Shedding some light on photovoltaic solar energy in Africa – A literature review

Brunet, Carole; Savadogo, O.; Baptiste, Pierre; Bouchard, Michel A.

doi:10.1016/j.rser.2018.08.004

Cited by 49 publications

(26 citation statements)

References 112 publications

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“…Besides, the energy produced by solar irradiation is considered the most promoting and safe energy supplied from the Photovoltaic (PV) systems [5]. PV energy sources are among the most extensively accessible and highly attractive RE sources according to their significant potential for energy production [6]- [9].…”

Section: A Backgroundmentioning

confidence: 99%

“…This interval entails partitioning the dataset ( ) into = 2 ∆ℎ = 6 different datasets, and thus, = 6 ANNs will be built, optimized, and evaluated. Each dataset represents the timestamps, weather variables, and the corresponding power productions collected at each hour interval ℎ during the = 3.625 years, ℎ ∈ [1,6]. Once the overall dataset ( ) is partitioned using the three considered hour intervals, i.e., ∆ℎ = 2, 3, and 4 hours, into = 12, 8, and 6 different datasets ( ), respectively, the proposed training strategy is applied following the steps illustrated in Section III.B (Fig.…”

Section: Influence Of Using Different Hour Intervals For Dataset Pmentioning

confidence: 99%

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A Local Training Strategy-Based Artificial Neural Network for Predicting the Power Production of Solar Photovoltaic Systems

2020

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Power production prediction from Renewable Energy (RE) sources has been widely studied in the last decade. This is extremely important for utilities to counterpart electricity supply with consumer demands across centralized grid networks. In this context, we propose a local training strategy-based Artificial Neural Network (ANN) for predicting the power productions of solar Photovoltaic (PV) systems. Specifically, the timestamp, weather variables, and corresponding power productions collected locally at each hour interval h, h=[1,24] (i.e., an interval of ∆ℎ=1 hour), are exploited to build, optimize, and evaluate H=24 different ANNs for the 24 hourly solar PV production predictions. The proposed local training strategy-based ANN is expected to provide more accurate predictions with short computational times than those obtained by a single (i.e., H=1) ANN model (hereafter called benchmark) built, optimized, and evaluated globally on the entire available dataset. The proposed strategy is applied to a case study regarding a 264kWp solar PV system located in Amman, Jordan, and its effectiveness compared to the benchmark is verified by resorting to different performance metrics from the literature. Further, its effectiveness is verified and compared when Extreme Learning Machines (ELMs) are adopted instead of the ANNs, and when the Persistence model is used. The prediction performance of the two training strategies-based ANN is also investigated and compared in terms of i) different weather conditions (i.e., seasons) experienced by the solar PV system under study and ii) different hour intervals (i.e., ∆ℎ=2, 3, and 4 hours) used for partitioning the overall dataset and, thus, establishing the different ANNs (i.e., H =12, 8, and 6 models, respectively).

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Section: A Backgroundmentioning

confidence: 99%

Section: Influence Of Using Different Hour Intervals For Dataset Pmentioning

confidence: 99%

A Local Training Strategy-Based Artificial Neural Network for Predicting the Power Production of Solar Photovoltaic Systems

2020

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“…To date, the impact of photovoltaic solar energy on the sustainable development of African countries [5] remains more limited to an analysis based on the three pillars (economic, social, and environmental) [6,7]. In addition, the authors [8][9][10][11][12][13][14][15] specialize more in the area of decentralized off-grid photovoltaic equipment which corresponds more to the needs of the most vulnerable, both for logistical and financial reasons.…”

Section: Our Research Question Is As Followsmentioning

confidence: 99%

Impacts Generated by a Large-Scale Solar Photovoltaic Power Plant Can Lead to Conflicts between Sustainable Development Goals: A Review of Key Lessons Learned in Madagascar

et al. 2020

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In a context of energy transition towards renewable energies, this case study situated in Madagascar allows us to verify the extent to which an on-grid photovoltaic solar power plant represents a vector for sustainable development. The article proposes a model for assessing sustainability from a qualitative multi-criteria perspective. This analysis fits into the theoretical question of the science of sustainability by challenging the theory of endogenous development. The innovation of this research is based on the use of a qualitative approach to a technological issue filling a literature gap in the major issue of the effective sustainability of renewable energy (particularly in the context of an island state). The study emphasizes that the plant can only represent a vector for sustainable development with the collaboration of the concerned parties, which implies considering the electrification needs at the local level. The article confirms that the impacts generated by the power plant can lead to conflicts between different sustainable development goals. Theoretically, the study emphasizes that the evaluation of the sustainability of solar power plants should follow a process that: (i) uses a preferably qualitative methodology likely to understand the local conditions of the communities in which they are established; (ii) identifies dissociated indicators while taking into account the context; and (iii) analyzes the possible negative interactions between the impact areas by highlighting the key areas linked to land management and the well-being of women within a poverty reduction approach.

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“…Also high prices or taxes imposed on such equipment, lead to difficult conditions for many communities and keep fossil fuels as reference in the energy production sector in several third world countries. In other words, the PV development depends on the competitiveness of other energy production technologies [10] .…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of a hypothetical 1-hectare PV plant in the Benguela region. The first year of operation.

Pinho¹,

Ramos

Mourão

2021

AMOGJ

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The presents study evaluates the performance of a hypothetical 1-hectare solar photovoltaic (PV) plant located in the Baía Azul Beach, in Benguela, Angola. The first year performance of the plant composed by 2,784 DuoMax 365 PV modules from Trina Solar Company was evaluated by means of the VelaSolaris Polysum software package. The total surface area of the PV modules was of 5,456.64 m2. The annual alternate current electricity production was of 1,511.70 MWh allowing a total of 710.47 tCO2 of CO2 emissions reduction and a performance ratio of 72.8 %. The annual average energy and exergy efficiencies of the PV system were respectively of of 14.3 % and 14.7 %.

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Shedding some light on photovoltaic solar energy in Africa – A literature review

Abstract: Ce fichier a été téléchargé à partir de PolyPublie, le dépôt institutionnel de Polytechnique MontréalThis file has been downloaded from PolyPublie, the institutional repository of Polytechnique Montréal http://publications.polymtl.ca

Cited by 49 publications

References 112 publications

A Local Training Strategy-Based Artificial Neural Network for Predicting the Power Production of Solar Photovoltaic Systems

A Local Training Strategy-Based Artificial Neural Network for Predicting the Power Production of Solar Photovoltaic Systems

Impacts Generated by a Large-Scale Solar Photovoltaic Power Plant Can Lead to Conflicts between Sustainable Development Goals: A Review of Key Lessons Learned in Madagascar

Performance analysis of a hypothetical 1-hectare PV plant in the Benguela region. The first year of operation.

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