A LoRaWAN based Open Source IOT Solution for Monitoring Rural Electrification Policy

Shaik, Mohammed Samdani; Shah, Dipam; Chetty, Raghuram; Marathe, Rahul R.

doi:10.1109/comsnets48256.2020.9027490

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…In Tables 2 and 3, a comprehensive list of popular microcontroller boards for solar plant monitoring is presented, including Uno, Nano, Mega, and NodeMCU. In the majority of the proposed solutions, Raspberry Pi (RPi) has been employed as presented by [3,4,6,7,16,20,32,37,41,43,49,50]. However, RPi is not an optimum choice for end devices considering its monetary cost and power requirements.…”

Section: Microcontroller Boardmentioning

confidence: 99%

“…In the existing literature, various wireless communication technologies have been employed for data transmission to remote servers. These technologies range from LoRA [4,43], LoRaWAN [50], RF [9,16,39], and Wi-Fi [3,7,14,20,21,42,[45][46][47][48]. However, to reduce network complexity and the ubiquitous presence of cellular towers in remote locations, GSM technology (SIM900) is the most optimum solution for edge nodes.…”

Section: Edge Layermentioning

confidence: 99%

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Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring

Ali,

Dost,

Khattak

et al. 2024

Future Internet

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Renewable Energy Sources (RESs) such as hydro, wind, and solar are merging as preferred alternatives to fossil fuels. Among these RESs, solar energy is the most ideal solution; it is gaining extensive interest around the globe. However, due to solar energy’s intermittent nature and sensitivity to environmental parameters (e.g., irradiance, dust, temperature, aging and humidity), real-time solar plant monitoring is imperative. This paper’s contribution is to compare and analyze current IoT trends and propose future research directions. As a result, this will be instrumental in the development of low-cost, real-time, scalable, reliable, and power-optimized solar plant monitoring systems. In this work, a comparative analysis has been performed on proposed solutions using the existing literature. This comparative analysis has been conducted considering five aspects: computer boards, sensors, communication, servers, and architectural paradigms. IoT architectural paradigms employed have been summarized and discussed with respect to communication, application layers, and storage capabilities. To facilitate enhanced IoT-based solar monitoring, an edge computing paradigm has been proposed. Suggestions are presented for the fabrication of edge devices and nodes using optimum compute boards, sensors, and communication modules. Different cloud platforms have been explored, and it was concluded that the public cloud platform Amazon Web Services is the ideal solution. Artificial intelligence-based techniques, methods, and outcomes are presented, which can help in the monitoring, analysis, and management of solar PV systems. As an outcome, this paper can be used to help researchers and academics develop low-cost, real-time, effective, scalable, and reliable solar monitoring systems.

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Section: Microcontroller Boardmentioning

confidence: 99%

Section: Edge Layermentioning

confidence: 99%

Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring

Ali,

Dost,

Khattak

et al. 2024

Future Internet

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“…The power electronic converters with suitable control allows integration of these resources to RESs, forming a DC microgrid (MG). The requirement of smaller distribution network and low power demand of such remote area electrification allows the deployment of microgrids of few kilowatts capacities [4]. This paper proposes the DC microgrid with integration of SPV, MH and Battery based sources with a novel hierarchical control for power management strategy.…”

Section: System Descriptionmentioning

confidence: 99%

Integrated Solar PV-Battery and Micro-Hydro Based Low-Voltage Autonomous DC Microgrid for Rural Electrification

Muchande¹,

Thale²,

Wandhare

2020

2020 47th IEEE Photovoltaic Specialists Conference (PVSC)

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The decreasing cost and simplicity in deployment of Solar-Photovoltaic (SPV) system have led to its penetration extended to the highly remote/rural areas. The SPV systems possessing non-dispatchable characteristics are typically deployed in the form of a DC microgrid or an AC microgrid in such areas. For rural electrification, a low voltage DC microgrid with SPV source suitably integrated with local resources like wind or microhydro generation plant supported with energy storage becomes highly desirable to enhance the availability of power throughout the day. This paper presents implementation and control of an autonomous Integrated SPV-battery and Micro-hydro based DC microgrid (DC-MG) meeting the need of electrical energy in remote rural areas. This paper proposes the hierarchical power control which enhances the operational reliability and flexibility.

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“…IoT-based solutions are proposed by researchers in [56][57][58] to make available appropriate and suitable source management, load shedding, data acquisition, and control of the SAPV systems and monitor and evaluate the electrification projects. [59] show that IoT provides the capability to bring into use the MATLAB, ThingSpeak, and other tools/ functions by granting the authority to one person to operate the forecasting system.…”

Section: Introductionmentioning

confidence: 99%

A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms

Patel

Swathika

Subramaniam

et al. 2022

International Journal of Photoenergy

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Climate change and the energy crisis substantially motivated the use and development of renewable energy resources. Solar power generation is being identified as the most promising and abundant source for bulk power generation. However, solar photovoltaic panel is heavily dependent on meteorological data of the installation site and weather fluctuations. To overcome these issues, collecting performance data at the remotely installed photovoltaic panel and predicting future power generation is important. The key objective of this paper is to develop a scaled-down prototype of an IoT-enabled datalogger for photovoltaic system that is installed in a remote location where human intervention is not possible due to harsh weather conditions or other circumstances. An Internet of Things platform is used to store and visualize the captured data from a standalone photovoltaic system. The collected data from the datalogger is used as a training set for machine learning algorithms. The estimation of power generation is done by a linear regression algorithm. The results are been compared with results obtained by another machine learning algorithm such as polynomial regression and case-based reasoning. Further, a website is developed wherein the user can key in the date and time. The output of that transaction is predicted temperature, humidity, and forecasted power generation of the specific standalone photovoltaic system. The presented results and obtained characteristics confirm the superiority of the proposed techniques in predicting power generation.

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A LoRaWAN based Open Source IOT Solution for Monitoring Rural Electrification Policy

Cited by 6 publications

References 5 publications

Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring

Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring

Integrated Solar PV-Battery and Micro-Hydro Based Low-Voltage Autonomous DC Microgrid for Rural Electrification

A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms

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