Predicting Solar Radiation for Tropical Islands From Rainfall Data

Sendanayake, S.; Miguntanna, Nandika. P.; Jayasinghe, Mtr

doi:10.4090/juee.2015.v9n2.109-118

Cited by 4 publications

(3 citation statements)

References 44 publications

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“…There is high uncertainty in the atmospheric inputs available in Southern Asia (CAMS AOD and INSAT-3D COT) [75]. The correlation between the INSIOS GHI and the SIS irradiance was not so good as compared to the bibliography as we are dealing with the extreme conditions of monsoon [75][76][77]. The relevant studies that have better results were located mainly in Europe, where the climate is much more stable with no extreme atmospheric phenomena [31,53,59,65,78].…”

Section: Cloud and Shadow Effectmentioning

confidence: 99%

Rooftop Photovoltaic Energy Production Management in India Using Earth-Observation Data and Modeling Techniques

et al. 2020

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This study estimates the photovoltaic (PV) energy production from the rooftop solar plant of the National Institute of Technology Karnataka (NITK) and the impact of clouds and aerosols on the PV energy production based on earth observation (EO)-related techniques and solar resource modeling. The post-processed satellite remote sensing observations from the INSAT-3D have been used in combination with Copernicus Atmosphere Monitoring Service (CAMS) 1-day forecasts to perform the Indian Solar Irradiance Operational System (INSIOS) simulations. NITK experiences cloudy conditions for a major part of the year that attenuates the solar irradiance available for PV energy production and the aerosols cause performance issues in the PV installations and maintenance. The proposed methodology employs cloud optical thickness (COT) and aerosol optical depth (AOD) to perform the INSIOS simulations and quantify the impact of clouds and aerosols on solar energy potential, quarter-hourly monitoring, forecasting energy production and financial analysis. The irradiance forecast accuracy was evaluated for 15 min, monthly, and seasonal time horizons, and the correlation was found to be 0.82 with most of the percentage difference within 25% for clear-sky conditions. For cloudy conditions, 27% of cases were found to be within ±50% difference of the percentage difference between the INSIOS and silicon irradiance sensor (SIS) irradiance and it was 60% for clear-sky conditions. The proposed methodology is operationally ready and is able to support the rooftop PV energy production management by providing solar irradiance simulations and realistic energy production estimations.

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Section: Cloud and Shadow Effectmentioning

confidence: 99%

Rooftop Photovoltaic Energy Production Management in India Using Earth-Observation Data and Modeling Techniques

et al. 2020

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“…This paper also considered weather condition, which includes clear, partly cloudy, cloudy, overcast, mist, lightly patchy rain, moderate patchy rain, heavy patchy rain, light rain, moderate rain, rain shower, heavy rain, patchy storm, and heavy storm [26]. A previous study [32] predicted solar radiation from an established relationship between the monthly average daily global radiation and the mean number of rainy days. In this previous study [32], the clearness index, which is calculated using sky transmittance of clear days and sky transmittance of overcast days, uses the rainfall data to predict solar radiation.…”

Section: Identifying Significant Factors Affecting Solar Irradiancementioning

confidence: 99%

“…A previous study [32] predicted solar radiation from an established relationship between the monthly average daily global radiation and the mean number of rainy days. In this previous study [32], the clearness index, which is calculated using sky transmittance of clear days and sky transmittance of overcast days, uses the rainfall data to predict solar radiation. The mean daily values of the sky transmittance of clear days and mean daily values of the sky transmittance of overcast days are calculated by integrating and averaging ( 6) and (7), respectively, over the day [32].…”

Section: Identifying Significant Factors Affecting Solar Irradiancementioning

confidence: 99%

Solar Photovoltaic Power Prediction Using Big Data Tools

Arias

Bae

2021

Sustainability

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Solar photovoltaic (PV) installation has been continually growing to be utilized in a grid-connected or stand-alone network. However, since the generation of solar PV power is highly variable because of different factors, its accurate forecasting is critical for a reliable integration to the grid and for supplying the load in a stand-alone network. This paper presents a prediction model for calculating solar PV power based on historical data, such as solar PV data, solar irradiance, and weather data, which are stored, managed, and processed using big data tools. The considered variables in calculating the solar PV power include solar irradiance, efficiency of the PV system, and characteristics of the PV system. The solar PV power profiles for each day of January, which is a summer season, were presented to show the variability of the solar PV power in numerical examples. The simulation results show relatively accurate forecasting with 17.57 kW and 2.80% as the best root mean square error and mean relative error, respectively. Thus, the proposed solar PV power prediction model can help power system engineers in generation planning for a grid-connected or stand-alone solar PV system.

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Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas

et al. 2020

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Solar radiation ground data is available in poor spatial resolution, which provides an opportunity and demonstrates the necessity to consider solar irradiance modeling based on satellite data. For the first time, solar energy monitoring in near real-time has been performed for India. This study focused on the assessment of solar irradiance from the Indian Solar Irradiance Operational System (INSIOS) using operational cloud and aerosol data from INSAT-3D and Copernicus Atmosphere Monitoring Service (CAMS)-Monitoring Atmospheric Composition Climate (MACC), respectively. Simulations of the global horizontal irradiance (GHI) and direct normal irradiance (DNI) were evaluated for 1 year for India at four Baseline Surface Radiation Network (BSRN) stations located in urban regions. The INSIOS system outputs as per radiative transfer model results presented high accuracy under clear-sky and cloudy conditions for GHI and DNI. DNI was very sensitive to the presence of cloud and aerosols, where even with small optical depths the DNI became zero, and thus it affected the accuracy of simulations under realistic atmospheric conditions. The median BSRN and INSIOS difference was found to vary from −93 to −49 W/m2 for GHI and −103 to −76 W/m2 for DNI under high solar energy potential conditions. Clouds were able to cause an underestimation of 40%, whereas for various aerosol inputs to the model, the overall accuracy was high for both irradiances, with the coefficient of determination being 0.99, whereas the penetration of photovoltaic installation, which exploits GHI, into urban environments (e.g., rooftop) could be effectively supported by the presented methodology, as estimations were reliable during high solar energy potential conditions. The results showed substantially high errors for monsoon season due to increase in cloud coverage that was not well-predicted at satellite and model resolutions.

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Predicting Solar Radiation for Tropical Islands From Rainfall Data

Cited by 4 publications

References 44 publications

Rooftop Photovoltaic Energy Production Management in India Using Earth-Observation Data and Modeling Techniques

Rooftop Photovoltaic Energy Production Management in India Using Earth-Observation Data and Modeling Techniques

Solar Photovoltaic Power Prediction Using Big Data Tools

Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas

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