PV technologies performance comparison in temperate climates

Aste, N.; Pero, Claudio Del; Leonforte, Fabrizio

doi:10.1016/j.solener.2014.08.015

Cited by 64 publications

(29 citation statements)

References 27 publications

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“…The a-si has more important capture and system losses in comparison with those of c-si and p-si, this result can be explain to the a-si's greater temperature coefficient. Also the PR varies with solar irradiation [20] and the PR is maximal for the three technologies in the cloudy day owing the fact that the decreasing in losses which is caused by the decrease of module temperature. and rainy (C)) shows the final yields, the losses of the captures and the losses of systems for the three technologies during the three days.…”

Section: Performances Of the Pv Systemsmentioning

confidence: 99%

Performance Analysis and Comparison of Different Photovoltaic Modules Technologies under Different Climatic Conditions in Casablanca

Karami¹,

Rafi²,

Haibaoui³

et al. 2017

J Fundam Renewable Energy Appl

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Section: Performances Of the Pv Systemsmentioning

confidence: 99%

Performance Analysis and Comparison of Different Photovoltaic Modules Technologies under Different Climatic Conditions in Casablanca

Karami¹,

Rafi²,

Haibaoui³

et al. 2017

J Fundam Renewable Energy Appl

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“…However, such PR value can be considered within the normal range; literature showed that PV arrays installed in Italy have a PR value varying across the range from 62% to 81% [26], though it has a different tilt angle and different system structure. Some research shows that the climatic conditions have a great impact on the PR [27]. For example, the year that had the greatest number of days with bad weather (rain, snow, or fog) had the lower PR; on the contrary, the highest PR [25].…”

Section: Analysis and Discussionmentioning

confidence: 99%

Grid-Connected Semitransparent Building-Integrated Photovoltaic System: The Comprehensive Case Study of the 120 kWp Plant in Kunming, China

Wang

Hassanien

et al. 2018

International Journal of Photoenergy

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A 120 kWp building-integrated photovoltaic (BIPV) system was installed on the south facade of the Solar Energy Research Institute building in Yunnan Normal University. The area of the curtain wall was 1560 m 2 (26 m × 60 m), which consisted of 720 semitransparent monocrystalline silicon double-glazing PV panels. This paper studied the yearly and monthly variations of power generation in terms of solar data and meteorological parameters. The total amount of power generation of the BIPV system measured from October 2014 to September 2015 was 64.607 MWh, and the simulation results with TRNSYS (Transient Systems Simulation Program) provided the 75.515 MWh predicted value of annual electricity production with the meteorological database of Meteonorm, while, based on the average value of the performance ratio (PR) of 60% and the life cycle assessment (LCA) of the system, the energy payback time (EPBT) of 9.38 years and the potential for pollutant emission reductions have been evaluated and the environmental cost is RMB ¥0.01053 per kWh. Finally, an economic analysis was carried out; the net present value (NPV) and the economic payback time of the BIPV system were estimated to be RMB ¥359,347 and 15 years, respectively.

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“…Liu et al posit that there is no obvious correlation between wind speed and PV output power [9]. Aste et al find that performance ratio for crystalline silicon PV modules is fairly constant in the face of changes in air mass [10]. Seasonal variations in weather affect the PV output power from month to month.…”

Section: Introductionmentioning

confidence: 99%

Future load profiles under scenarios of increasing renewable generation and electric transport

Allison

Akakabota

Pillai

2018

2018 5th International Conference on Renewable Energy: Generation and Applications (ICREGA)

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Abstract-Load profiles are indispensable in the decision making process of power transmission and distribution companies. Increasing levels of customer-side renewable generation and electric transport will alter the nature of load profiles significantly. Traditional methods relying on historical data will not be suitable for modelling the increasingly complex power networks of the future. In this paper the feasibility of synthesising future load profiles under increasing levels of photovoltaic (PV) generation and electric vehicles (EV) is investigated using an artificial neural network (ANN) based method, trained with publically available data. The performance of the proposed method is evaluated by using a case study developed for a targeted region in the UK. A comparison of results from the ANN model against those using Multiple Linear Regression (MLR) demonstrates the superior performance of ANN over MLR as well as proves the viability of ANN to synthesise future load profiles.

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PV technologies performance comparison in temperate climates

Cited by 64 publications

References 27 publications

Performance Analysis and Comparison of Different Photovoltaic Modules Technologies under Different Climatic Conditions in Casablanca

Performance Analysis and Comparison of Different Photovoltaic Modules Technologies under Different Climatic Conditions in Casablanca

Grid-Connected Semitransparent Building-Integrated Photovoltaic System: The Comprehensive Case Study of the 120 kWp Plant in Kunming, China

Future load profiles under scenarios of increasing renewable generation and electric transport

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