Effects of particulate matters and climate condition on photovoltaic system efficiency in tropical climate region

Narkwatchara, Piya; Ratanatamskul, Chavalit; Chandrachai, Achara

doi:10.1016/j.egyr.2020.09.016

Cited by 13 publications

(2 citation statements)

References 10 publications

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“…In ref. [33] the correlation of ambient humidity with PV power for a tropical system was of 0.43, which according to Figure 2 is higher than the median correlation (in absolute value) with wind speed of 0.12. Moreover, tropical places usually show seasonal weather variations substantially different from other climates.…”

Section: Climatic Trendsmentioning

confidence: 68%

Effect of Climate on Photovoltaic Yield Prediction Using Machine Learning Models

et al. 2022

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and clouds create fluctuations in the power generation, which result in voltage unbalance, voltage rise and voltage flickers in networks with high PV presence. [3] Forecasting technologies can help grid operators with the scheduling and dispatching of this renewable energy source more effectively. Due to the stochastic nature of PV power generation, machine learning (ML) approaches have gained popularity in forecasting tasks. [4] The main characteristic of ML algorithms is that the coefficients of the model are obtained automatically by using training data. [5] There is a huge amount of algorithms inside this family, whose complexity ranges from linear regression techniques to deep learning neural networks.Despite abundant literature, most studies consider <5 PV systems that are generally located in the same areas. [6] Most researchers study systems in Europe, the US, Australia, or China, which are population-dense areas, but receive relatively small solar intensity (except for Australia). Few studies have considered PV systems subjected to diverse meteorological conditions in order to study the effect that climate has on the performance of ML models.Pasion et al. forecasted the PV power of 12 northern-hemisphere locations subjected to seven different climate regions. [7] The selected algorithm best predicted the data in a hot-dry climate region, and a mixed-humid climate region had the secondbest model performance, with coefficient of determination (R 2 ) scores of 0.968 and 0.962, respectively. In contrast, the model performance for the tropical rainforest site was the poorest with an R 2 score of 0.908. Do et al. forecasted two PV systems, one in Guadeloupe (North America, tropical climate) and the other in Lille (Europe, mild temperate climate). [8] They discovered that the system in Lille required a longer training duration but achieved a lower normalized root mean squared error of 10.69 % compared to the one in Guadeloupe (error of 11.97 %). In ref. [9], two systems located in tropical (Singapore) and mild temperate (Australia) climates were also considered for a probabilistic forecasting model. As opposed to the previous study, they reported worse metrics for the system located in the mild temperate climate, due to the highly variable Australian weather and thunderstorms in the summer season. Zhang et al. considered three distant PV systems located in the USA, Denmark, and Italy. [10] Because of the unique weather and climate characteristics of each location, the modeling parameters and best features of the optimum ML model differed between sites. Finally,

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Section: Climatic Trendsmentioning

confidence: 68%

Effect of Climate on Photovoltaic Yield Prediction Using Machine Learning Models

et al. 2022

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“…The result stated that the energy rate was about 460.77 kWh/m 2 /y and the average heat flux was 93.03 W/m 2 for the climate of Stuttgart-Germany. Thus, Narkwatchara et al, reported that the tropical climate region contributes to the negative impacts on the electrical production of PV collectors [16]. Furthermore, Misha et al, conducted simulation and experiments tests of the PV/T water system under Malaysian weather conditions.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of PV/T-TEC Collector for The Tropical Climate Conditions of Indonesia

Amrizal,

Amrul,

Warih Winardi

et al. 2022

ARFMTS

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A photovoltaic (PV) collector converts sunlight directly into electrical energy about 20% while the rest is wasted as heat energy. This heat energy increases the operating temperature of the PV collector which has negative impacts on the semiconductor material and electricity production. Concerning the issue, the PV collector is then integrated by both the finned absorber and Thermoelectric Cooler (TEC) modules which is known as a PV/T-TEC collector. This study aims to determine the performance of the PV/T-TEC collector based on the tropical climate condition in Bandarlampung, Indonesia (latitude 5°27΄S and longitude 105°16΄E). Furthermore, the finned absorber and TEC modules are attached underneath the PV collector surface to act as a heat sink. Air as a working fluid was specifically passed through the PV/T-TEC collector to absorb the unused waste heat. Thus, the irradiation level and air mass flow rate are varied in the range of 700-1000 W/m2 and 0.0075-0.075 kg/s, respectively. Based on data obtained from the experimental tests, the highest values of the thermal and electrical efficiency are found to be 26.3 % and 9.8 %, respectively. The temperature difference between the two sides of the TEC is found to be less than 2.2 oC and there is a 0.3% increase in the total electric power generated from the TEC modules which cover 12% of the PV collector surface.

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Evaluation of particle pollution influence on loss of solar power generation between commercial and background areas in Lucknow, India

Prasad

Mahanta

Bojjagani

2023

Environ Dev Sustain

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Effects of particulate matters and climate condition on photovoltaic system efficiency in tropical climate region

Cited by 13 publications

References 10 publications

Effect of Climate on Photovoltaic Yield Prediction Using Machine Learning Models

Effect of Climate on Photovoltaic Yield Prediction Using Machine Learning Models

Performance Analysis of PV/T-TEC Collector for The Tropical Climate Conditions of Indonesia

Evaluation of particle pollution influence on loss of solar power generation between commercial and background areas in Lucknow, India

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