Angular loss of window integrated thin film semi-transparent photovoltaic module

Barman, Sankar; Chowdhury, Amartya; Mathur, Sanjay; Mathur, Jyotirmay

doi:10.1016/j.jobe.2021.102353

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…The effective radiation is determined taking into account the angular loss (Barman et al, 2021) and spectral (Martin and Ruiz, 2001) with p-Si module and moderate dust level at DT 0.97 for installation.…”

Section: Ec Modeling and Forecasting Research Irradiationmentioning

confidence: 99%

Solar Prediction Strategy for Managing Virtual Power Stations

Cahyadi,

Suwarno,

Dewi

et al. 2023

IJEEP

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The potential for solar power is available in Indonesia because it is located on the equator, with good sunshine all year round. The Indonesian government is currently actively developing a solar power plant while still looking at the consequences of development, especially on the surrounding environment. It is necessary to pay attention so that it does not disturb the surrounding environment, which can also cause climate change. The city of Medan is one of the largest cities in Indonesia, which has direct exposure to sunlight which is quite promising for predicting solar power plants in the future. Solar energy generation in the last decade has continued to improve and develop in solar power predictions in a short period. Integration of solar power sources without accurate power predictions can hinder network operations and the use of renewable generation sources. To solve this problem, virtual power plant modeling can solve as a solution that manages to minimize the prediction error. This research studies methods that can efficiently generate significant daily Photovoltaic (PV) predictions at the locations studied using available data from the Meteorology, Climatology, and Geophysics Agency (MCGA). The approach of the two models based on RMSE (root mean square error) and MAE (Mean Absolute Error), can be virtual power plant modeling can solve it as a management solution to be minimal in its prediction error. This research studies methods that can efficiently generate significant daily Photovoltaic (PV) predictions at the locations studied using available data from the Meteorology, Climatology, and Geophysics Agency (MCGA). The approach of the two models based on RMSE (root mean square error) and MAE (Mean Absolute Error), can be virtual power plant modeling can solve it as a management solution to be minimal in its prediction error. This research studies methods that can efficiently generate significant daily Photovoltaic (PV) predictions at the locations studied using available data from the Meteorology, Climatology, and Geophysics Agency (MCGA). The approach of the two models based on RMSE (root mean square error) and MAE (Mean Absolute Error), can be taking into account the uncertainty of predictions that can provide additional information from virtual power plants. Verified prediction strategy performance against PV module power output and a set based on geographic meteorological station data have been used to simulate Virtual Power Plants (VPP). The power forecasting prediction refers to the LSTM (Long Short-Term Memory) network and gives an error close to that of other learning methods, based on the RMS characteristic of 4.19 W/m2 under lead time with different launch times. The application of the VPP model can reduce the global error by about 12.37% with the model RMSE, and shows great potential.

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Section: Ec Modeling and Forecasting Research Irradiationmentioning

confidence: 99%

Solar Prediction Strategy for Managing Virtual Power Stations

Cahyadi,

Suwarno,

Dewi

et al. 2023

IJEEP

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A systematic review of research methods and economic feasibility of photovoltaic integrated shading device

Chen,

Qiu,

Wang

2024

Energy and Buildings

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Energy Savings Potential of Semitransparent Photovoltaic Skylights under Different Climate Conditions in China

et al. 2022

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Due to the limited available envelope area, height-constrained buildings integrated with photovoltaics require that more attention be given to the effective use of roofs. Thus, it is crucial to study the energy savings potential of previously neglected semitransparent photovoltaic (STPV) skylights. In this paper, the net energy consumption (NEC) of a room with STPV skylights and energy superiority compared to a reference window were investigated. The energy savings potential was then calculated for five representative cases located in different climate zones and daylight zones, according to the mandatory codes to be implemented in April 2022. Through a global sensitivity analysis, the extent to which each component of the NEC affects the energy savings potential was evaluated. The results indicate that STPV skylights exhibit promising energy savings potential in China. In temperate zones with excellent daylight conditions, an energy savings potential of 0.21 to 2.55 can be achieved, while the maximum energy savings rate (ESR) for the other four cases ranges from 0.52 to 1.1. The effect of electricity power generation (EPG) on the energy savings potential is most pronounced, except for that of STPV skylights on sloped roofs in hot summer and cold winter zones with poor daylight.

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Angular loss of window integrated thin film semi-transparent photovoltaic module

Cited by 3 publications

References 34 publications

Solar Prediction Strategy for Managing Virtual Power Stations

Solar Prediction Strategy for Managing Virtual Power Stations

A systematic review of research methods and economic feasibility of photovoltaic integrated shading device

Energy Savings Potential of Semitransparent Photovoltaic Skylights under Different Climate Conditions in China

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