Vashisht; Amritpal Singh; Gaurav Dhiman; Wattana Viriyasitavat; Norah Saleh Alghamdi

doi:10.1016/j.compeleceng.2024.109128

A power prediction approach for a solar-powered aerial vehicle enhanced by stacked machine learning technique

Neha Sehrawat, Sahil Vashisht, Amritpal Singh, Gaurav Dhiman^*, Wattana Viriyasitavat, Norah Saleh Alghamdi

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

34 Citations (Scopus)

Abstract

This study aims to enhance the solar energy harvesting capabilities of Unmanned Aerial Vehicles (UAVs), with a focus on integrating solar power to improve overall energy harvesting systems. The proposed method combines two independent renewable systems to extract electricity from the environment. UAV wings equipped with solar panels capture solar energy, employing optimal power point tracking for increased efficiency. Simulation results utilize an ensemble machine learning algorithm, incorporating environmental variables and UAV data to predict solar power output. A comparative analysis involving various machine learning algorithms provides additional insights gleaned from the UAV dataset.

Original language	English
Article number	109128
Pages (from-to)	1-16
Number of pages	16
Journal	Computers and Electrical Engineering
Volume	115
Early online date	19 Feb 2024
DOIs	https://doi.org/10.1016/j.compeleceng.2024.109128
Publication status	Published - 1 Apr 2024
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Cloud computing
Ensemble algorithms
Machine learning
Regression
Solar energy
Solar power output
Stacking
Unmanned aerial vehicle

Access to Document

10.1016/j.compeleceng.2024.109128

Cite this

@article{db8b15faad1d4c97b41e5a2acef3e573,

title = "A power prediction approach for a solar-powered aerial vehicle enhanced by stacked machine learning technique",

abstract = "This study aims to enhance the solar energy harvesting capabilities of Unmanned Aerial Vehicles (UAVs), with a focus on integrating solar power to improve overall energy harvesting systems. The proposed method combines two independent renewable systems to extract electricity from the environment. UAV wings equipped with solar panels capture solar energy, employing optimal power point tracking for increased efficiency. Simulation results utilize an ensemble machine learning algorithm, incorporating environmental variables and UAV data to predict solar power output. A comparative analysis involving various machine learning algorithms provides additional insights gleaned from the UAV dataset.",

keywords = "Cloud computing, Ensemble algorithms, Machine learning, Regression, Solar energy, Solar power output, Stacking, Unmanned aerial vehicle",

author = "Neha Sehrawat and Sahil Vashisht and Amritpal Singh and Gaurav Dhiman and Wattana Viriyasitavat and Alghamdi, \{Norah Saleh\}",

year = "2024",

month = apr,

day = "1",

doi = "10.1016/j.compeleceng.2024.109128",

language = "English",

volume = "115",

pages = "1--16",

journal = "Computers and Electrical Engineering",

issn = "0045-7906",

publisher = "Elsevier",

}

TY - JOUR

T1 - A power prediction approach for a solar-powered aerial vehicle enhanced by stacked machine learning technique

AU - Sehrawat, Neha

AU - Vashisht, Sahil

AU - Singh, Amritpal

AU - Dhiman, Gaurav

AU - Viriyasitavat, Wattana

AU - Alghamdi, Norah Saleh

PY - 2024/4/1

Y1 - 2024/4/1

N2 - This study aims to enhance the solar energy harvesting capabilities of Unmanned Aerial Vehicles (UAVs), with a focus on integrating solar power to improve overall energy harvesting systems. The proposed method combines two independent renewable systems to extract electricity from the environment. UAV wings equipped with solar panels capture solar energy, employing optimal power point tracking for increased efficiency. Simulation results utilize an ensemble machine learning algorithm, incorporating environmental variables and UAV data to predict solar power output. A comparative analysis involving various machine learning algorithms provides additional insights gleaned from the UAV dataset.

AB - This study aims to enhance the solar energy harvesting capabilities of Unmanned Aerial Vehicles (UAVs), with a focus on integrating solar power to improve overall energy harvesting systems. The proposed method combines two independent renewable systems to extract electricity from the environment. UAV wings equipped with solar panels capture solar energy, employing optimal power point tracking for increased efficiency. Simulation results utilize an ensemble machine learning algorithm, incorporating environmental variables and UAV data to predict solar power output. A comparative analysis involving various machine learning algorithms provides additional insights gleaned from the UAV dataset.

KW - Cloud computing

KW - Ensemble algorithms

KW - Machine learning

KW - Regression

KW - Solar energy

KW - Solar power output

KW - Stacking

KW - Unmanned aerial vehicle

UR - https://www.scopus.com/pages/publications/85185401534

U2 - 10.1016/j.compeleceng.2024.109128

DO - 10.1016/j.compeleceng.2024.109128

M3 - Article

AN - SCOPUS:85185401534

SN - 0045-7906

VL - 115

SP - 1

EP - 16

JO - Computers and Electrical Engineering

JF - Computers and Electrical Engineering

M1 - 109128

ER -

A power prediction approach for a solar-powered aerial vehicle enhanced by stacked machine learning technique

Abstract

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this