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.
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. Meanwhile, Indonesia is only able to contribute for less than 0.03% (around 60 MW) of the global total installed PV capacity [
Nevertheless, the fact that the solar PV project requires high initial investment cost remains. Employing reliable solar resource data with a long-term historical database enables a more detailed solar resource analysis such as inquiring how solar resources on potential power plants would behave throughout the investment years. This kind of analysis will help investors to understand more about the uncertainty involved in solar project investment. Likewise, the developer would also be able to refine this analysis into a P50/P90 uncertainty report, which is required to obtain funding from the investors. The existence of reliable solar resource data without a doubt is an important aspect of stimulating the growth of the solar PV project within a region.
Several studies have been conducted to estimate local solar irradiation in some regions in Indonesia via ground measurements [
Large area coverage of satellite images makes them superior in acquiring data over certain regions instantaneously, while the ground-based sensor only acquires data within a perimeter of the sensor’s location. Moreover, the captured images are well-archived, which enables the possibility of building a proper solar resources database from archived historical images. Due to these pronounced advantages, most of the solar resources databases that exist today incorporate satellite images into their algorithms either as a primary or secondary input variable.
Unlike developed countries who put some interest in developing their solar resource database and provide the service for free, Indonesia currently is only able to rely on the data that have been provided by the third party. As a support to the development of renewable energy worldwide, some research institutions funded by developed countries have been providing worldwide open-source solar resource databases service for quite sometimes, while some private institutions charge some fees for the solar resource data services. Despite having their limitations, such as temporal and spatial coverage ranges and resolutions, the open-source databases are more preferable for solar PV players in Indonesia since they do not incur any additional cost.
The aforementioned limitations of several open-source solar resources databases will be thoroughly discussed within this article, and finally, a common ground map where all open-source solar resources databases agree on several statistical measures limit is drawn out. Hopefully, the map can act as a guide for solar PV players in Indonesia to utilize the open-source databases and adjust them appropriately to any specific needs.
As mentioned previously, open-source solar databases addressed within this article are all satellite-derived. In general, each database only differs in terms of satellite selection as images producer and surface irradiation derivation algorithm. The 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 (commonly known as clear-sky irradiation). The prediction applies some physics modeling that normally requires the estimated value of solar radiation on the top of the atmosphere (known as TOA irradiation) and some atmospheric parameters such as aerosols optical depth (AOD), precipitable water vapor (PWV), and others [
Upon predicting clear-sky irradiation, the algorithm continues by deciding the inclusion of additional effects caused by cloud conditions. The cloud condition is often described by several cloud properties retrieved from satellite images. The cloud properties retrieval algorithm inspects each pixel (normally in form of a square) of the image and determines the most appropriate cloud properties for that particular pixel. The number of pixels within an image determines the level of detail on the captured 2D space that can be derived or simply known as spatial resolution. While the number of captured images over a period of time determines the level of 1D time detail that can be inferred from a group of images or often known as temporal resolution. The quality of the solar resources databases also depends on these resolution values, higher resolutions mean closer the database to resemble continuous behavior of true irradiation values.
The first solar resource database is Prediction of Worldwide Energy Resource (POWER) developed by NASA Langley Research Centre. This database is an outgrowth of the previously mentioned SSE database with a similar spatial resolution. Unlike most solar resources databases, POWER historical data are built from 3 different sources for each respective period, GEWEX SRB 3.0 (1983–2007), FLASHFlux v2 (2008–2012), and FLASHFlux v3 (2013-present) [
The second database is Surface Solar Radiation–Heliosat-East (SARAH-E) provided by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) [
The third database is Cloud, Albedo, and Surface Radiation edition 2 (CLARA-A2), also developed by CM SAF [
The fourth solar resources database is provided by SolarGIS, which comes with paid and free versions. An open-source version of the database is presented in form of high spatial resolution local solar resource map product assisting the request of the World Bank’s Energy Sector Management Assistance Program (ESMAP) as the funding source [
SolarGIS long-term mean surface radiation in Indonesia.
The general summary of open-source satellite-derived solar resource database for Indonesia.
SARAH-E | SolarGIS | |
---|---|---|
Spatial resolution | 0.05 | 0.0025 |
Temporal resolution | Hourly | Monthly average (free edition) |
Spatial coverage | 40 S-62 30′ N, 65 W-128 E | Global |
Temporal coverage | 1999–2016 | 1999–2016/2007–2016 (average) |
Derivation algorithm | MAGICSOL | SolarGIS algorithm |
Satellite | METEOSAT by EUMETSAT | METEOSAT, GOES, and Himawari |
Database access | https://wui.cmsaf.eu/ | https://solargis.com/maps-and-gis-data/download/indonesia |
POWER | CLARA-A2 | |
Spatial resolution | 1 | 0.25 |
Temporal resolution | Hourly | Daily |
Spatial coverage | Global | Global |
Temporal coverage | 1983–present | 1982–2015 |
Derivation algorithm | LPSA, CERES-2 cloud property retrieval MAGIC, and SAFNWC | |
Satellite | Aqua and terra by NASA | POES by NOAA and EUMETSAT |
Database access | https://power.larc.nasa.gov/ | https://wui.cmsaf.eu/ |
Nowadays, the initial solar resource database is usually included within common PV design software, e.g., PVsyst with Meteonorm and HOMER with NASA-SSE. Since many Indonesian solar developers are already familiar with both software types, the coupled solar resource databases within will be assessed in terms of coherency with the four aforementioned satellite-derived databases; NASA-SSE is excluded since it has been represented by POWER database. Similar to SolarGIS, Meteonorm 7.2 brought by Meteotest also offers free and paid versions. The open-source version covers a multiyear monthly mean annual cycle irradiation dataset, limited at a certain maximum number of sites. The algorithm of Meteonorm is a mixed version of ground measurement data interpolation and satellite-derived process, which roughly follows this rule [ If the closest station is within 30 km (for Europe 10 km) radius, the data will be either similar to the station’s measured data or derived from the interpolation of nearby stations If the closest station is within a radius of 30–300 km, the data will be a mixture of interpolation and satellite-derived data If there is no station within a 300 km radius, the data will be purely derived from a satellite
To stand in equal terms with the other databases in this article, only data generated from the last term of the aforementioned rule is considered. So far, Meteonorm Satellite Irradiation (MNSI) has only been validated for Europe, Africa, and Middle East regions whose result has found that rRMSE of hourly values lies around 12–25% [
Besides comparing, the article also intends to check these databases locally whose result might be able to reveal some specific characteristics that the user needs to be aware of. The local validation process of the solar resource database requires a reliable ground measurement dataset, and in this article, daily sum global irradiation data provided by World Radiation Data Centre (WRDC) network stations [
Summary of the WRDC station network used for validation.
Measurement stations | Period | Coordinates | Elevation (m) |
---|---|---|---|
Brunei Airport–Brunei Darussalam | 1987–1995 | 4 56′N, 114 56′E | 22 |
Christmas Island–Australia | 1996–1997 | 10 27′S, 105 41′E | 262 |
Changi Airport–Singapore | 1984–2004 | 1 22′N, 103 59′E | 15 |
Bayan Lepas–Malaysia | 1984–2009 | 5 18′N, 100 16′E | 3 |
Pengkalan Chepa–Malaysia | 1984–2009 | 6 10′N, 102 17′E | 5 |
Subang–Malaysia | 1984–2009 | 3 07′N, 101 33′E | 17 |
Kuching–Malaysia | 1989–2009 | 1 29′N, 110 21′E | 20 |
Kota Kinabalu–Malaysia | 1989–2007 | 5 56′N, 116 03′E | 2 |
Bukit Kototabang–Indonesia | 1996–2017 | 0 12′S, 100 19′E | 864 |
Variations of global horizontal irradiation (GHI) value has been proven able to represent the energy yield variations of the flat PV system [
The first thing that has to be verified is the GHI annual intermonth trend of each database, which is a cycle formed by a monthly average of irradiance data within a year. The trend similarity of two databases is estimated by employing Pearson correlation coefficient (
Another statistical measure that has been employed is the mean bias (
At the last step, both computed
The validations presented in Table
Daily energy resource validation result of POWER, CLARA-A2, and SARAH-E against data retrieved from WRDC network stations.
Number of pairs | AVG (kWh/m2) | RMSE (kWh/m2) and rRMSE | MBE (kWh/m2) and rMBE | ||
---|---|---|---|---|---|
POWER | |||||
Brunei Airport | 2658 | 5.00 | 1.20 (24.0%) | 0.44 (8.7%) | 0.56 |
Christmas Island | 485 | 4.74 | 1.50 (31.6%) | 1.77 (37.4%) | 0.85 |
Changi Airport | 7366 | 4.55 | 1.04 (22.8%) | 0.03 (0.6%) | 0.72 |
Bayan Lepas | 8917 | 4.96 | 0.90 (18.2%) | 0.19 (3.8%) | 0.80 |
Pengkalan Chepa | 8307 | 4.95 | 1.01 (20.3%) | 0.34 (6.9%) | 0.80 |
Subang | 8577 | 4.38 | 0.99 (22.6%) | 0.53 (12.0%) | 0.70 |
Kuching | 7059 | 4.17 | 1.06 (25.5%) | 0.50 (11.9%) | 0.69 |
Kota Kinabalu | 6646 | 5.02 | 0.99 (19.7%) | 0.09 (1.8%) | 0.66 |
Bukit Kototabang | 7107 | 4.35 | 0.89 (20.5%) | 0.40 (9.2%) | 0.73 |
CLARA-A2 | |||||
Brunei Airport | 1655 | 5.03 | 1.61 (32.1%) | 0.42 (8.3%) | 0.41 |
Christmas Island | 319 | 4.81 | 1.41 (29.3%) | 2.30 (47.9%) | 0.84 |
Changi Airport | 5183 | 4.53 | 1.23 (27.3%) | 0.13 (2.8%) | 0.72 |
Bayan Lepas | 6969 | 4.95 | 1.30 (26.3%) | −0.20 (−4.1%) | 0.65 |
Pengkalan Chepa | 6572 | 4.94 | 1.16 (23.6%) | 0.57 (11.6%) | 0.78 |
Subang | 6456 | 4.40 | 1.29 (29.4%) | −0.02 (−0.4%) | 0.60 |
Kuching | 5710 | 4.17 | 1.35 (32.4%) | 0.43 (10.3%) | 0.58 |
Kota Kinabalu | 5381 | 5.00 | 1.39 (27.8%) | −0.42 (−8.5%) | 0.57 |
Bukit Kototabang | 6197 | 4.38 | 1.08 (24.6%) | 0.32 (7.3%) | 0.65 |
SARAH-E | |||||
Brunei Airport | x | x | X | x | x |
Christmas Island | x | x | X | x | x |
Changi Airport | 2045 | 4.49 | 0.62 (13.7%) | 0.26 (5.9%) | 0.92 |
Bayan Lepas | 3652 | 4.95 | 0.60 (12.1%) | 0.34 (6.8%) | 0.92 |
Pengkalan Chepa | 3427 | 5.02 | 0.53 (10.6%) | 0.12 (2.4%) | 0.95 |
Subang | 3154 | 4.44 | 0.74 (16.6%) | 0.44 (10.0%) | 0.86 |
Kuching | 3607 | 4.16 | 1.06 (25.4%) | 0.51 (12.3%) | 0.70 |
Kota Kinabalu | 3129 | 5.05 | 0.97 (19.3%) | 0.19 (3.7%) | 0.72 |
Bukit Kototabang | 6010 | 4.39 | 0.70 (16.0%) | 0.27 (6.2%) | 0.84 |
According to the result, it is no doubt that SARAH-E outperforms the other two databases especially on RMSE and time series correlation measures. However, there is a concern regarding sudden performance drops on 2 validation sites, namely Kuching and Kota Kinabalu. Similarly, POWER and CLARA-A2 also suffer from these performance reductions, but somehow, the drops are not as pronounced as the one on SARAH-E. The two stations are located at the Malay side of Kalimantan, close to Indonesian regions where the majority of databases do not agree on how the GHI annual intermonth trend should be. Therefore, the performance drop could also be triggered by low/no-temporal variation dataset as elaborated on the next section or transnational forest fire pollution, which has been proven to be one of the causes of air quality degradation in Malaysia [
As implicitly stated by the previous equation, the correlation coefficient explains the relation of two variables that move together in parallel, or in this case, the two GHI databases intermonth variations. Two representative results, POWER vs SolarGIS and CLARA-A2 vs SolarGIS, presented in Figure
Representative figure of correlation coefficient between two databases. (a) POWER vs SolarGIS. (b) CLARA-A2 vs SolarGIS.
Looking closely at the distribution of low/no correlation regions, there are two possibilities that might be able to explain this situation. The first cause should be related to the low/no temporal variation cells within the dataset; without temporal variation, the correlation itself would be minimum or even do not exist since the covariance between pair of databases should also be minimum. The maps of CoVT in Figure
CoVT on the 1999–2016 period. (a) CLARA-A2. (b) POWER. (c) SolarGIS.
The second most probable cause is the disturbance of aerosol temporal trend within the atmosphere, which is most likely related to a natural or man-made forest fire that occurs frequently during the dry season. Citing a summary data released by Indonesian Ministry of Living Environment and Forestry (MENLHK) within the 2014–2016 period, Sumatera, Kalimantan, and Papua were the three top regions suffered from forest fire phenomenon, with each contributing 38%, 33%, and 17% of total around 3 million hectares of the burnt area across the country [
CM SAF surface radiation products have been validated on several ground measurement sites around the world; among those sites, there are some cases where the local climate is prone to aerosol load changes such as China [
The spatial distribution of
GHI mean bias between two databases. (
As depicted by the first three box plots, the POWER database overvalues other databases by about 0.5–0.22 kWh/m2/day on average. More than 90%, 60%, and 55% of POWER data estimations overvalue estimated GHI of SolarGIS, SARAH-E, and CLARA-A2, respectively. Meanwhile, when CLARA-A2 is appointed as reference (box plot 4 and 5), more than 95% and 75% of its data estimations overvalue SolarGIS and SARAH-E by 0.22 and 0.14 kWh/m2/day, respectively. The last box plot reveals that around 70% of SARAH-E data estimations overvalue SolarGIS by 0.09 kWh/m2/day. Sorting the result in the ascending order will lead to following rank configuration, SolarGIS < SARAH-E < CLARA-A2 < POWER, which overall explains the relative position of each database in term of estimated GHI magnitude.
The loss of spatial variations during the downscaling process is recorded and presented in Figure
Spatial CoV lost due to downscaling. (
In addition, the spatial CoV loss of two representative figures that have undergone the strongest downscaling (i.e., SolarGIS to CLARA-A2 and SolarGIS to POWER) is presented on Figure
The spatial CoV loss figure due to downscaling. (a) SolarGIS to CLARA-A2. (b) SolarGIS to POWER.
By putting some constraints over
The common ground region between databases. (a) POWER, CLARA-A2, SARAH-E, and SolarGIS. (b) CLARA-A2, SARAH-E, and SolarGIS. (c) SARAH-E and SolarGIS.
According to the maps, it is clear that the main highlights of “not agree” regions are highly influenced by previously discussed GHI annual intermonth trend disagreement around Kalimantan, Sumatera, and Papua. Since the GHI annual intermonth trend does not seem to be the cause of some “not agree” pixels on Sulawesi and Maluku region as shown in Figure
To assess Meteonorm, five sample points across Indonesia were randomly selected. The points have been arranged to be positioned within regions that satisfy “agree” requirements on the three aforementioned common ground maps. The points also have been filtered to only consist of a satellite-derived dataset (no interpolation involved). The assessment is conducted by simply checking
Correlation coefficient and mean bias of Meteonorm relative to other satellite-derived databases.
Locations | Correlation coefficient | Mean bias (kWh/m2) | ||||||
---|---|---|---|---|---|---|---|---|
POWER | CLARA-A2 | SARAH-E | SolarGIS | POWER | CLARA-A2 | SARAH-E | SolarGIS | |
Bali (8.39 S, 115.56 E) | 0.62 | 0.80 | 0.77 | 0.76 | −0.01 | 0.11 | −0.46 | −0.58 |
Java (7.39 S, 111.95 E) | 0.74 | 0.80 | 0.71 | 0.72 | 0.30 | 0.23 | 0.42 | 0.24 |
Kalimantan (2.47 N, 117.41 E) | 0.87 | 0.90 | 0.81 | 0.84 | 0.41 | 0.31 | 0.30 | 0.07 |
Sulawesi (2.42 S, 120.97 E) | 0.62 | 0.72 | 0.73 | 0.71 | −0.07 | −0.28 | −0.09 | −0.16 |
Sumatera (4.80 N, 97.63 E) | 0.90 | 0.87 | 0.95 | 0.95 | 0.14 | 0.24 | 0.44 | 0.12 |
In this article, four satellite-derived databases, i.e., POWER, SARAH-E, CLARA-A2, and SolarGIS, which cover the region of Indonesia have been assessed in terms of data correlation and mean bias relatively between each other. Most database pairs are showing a strong correlation, which implies they follow roughly similar GHI annual intermonth trend. Meanwhile, some regions such as Central Kalimantan, Riau, South Sumatera, and Papua are exhibiting weak/no correlation, which is suspected to be triggered by the existence of cells with low/no temporal variation within the inspected databases and the absence of aerosol temporal trends due to the forest fire phenomenon. Spatial distributions of mean bias values reveal the relative position of each database in terms of estimated GHI magnitude, which on average follows this order: SolarGIS < SARAH-E < CLARA-A2 < POWER. Both computed correlation coefficient and relative bias are employed as agreement constraints to depict spatial common grounds for the four databases. The majority of “not agree” regions on presented common ground maps are influenced by the GHI annual intermonth trend disagreement between databases. While some minor “not agree” regions triggered by mean bias disagreement are believed to be the consequence of METEOSAT-East’s parallax effect.
In addition, a similar assessment was conducted on Meteonorm satellite-derived dataset relative to the four databases in five selected sample points, Bali, Java, Kalimantan, Sulawesi, and Papua. It reveals that none of these sample points passes the minimum requirement of being “agree” pixel, while the four databases do. Three databases, i.e., POWER, CLARA-A2, and SARAH-E are validated against WRDC stations located in Indonesia and surrounding nations by employing three statistical measures, namely RMSE, MBE, and correlation coefficient. According to the result, SARAH-E outperforms the other two databases especially on RMSE and time series correlation measures, while CLARA-A2 is still behind POWER on three statistical measures. The result also reveals that the three satellite-derived databases tend to overestimate their GHI value by about 0.07–0.32 kWh/m2/day (3–7% rMBE) on average compared to the true measured value.
The standard deviation of db1
The standard deviation of db2
Mean bias between one database relative to another
Coefficient of variation
Temporal coefficient of variation
Covariance
The GHI reference data
The GHI estimated data
Estimated solar energy resources
GHI value for each pixel
Multiyear average of typical daily GHI value for each month
Mean of multiyear average of typical daily GHI value for each month in a year
Measured solar energy resources
Mean GHI value of pixels before downscaling
Mean bias error
Amount of data
Root mean square error
Pearson correlation coefficient
Relative mean bias error
Relative root mean square error.
The data used to support this study are obtained from NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program. The work performed was done (i.a.) by using SARAH-E and CLARA-A2 data from EUMETSAT’s Satellite Application Facility on Climate Monitoring (CM SAF), copyright (2019) EUMETSAT. © 2019, The World Bank, Source: Global Solar Atlas 2.0, Solar resource data: Solargis.
The authors declare that there are no conflicts of interest regarding the publication of this paper.