Solar energy harvesting technologies for PV self-powered applications: A comprehensive review

Hao, Daning; Qi, Lingfei; Tairab, Alaeldin M.; Ahmed, Ammar; Azam, Ali; Luo, Dabing; Pan, Yajia; Zhang, Zutao; Yan, Jinyue

doi:10.1016/j.renene.2022.02.066

Cited by 186 publications

(56 citation statements)

References 120 publications

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“…It is important that with government support, as we continue with developing the renewable energy sector, we deploy a model tool mapped into the Renewable Energy Master Plan (REMP) to aid Photovoltaic System Home Users, Small and Medium-Scale Enterprises (SMSE) to make a guided decision on the choice of Photovoltaic Systems to adopt in homes, business and public places to reduce the economic burden of wastefulness that has been experienced in time past by individuals and governments collectively as a result of lack of proper calculation/estimation of system demands needed per installation (Daning et al, 2022). This we believe will play its role in the Economic Growth and Recovery Plan (EGRP) of the government.…”

Section: Discussionmentioning

confidence: 99%

Boosting the Harvesting of Nigeria’s Abundant Renewable Energy Potentials and Legal Implications

Chineke

Ozuomba²,

Anumaka³

et al. 2022

mijst

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Renewable energy has been highlighted not only as a means of satisfying the energy needs of Nigerians but also as a tool for the country's growth, thereby improving the income of the citizens who have more energy to work with, serving as the energy source for domestic and office use, in addition to reducing the carbon footprint from the conventional fossil fuels. From data obtained from the Photovoltaic Geographic Information System (PVGIS), the solar electricity potential at some selected cities in Southern Nigeria that ranges from 4.5 to 6.5 kWh/m2 has been presented as a tool, which when properly harnessed, can be used to mitigate avoidable energy-related “national disasters” such as unemployment and youth-restiveness, thereby accelerating Nigeria’s development. There is the urgent need to revise and effectively implement helpful laws and policies that support the addition of renewable energy sources for electricity generation. Lack of a coherent legal framework with incentives for the utilization of renewable energy is among the key factors causing poor utilization of renewable energy in Nigeria. Governmental and stakeholder collaboration is highly necessary for developing countries to robustly track renewable electricity adoption via laws aimed at boosting its adoption.

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Section: Discussionmentioning

confidence: 99%

Boosting the Harvesting of Nigeria’s Abundant Renewable Energy Potentials and Legal Implications

Chineke

Ozuomba²,

Anumaka³

et al. 2022

mijst

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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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“…If batteries are used to power these electronic devices, they can only meet the electricity demand for a short period and require regular manual maintenance. Not only is it a waste of human resources but these discarded batteries may also pollute the environment ( Hao et al., 2022 ; Schismenos et al., 2021 ). Environmental energy recovery technology can realize the self-powered of wireless monitoring nodes green and cleanly.…”

Section: Introductionmentioning

confidence: 99%

A vibration energy harvester for freight train track self-powered application

et al. 2022

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Solar energy harvesting technologies for PV self-powered applications: A comprehensive review

Cited by 186 publications

References 120 publications

Boosting the Harvesting of Nigeria’s Abundant Renewable Energy Potentials and Legal Implications

Boosting the Harvesting of Nigeria’s Abundant Renewable Energy Potentials and Legal Implications

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

A vibration energy harvester for freight train track self-powered application

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