An IoT-based remote IV tracing system for analysis of city-wide solar power facilities

Shapsough, Salsabeel; Takrouri, Mohannad; Dhaouadi, Rached; Zualkernan, Imran A.

doi:10.1016/j.scs.2020.102041

Cited by 29 publications

(15 citation statements)

References 22 publications

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“…IT became a strong part of the renewable energy domain, and the new concept of the Internet of Things-IoT-is now applied in PV system characterization and monitoring, too. Shapsough et al [86] propose a monitoring architecture based on the IoT concept dedicated for PV systems. The architecture is structured with five layers: a perception layer-hardware for data acquisition and processing (edge computing); network layer-the wireless technologies used for connecting the measuring hardware with other parts of the system through the Internet; middleware layer-technologies dedicated for data storage, processing (even using AI and cloud computing) and message transfer; application layer-dedicated for user interfaces; and business layer-technologies and tools for interconnection with other services.…”

Section: Capacitor-based Methods (Cbm)mentioning

confidence: 99%

Comprehensive Review of Methods and Instruments for Photovoltaic–Thermoelectric Generator Hybrid System Characterization

Cotfas

2020

Energies

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Finding new sustainable energy sources or improving the efficiencies of the existing ones represents a very important research and development direction. The hybridization approach is one solution for increasing the efficiency of the existing energy sources. In the case of photovoltaic technology, the hybridization of the photovoltaic panels (PV) with thermoelectric generators (TEGs) has become a more interesting solution for the research community in the last decade. Thus, a comprehensive review of the characterization methods and instruments used in PV-TEG hybrid system study represents the objective of this work. PV and TEG equivalent circuits are presented. The instruments and software applications used for the measurements and simulations are presented and analyzed. The analysis of the literature reveals that there are many papers that offer partial or no information about the instruments used or about the measurement quality (accuracies, uncertainties, etc.). In hybrid system modeling, the preferred software applications are MATLAB (MathWorks, Natick, MA, USA) and COMSOL Multiphysics (Comsol, Burlington, MA, USA), while for experimental studies based on computers, LabVIEW (NI, Austin, TX, USA) is preferred. This review work could be interesting for researchers and engineers who are interested in finding solutions for characterizing or monitoring hybrid system components, but it is not limited to these.

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Section: Capacitor-based Methods (Cbm)mentioning

confidence: 99%

Comprehensive Review of Methods and Instruments for Photovoltaic–Thermoelectric Generator Hybrid System Characterization

Cotfas

2020

Energies

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“…In [ 21 ], a literature review of IoT energy platforms aimed at end users is presented, where platform selection, new energy platform construction and, finally, platform comparison are considered. In [ 22 ], the design and implementation of an IoT-based solar monitoring system for city-wide, large-scale, and distributed solar facilities in smart cities was presented. In [ 23 ], a solar tracking system enabling increased efficiency of photovoltaic systems was proposed.…”

Section: Related Workmentioning

confidence: 99%

A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy

Almonacid-Olleros

Almonacid

Fernández-Carrasco

et al. 2020

Sensors

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The classic models used to predict the behavior of photovoltaic systems, which are based on the physical process of the solar cell, are limited to defining the analytical equation to obtain its electrical parameter. In this paper, we evaluate several machine learning models to nowcast the behavior and energy production of a photovoltaic (PV) system in conjunction with ambient data provided by IoT environmental devices. We have evaluated the estimation of output power generation by human-crafted features with multiple temporal windows and deep learning approaches to obtain comparative results regarding the analytical models of PV systems in terms of error metrics and learning time. The ambient data and ground truth of energy production have been collected in a photovoltaic system with IoT capabilities developed within the Opera Digital Platform under the UniVer Project, which has been deployed for 20 years in the Campus of the University of Jaén (Spain). Machine learning models offer improved results compared with the state-of-the-art analytical model, with significant differences in learning time and performance. The use of multiple temporal windows is shown as a suitable tool for modeling temporal features to improve performance.

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“…Regarding the monitoring of electrical appliances and devices, there are few studies in this area. However, the most mature studies base their approach to energy consumption in buildings [29,33]. Similar to the case of thermal comfort variables, laboratory conditions prevail for the analysis of developed energy monitoring IoT systems [29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

“…However, the most mature studies base their approach to energy consumption in buildings [29,33]. Similar to the case of thermal comfort variables, laboratory conditions prevail for the analysis of developed energy monitoring IoT systems [29][30][31][32][33]. Furthermore, the state-of-the-art review reveals that although monitoring systems based on IoT have been designed for buildings, they have not addressed the interaction between energy demand and thermal satisfaction, playing these concepts in isolation.…”

Section: Introductionmentioning

confidence: 99%

IoT System for the Continuous Electrical and Environmental Monitoring into Mexican Social Housing Evaluated under Tropical Climate Conditions

et al. 2022

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This work presented the design, development, and implementation of a low-cost IoT system for real-time monitoring of electrical consumption and environment parameters inside social housing. The IoT monitoring system is composed of a set of remote measurement nodes that wirelessly sense and transmit records of the air temperature and relative humidity inside the house as well as the voltage, current, and power consumed by several electrical devices. A server coordinator composed of a Raspberry Pi was used for the interaction with the sensors through the Message Queue Telemetry Transport (MQTT) protocol and allowed the measured data packaging, fragmentation, transfer, storage, and cloud upload for remote multiuser visualization. The system was experimentally evaluated in an inhabited single-story house under tropical climate conditions. Operation tests indicate a successful performance of the protocols implemented for remote visualization and cloud storage. Moreover, an analysis of measured data allows the feasibility of identifying the occupants’ energy consumption patterns and their relationship with the search for comfortable environmental conditions. Thus, the proposed friendly framework is a promising alternative to energy management. Its implementation in embedded Linux-based systems provides the flexibility to integrate control strategies based on artificial intelligence.

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An IoT-based remote IV tracing system for analysis of city-wide solar power facilities

Cited by 29 publications

References 22 publications

Comprehensive Review of Methods and Instruments for Photovoltaic–Thermoelectric Generator Hybrid System Characterization

Comprehensive Review of Methods and Instruments for Photovoltaic–Thermoelectric Generator Hybrid System Characterization

A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy

IoT System for the Continuous Electrical and Environmental Monitoring into Mexican Social Housing Evaluated under Tropical Climate Conditions

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