Review on maintenance of photovoltaic systems based on deep learning and internet of things

Hammoudi, Younes; Idrissi, Idriss; Boukabous, Mohammed; Zerguit, Youssef; Bouali, Hicham

doi:10.11591/ijeecs.v26.i2.pp1060-1072

Cited by 15 publications

(8 citation statements)

References 51 publications

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“…Deep learning is a powerful tool that can be used to solve many complex problems. However, requires an important processing power and a large amount of data in order to train fast its models and learn effectively [9].…”

Section: B Deep Learning (Dl)mentioning

confidence: 99%

Investigation of Combining Deep Learning Object Recognition with Drones for Forest Fire Detection and Monitoring

YANDOUZI¹,

GRARI²,

BERRAHAL³

et al. 2023

IJACSA

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Forest fires are a global environmental problem that can cause significant damage to natural resources and human lives. The increasing frequency and severity of forest fires have resulted in substantial losses of natural resources. To mitigate this, an effective fire detection and monitoring system is crucial. This work aims to explore and review the current advancement in the field of forest fire detection and monitoring using both drones or unmanned aerial vehicles (UAVs), and deep learning techniques. The utilization of drones fully equipped with specific sensors and cameras provides a cost-effective and efficient solution for real-time monitoring and early fire detection. In this paper, we conduct a comprehensive analysis of the latest developments in deep learning object detection, such as YOLO (You Only Look Once), R-CNN (Region-based Convolutional Neural Network), and their variants, with a focus on their potential application in the field of forest fire monitoring. The performed experiments show promising results in multiple metrics, making it a valuable tool for fire detection and monitoring.

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Section: B Deep Learning (Dl)mentioning

confidence: 99%

Investigation of Combining Deep Learning Object Recognition with Drones for Forest Fire Detection and Monitoring

YANDOUZI¹,

GRARI²,

BERRAHAL³

et al. 2023

IJACSA

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“…The mean absolute error (MAE) estimates the average error size without taking into account the direction of the abnormalities. All individual variations in the sample set have the same weight, therefore the average of the absolute errors between predictions and taking into account the effects are calculated [48], [49]. The MAE equation is shown in (2).…”

Section: Mean Absolute Errormentioning

confidence: 99%

Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT

Grari¹,

Idrissi²,

Boukabous³

et al. 2022

IJEECS

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<span lang="EN-US">The impact of wildfires, even following the fire's extinguishment, continues to affect harmfully public health and prosperity. Wildfires are becoming increasingly frequent and severe, and make the world's biodiversity in a growing serious danger. The fires are responsible for negative economic consequences for individuals, corporations, and authorities. Researchers are developing new approaches for detecting and monitoring wildfires, that make use of advances in computer vision, machine learning, and remote sensing technologies. IoT sensors help to improve the efficiency of detecting active forest fires. In this paper, we propose a novel approach for predicting wildfires, based on machine learning. It uses a regression model that we train over NASA's fire information for resource management system (FIRMS) dataset to predict fire radiant power in megawatts. The analysis of the obtained simulation results (more than 99% in the R2 metric) shows that the ensemble learning model is an effective method for predicting wildfires using an IoT device equipped with several sensors that could potentially collect the same data as the FIRMS dataset, such as smart cameras or drones.</span>

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“…Deep learning is quite effective at dealing with such massive amounts of data and can generate highly accurate results. As a result, Internet of Things (IoT), big data analytics, and deep learning are all important to the growth of a high-tech society [22], [28], [74], [77], [89], [92], [93], [101], [103], [110], [114], [126], [149], [152], [155], [183], [188], [202], [232], [265], [318], [352].…”

Section: Big Data Analytics In Iotmentioning

confidence: 99%

Insights on the internet of things: past, present, and future directions

Sutikno

Thalmann

2022

TELKOMNIKA

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The internet of things (IoT) is rapidly expanding and improving operations in a wide range of real-world applications, from consumer IoT and enterprise IoT to manufacturing and industrial IoT (IIoT). Consumer markets, wearable devices, healthcare, smart buildings, agriculture, and smart cities are just a few examples. This paper discusses the current state of the IoT ecosystem, its primary applications and benefits, important architectural stages, some of the problems and challenges it faces, and its future. This paper explains how an appropriate IoT architecture that saves data, analyzes it, and recommends corrective action improves the process's ground reality. The IoT system architecture is divided into three layers: device, gateway, and platform. This then cascades into the four stages of the IoT architectural layout: sensors and actuators; gateways and data acquisition systems; edge IT data processing; and datacenter and cloud, which use high-end apps to collect data, evaluate it, process it, and provide remedial solutions. This elegant combination provides excellent value in automatic action. In the future, IoT will continue to serve as the foundation for many technologies. Machine learning will become more popular in the coming years as IoT networks take center stage in a variety of industries.

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Review on maintenance of photovoltaic systems based on deep learning and internet of things

Cited by 15 publications

References 51 publications

Investigation of Combining Deep Learning Object Recognition with Drones for Forest Fire Detection and Monitoring

Investigation of Combining Deep Learning Object Recognition with Drones for Forest Fire Detection and Monitoring

Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT

Insights on the internet of things: past, present, and future directions

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