Abstract
Solar resource data derived from satellite imagery are widely available nowadays, either as an open-source or paid database. This article is intended to assess open-source databases, which cover the region of Indonesia. Here, four known solar resource databases, which spatially cover the Indonesian archipelago, have been used, namely, Prediction of Worldwide Energy Resource (POWER), Surface Solar Radiation–Heliosat-East (SARAH-E), CM SAF Cloud, Albedo, Radiation edition 2 (CLARA-A2), and SolarGIS. In addition, a minor portion of the Meteonorm database by Meteotest, around five sample points across Indonesia, has been assessed in terms of coherency to the four mentioned databases. Correlation coefficient and relative bias of the multiyear monthly mean annual cycle global horizontal irradiation (GHI) between pairs of databases are inspected. Three out of four databases are then validated through the available irradiation ground measurement data provided by the World Radiation Data Centre (WRDC). The correlation between each pair varies mostly between 0.7 and 1, which shows that the four databases to a certain extent agree on how the intermonthly variation would behave throughout the year. On the other hand, the validation result reveals that the three databases, i.e., POWER, CLARA-A2, and SARAH-E, are suffering from positive bias error ranging from 3% to 7%. Despite that fact, the correlation between measured and estimated values is still acceptable with SARAH-E showing the best performance among the three. Careful selections and adjustment enable the possibility of these databases to be utilized as a tool for depicting interannual and intermonthly variations of solar irradiation throughout the Indonesian archipelago.
Highlights
In the modern energy society, photovoltaic (PV) has emerged as one of the leading technologies contributing more than 20% of the worldwide total installed renewable energy power plant by the end of 2018
Each database only differs in terms of satellite selection as images producer and surface irradiation derivation algorithm. e basic derivation algorithm usually starts with predicting the total amount of solar radiation that would reach the Earth’s surface if the sky is clear. e prediction applies some physics modeling that normally requires the estimated value of solar radiation on the top of the atmosphere and some atmospheric parameters such as aerosols optical depth (AOD), precipitable water vapor (PWV), and others [15]
Databases Validation against World Radiation Data Centre (WRDC) Stations. e validations presented in Table 3 are only conducted on a database, which provides free access to at least a daily basis historical dataset, i.e., Prediction of Worldwide Energy Resource (POWER), CLARA-A2, and Solar Radiation–Heliosat-East (SARAH-E)
Summary
In the modern energy society, photovoltaic (PV) has emerged as one of the leading technologies contributing more than 20% of the worldwide total installed renewable energy power plant by the end of 2018. Around 57% of this PV capacity was installed within the region of Asia where China, Japan, and India are the three top contributors. Indonesia is only able to contribute for less than 0.03% (around 60 MW) of the global total installed PV capacity [1]. For the last 10 years, PV installed capacity annual growth in Indonesia only counts at 5.2 MW on average. Is rate is the fourth slowest growth among Southeast Asia countries despite being the largest economic power within the region. Other issues are frequently addressed regarding this slow development such as ineffective support regulation, opaque framework, low IRR for the developer, bank reluctancy on financing solar developers, low electricity tariff, and many more [2, 3]
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