Performance assessment of a V-trough photovoltaic system and prediction of power output with different machine learning algorithms

Ağbulut, Ümit; Gürel, Ali Etem; Ergün, Alper; Ceylan, İlhan

doi:10.1016/j.jclepro.2020.122269

Cited by 62 publications

(27 citation statements)

References 48 publications

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“…61 A comparison was made between the current LCPV system While comparing with the recently existing water-cooled LPVC system, 60 the current LPVC system obtained 9.20% higher electrical efficiency. Compared to the existing uncooled LCPV system, 26 the current LPVC system obtained 68.66% higher electrical efficiency. Given its higher thermal of conductivity relative to other existing coolants such as pure water and air (Table 6), cooling the LCPV with Al 2 O 3 /water nanofluid in case of direct contact is, therefore, more effective.…”

Section: Comparison Between the Performance Of Current Lcpv System And The Existing Systemsmentioning

confidence: 76%

“…Since, based on the concentration ratio, X, solar cell will be classified as a low, medium and high concentrator with a breakdown as: low, X ≤ 10; medium, 10 ˂ X ˂ 100; and high, X ≥ 100 25 . Among the various usable low concentration photovoltaic (LCPV) techniques, photovoltaic concentrators that are utilizing easy and low‐cost plane reflectors such as V‐trough photovoltaic concentrators 26 . Concentrated V‐trough (LCPV) system equipped with reflector mirrors utilizing a combination of copper metal and water cooling channels mounted to the back of the PV module offers a higher electrical power output compared to conventional module especially during noon conditions 27 .…”

Section: Introductionmentioning

confidence: 99%

“…25 Among the various usable low concentration photovoltaic (LCPV) techniques, photovoltaic concentrators that are utilizing easy and low-cost plane reflectors such as Vtrough photovoltaic concentrators. 26 Concentrated Vtrough (LCPV) system equipped with reflector mirrors utilizing a combination of copper metal and water cooling channels mounted to the back of the PV module offers a higher electrical power output compared to conventional module especially during noon conditions. 27 Abd El-Samie et al 28 studied the performance of a low concentrated LCPV/T system employing water and ethylene glycol as coolant.…”

Section: Introductionmentioning

confidence: 99%

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Performance enhancement of concentrator photovoltaic systems using nanofluids

et al. 2020

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In the current study, a newly developed low photovoltaic concentrator (LCPV) equipped with nanofluid-based cooling channels, directly contacting the rare LCPV, was examined in detail. Nanofluid acts as a coolant that flowing over the back of LCPV to maintain its optimal operating temperature with the best overall performance. The coolants used are water and aluminum oxide (Al 2 O 3)/water nanofluids 1%, 2%, and 3% by volume concentration. When circulating a 3%-Al 2 O 3 /water nanofluid, the temperature of the LCPV module is dropped by 16.47 C compared to the uncooled module and keeps its operating temperature under 45 C during the experimental tests. This reduction in the LCPV module temperature with V-trough reflector mirrors in turn increased its performance. The results revealed that the daily electrical output power of the uncooled LCPV module was 1646.85 W, whereas, was 1870.55 W with (13.58%) improvement with the case of water cooling. However, cooling with 3%-Al 2 O 3 /water nanofluid, the power output of the LPVC was 2319.88 W with (24.02%) and (40.86%) enhancement compared to pure water cooling and without cooling respectively. The rise in the volume fraction ratio of Al 2 O 3 nanoparticles remarkably decreases the operating temperature of the LCPV module and improves both electrical and thermal efficiency.

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Section: Comparison Between the Performance Of Current Lcpv System And The Existing Systemsmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance enhancement of concentrator photovoltaic systems using nanofluids

et al. 2020

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“…Another paper where ML algorithms are used in the field of CPV is [116]. The authors developed four machine-learning algorithms (support vector machine, ANN, kernel, nearest-neighbour and deep learning) to predict the power outputs of a CPV system.…”

Section: Other Applications In the Pv Fieldmentioning

confidence: 99%

A State-of-Art-Review on Machine-Learning Based Methods for PV

et al. 2021

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In the current era, Artificial Intelligence (AI) is becoming increasingly pervasive with applications in several applicative fields effectively changing our daily life. In this scenario, machine learning (ML), a subset of AI techniques, provides machines with the ability to programmatically learn from data to model a system while adapting to new situations as they learn more by data they are ingesting (on-line training). During the last several years, many papers have been published concerning ML applications in the field of solar systems. This paper presents the state of the art ML models applied in solar energy’s forecasting field i.e., for solar irradiance and power production forecasting (both point and interval or probabilistic forecasting), electricity price forecasting and energy demand forecasting. Other applications of ML into the photovoltaic (PV) field taken into account are the modelling of PV modules, PV design parameter extraction, tracking the maximum power point (MPP), PV systems efficiency optimization, PV/Thermal (PV/T) and Concentrating PV (CPV) system design parameters’ optimization and efficiency improvement, anomaly detection and energy management of PV’s storage systems. While many review papers already exist in this regard, they are usually focused only on one specific topic, while in this paper are gathered all the most relevant applications of ML for solar systems in many different fields. The paper gives an overview of the most recent and promising applications of machine learning used in the field of photovoltaic systems.

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“…A number of studies presented in the literature indicate the significance of solar energy-based systems. Solar dryers (Hatami et al , 2020; Sözen et al , 2020; Sözen et al , 2021), solar ponds technology (Ines et al , 2019; Kasaeian et al , 2018; Abu-Hamdeh et al , 2020), photovoltaic-thermal systems (Elmustapha et al , 2018; Ağbulut et al , 2020) and solar collectors (Vera-Medina et al , 2018; Tong et al , 2019; Varol et al , 2010) are some of the important studies in the field of solar energy systems presented in the literature.…”

Section: Introductionmentioning

confidence: 99%

Numerical and experimental analysis of parallel-pass forced convection solar air heating wall with different plenum and absorber configurations

Khanları

Sözen

Afshari

et al. 2021

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Purpose Over the recent years, solar energy has received outstanding attention from researchers. Solar energy applications and related large-scale projects are increasing to meet growing global energy demand as an economical, non-polluting and renewable energy source. The purpose of this study is investigating different plenum and absorber configurations of solar air heating wall (SAHW) experimentally and numerically. Design/methodology/approach In this study, various configurations of SAHW have been numerically simulated to determine the most effective design. According to the simulation results, two SAHWs with various plenum thicknesses have been fabricated and tested at different conditions. Findings Numerical simulation results indicated that parallel-flow SAHWs exhibited better performance in comparison with other placements of absorber plate. Regarding to the experimentally attained results, the highest thermal efficiency was reached to 80.51%. Also, the average deviation between experimentally and numerically obtained outlet temperature is 5.5%. Originality/value Considering the obtained results in the present study, designed SAHW has admissible efficiency to be used in various industrial and residential applications such as; air preheating, space heating and drying.

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Performance assessment of a V-trough photovoltaic system and prediction of power output with different machine learning algorithms

Cited by 62 publications

References 48 publications

Performance enhancement of concentrator photovoltaic systems using nanofluids

Performance enhancement of concentrator photovoltaic systems using nanofluids

A State-of-Art-Review on Machine-Learning Based Methods for PV

Numerical and experimental analysis of parallel-pass forced convection solar air heating wall with different plenum and absorber configurations

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