Renewable energy adoption: Design, development, and assessment of solar tree for the mountainous region

Kumar, Roushan; Bansal, Kamal; Kumar, Adesh; Yadav, Jitendra; Gupta, Mukul; Singh, Vivek Kumar

doi:10.1002/er.7197

Cited by 21 publications

(5 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNNs and Deep Learning (DL), in particular, have been widely recognized as popular solutions for a broad range of applications based on computer vision, such as activity recognition, person recognition, vehicle recognition, and classification [6][7][8][9][10]. In prior research, utilizing transfer learning, a pre-trained CNN was used as a feature extricate, and certain layers have been added to perform classification for the current job, learning techniques to outperform standard machine learning methods using handcrafted features.…”

Section: Introductionmentioning

confidence: 99%

Efficient Deep Learning Framework for Fire Detection in Complex Surveillance Environment

Dilshad¹,

Khan²,

Song³

2023

CSSE

View full text Add to dashboard Cite

To prevent economic, social, and ecological damage, fire detection and management at an early stage are significant yet challenging. Although computationally complex networks have been developed, attention has been largely focused on improving accuracy, rather than focusing on real-time fire detection. Hence, in this study, the authors present an efficient fire detection framework termed E-FireNet for real-time detection in a complex surveillance environment. The proposed model architecture is inspired by the VGG16 network, with significant modifications including the entire removal of Block-5 and tweaking of the convolutional layers of Block-4. This results in higher performance with a reduced number of parameters and inference time. Moreover, smaller convolutional kernels are utilized, which are particularly designed to obtain the optimal details from input images, with numerous channels to assist in feature discrimination. In E-FireNet, three steps are involved: preprocessing of collected data, detection of fires using the proposed technique, and, if there is a fire, alarms are generated and transmitted to law enforcement, healthcare, and management departments. Moreover, E-FireNet achieves 0.98 accuracy, 1 precision, 0.99 recall, and 0.99 F1-score. A comprehensive investigation of various Convolutional Neural Network (CNN) models is conducted using the newly created Fire Surveillance SV-Fire dataset. The empirical results and comparison of numerous parameters establish that the proposed model shows convincing performance in terms of accuracy, model size, and execution time.

show abstract

Section: Introductionmentioning

confidence: 99%

Efficient Deep Learning Framework for Fire Detection in Complex Surveillance Environment

Dilshad¹,

Khan²,

Song³

2023

CSSE

View full text Add to dashboard Cite

show abstract

“…Projection of shadow on panel 2 Figure (12).The algorithm to calculate the Intersection between a point and a plane Figure (13).Flowchart for the multiobjective genetic algorithm Figure (14).Simulink model for the solar tree Figure (15).Ansys loading condition Figure (16). I-V Characteristics of the PV-tree Figure (17).Von-Mises stress analysis Figure (18).Deflection…”

Section: Discussionmentioning

confidence: 99%

“…[3] it is characterized to be inhomogeneous and perpendicular to the sunlight. [17] researcher developed a roughly 5-meter simple solar tree with 7 PV panels at an angle of 28 degrees with different heights. The tree generated 28% more power in comparison to the land-based system.…”

Section: Simple Solar Treementioning

confidence: 99%

Optimization of Solar Tree Performance in Egypt: A Simulation-Based Investigation

Rabie,

Thabet,

Elsayed

et al. 2023

Preprint

View full text Add to dashboard Cite

The paper discusses the orientation and positions of solar panels in solar tree. Solar energy contributes to the most available and abundant energy source in Egypt. Hence, The PV system is considered a promising solution to generate power from this source. However, its energy density is low. Therefore, the solar tree is considered in this paper as it has higher density and fits the urban cities of Egypt. The design of the solar tree is divided into two sections: the study of the solar panel's orientation and positions. For the solar tree orientation, a single-objective genetic algorithm is used to maximize the total incident irradiance on a tilted solar panel. The output of this optimization problem is 20 optimal angles for the tilt angle (beta) and surface azimuth angle of the solar panel (yaw). These angles are inputted to the second methodology to determine their positions. In this methodology, a multi-objective genetic algorithm is used to trade between the area occupied by the solar tree representing the cost and the power loss due to the shading of the solar panels on each other, the output is the coordinates of the center of each solar panel. The proposed solar tree design increased the incident irradiance from 4427.389 to 4496.584 watt/m^2year using the optimal angles and the output power increased as the shadow loss decreased from 904.685 watts to 300 watts The final design of the solar tree is 5m height and 1.13m Wide.

show abstract

“…The notion of solar trees is a highly effective way to generate power, and it should be used in conjunction with the growing demand for energy and land economy. The study [18] describes the creation, testing, and certification of a solar tree in the Himalayan area. It produces 28% more electricity than the system that is based on land.…”

Section: A-by Region B-by Componentmentioning

confidence: 99%

NEW generation of solar energy: Investigation and implementation of artificial solar tree application in Egypt

Ibrahim,

Ashor

2024

Solar Energy

View full text Add to dashboard Cite

Renewable energy adoption: Design, development, and assessment of solar tree for the mountainous region

Cited by 21 publications

References 53 publications

Efficient Deep Learning Framework for Fire Detection in Complex Surveillance Environment

Efficient Deep Learning Framework for Fire Detection in Complex Surveillance Environment

Optimization of Solar Tree Performance in Egypt: A Simulation-Based Investigation

NEW generation of solar energy: Investigation and implementation of artificial solar tree application in Egypt

Contact Info

Product

Resources

About