Study on IoT networks with the combined use of wireless power transmission and solar energy harvesting

Chinipardaz, Maryam; Amraee, Somaieh

doi:10.1007/s12046-022-01829-y

Cited by 13 publications

(3 citation statements)

References 67 publications

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“…The growth of machine learning technique is increasing extravagantly and their applicability for solving varied problems of medical image classification, solving optimization problems and for automobile based applications has been reported in the works of Alzubi et al ( 2019) [62], Braik et al (2022) [63] and Alzubi et al (2022) [64] respectively. Various studies in respect of IOT based solar PV based energy harvesting and based on wireless sensor networks has been dealt and is currently going on in this related field of solar PV power generation studies [65][66][67]. The related works section thus provides a clear insight on the works earlier and presently going on in this solar PV power production including their varied applications.…”

Section: Plos Onementioning

confidence: 99%

Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

et al. 2022

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A novel Variational Mode Decomposition (VMD) combined Fuzzy-Twin Support Vector Machine Model with deep learning mechanism is devised in this research study to forecast the solar Photovoltaic (PV) output power in day ahead basis. The raw data from the solar PV farms are highly fluctuating and to extract the useful stable components VMD is employed. A novel Fuzzy–Twin Support Vector Machine (FTSVM) model developed acts as the forecasting model for predicting the solar PV output power for the considered solar farms. The twin support vector machine (SVM) model formulates two separating hyperplanes for predicting the output power and in this research study a fuzzy based membership function identifies most suitable two SVM prediction hyperplanes handling the uncertainties of solar farm data. For the developed, new VMD-FTSVM prediction technique, their optimal parameters for the training process are evaluated with the classic Ant Lion Optimizer (ALO) algorithm. The solar PV output power is predicted using the novel VMD-FTSVM model and during the process multi-kernel functions are utilized to devise the two fuzzy based hyperplanes that accurately performs the prediction operation. Deep learning (DL) based training of the FTSVM model is adopted so that the deep auto-encoder and decoder module enhances the accuracy rate. The proposed combined forecasting model, VMD-ALO-DLFTSVM is validated for superiority based on a two 250MW PV solar farm in India. Results prove that the proposed model outperforms the existing model in terms of the performance metrics evaluated and the forecasted PV Power.

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Section: Plos Onementioning

confidence: 99%

Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

et al. 2022

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“…From using multi-modal image classifications for COVID-19 detection to employing FPGA for quantum clustering and AI for medical diagnostics and path planning , these developments highlight the convergence of technology in solving complex problems . Efficient code compression for embedded systems and IoT applications in vehicular safety further illustrate the role of technology in enhancing efficiency and safety [80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95]. Collectively, these achievements reflect a multidisciplinary approach towards leveraging computational algorithms and hardware capabilities to address a wide range of contemporary challenge.…”

Section: Introductionmentioning

confidence: 99%

Advanced crop monitoring: incorporating the Kalman filter into modern agriculture

Obaideen,

Faroukh,

Bonny

et al. 2024

Sensing for Agriculture and Food Quality and Safety XVI

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This paper investigates the integration of Kalman Filter technologies into modern agricultural practices, with a focus on advancing crop monitoring techniques. Through a comprehensive bibliometric and text mining analysis, we explore the evolution of research in this area from 1983 to 2023, highlighting the steady growth in scholarly publications and the interdisciplinary nature of the field. Our findings reveal a significant interest in leveraging Kalman Filter algorithms to enhance the precision and efficiency of agricultural operations, addressing the challenges of sustainability and food security. The study underscores the importance of data assimilation, real-time monitoring, and predictive analysis in agriculture, facilitated by the adoption of Kalman Filter and its variants. We also examine the integration of emerging technologies, such as unmanned aerial vehicles (UAVs) and remote sensing, with Kalman Filter techniques to develop sophisticated agricultural monitoring systems. The paper concludes with insights into future research directions, emphasizing the need for overcoming barriers to technology adoption and fostering interdisciplinary collaborations. This work contributes to the understanding of how computational intelligence can transform agricultural practices, offering solutions for more sustainable and efficient farming.

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“…There are two ways to identify each PV/T system's optimal performance point: conducting several experiments and utilizing sophisticated computer techniques. Nowadays, computer approaches direct us to quickly and correctly find the optimal solution for each complex system [9]. In an air PV/T system, Shahsavar et al 's [10] study concentrated on a number of certain variables, such as the channel's depth, length, and width as well as the output air temperature for system optimizing, and report the finest cases, based on NSGA.…”

mentioning

confidence: 99%

Electrical and thermal performance analysis of hybrid photovoltaic/thermal water collector using meta-heuristic optimization

Aggour,

Atia,

Farghally

et al. 2024

Journal of Electrical Systems and Inf Technol

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The photovoltaic/thermal (PV/T) flat-panel technology has numerous advantages over PV modules and separately mounted solar thermal collectors regarding overall effectiveness and space-saving. Hybrid PV/T solar collectors’ thermal and electrical performance is influenced by design parameters like mass flow rate, tube diameter, tube spacing, packing factor, and absorber conductivity. This paper focused on using several meta-heuristic optimization techniques, incorporating the following: multiverse algorithm, dragonfly algorithm, sine–cosine algorithm, moth-flame algorithm, whale algorithm, particle swarm algorithm, ant-lion algorithm, grey wolf algorithm, and particle swarm optimization algorithm in PV/T collector optimal design according to maximum total efficiency obtained. The outcomes of the various algorithms revealed that the maximum electrical efficiency of the PV/T collector ranged from 13.85 to 14.28%, while the maximum thermal efficiencies ranged from 41.41 to 52.08% under standard test conditions (1000 W/m2 and 25 °C). The optimized values for the design parameters of the PV/T collector were as follows: the absorber conductivity was determined to be 356.6 W/m K, the packing factor was optimized to 0.7, the mass flow rate was set at 0.019 kg/s, the tube width was determined to be 0.035 m, and the tube spacing was optimized to 0.0524 m. The results indicated that the grey wolf optimizer (GWO) algorithm proved to be highly effective in optimizing the design parameters of PV/T collectors. Furthermore, the study examined the relationship between the temperature of PV modules and PV/T collectors by considering variations in mass flow rate, packing factor, and tube width at different solar radiation levels. The results confirmed that the PV/T collector temperature exhibited improvements compared to the PV module temperature. As a result, this led to higher electrical efficiency and an overall increase in the total efficiency of the PV/T collector.

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Study on IoT networks with the combined use of wireless power transmission and solar energy harvesting

Cited by 13 publications

References 67 publications

Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

Advanced crop monitoring: incorporating the Kalman filter into modern agriculture

Electrical and thermal performance analysis of hybrid photovoltaic/thermal water collector using meta-heuristic optimization

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