SolarBench: A global solar energy nowcasting benchmark

Yuhao Nie; Stephen Campbell; Quentin Paletta; Liwenbo Zhang; Tao Jing; Samer Chaaraoui; Jonathan Giezendanner; Andea Scott

cs.CVSep 5, 2026

SolarBench: A global solar energy nowcasting benchmark

Authors: Yuhao Nie, Stephen Campbell, Quentin Paletta, Liwenbo Zhang, Tao Jing, Samer Chaaraoui, Jonathan Giezendanner, Andea Scott,

Organizations: Institute for Data, Systems, and Society, Massachusetts Institute of Technology, United States. · Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, United States. · Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, United States. · Φ-lab, ESRIN, European Space Agency, Italy. · Climate Office, ECSAT, European Space Agency, United Kingdom. · Department of Architecture and Built Environment, University of Nottingham, United Kingdom. · Department of Mechanical Engineering, Hong Kong Polytechnic University, Hong Kong. · International Center for Sustainable Development, Hochschule Bonn-Rhein-Sieg University of Applied Sciences, Germany. · Department of Energy Science and Engineering, Stanford University, United States. · Department of Civil and Environmental Engineering, Stanford University, United States. · Department of Mechanical Engineering, The University of Texas at Dallas, United States. · Mines Paris, Universit´e PSL, France. · Independent Researcher, United States. · Department of Civil and Mechanical Engineering, Technical University of Denmark, Denmark. · OFFIS - Institute for Information Technology, Germany. · Department of Mechanical Engineering, Massachusetts Institute of Technology, United States.

Abstract

As the share of solar power grows, nowcasting weather-driven solar variability becomes critical for reliable energy system operation. State-of-the-art approaches increasingly apply deep learning to sky camera and geostationary satellite observations, but fragmented datasets and inconsistent evaluation make it difficult to determine whether reported improvements generalize across climates, cloud regimes, and photovoltaic (PV) systems. Here we introduce SolarBench, an open global benchmark for image-based solar nowcasting. SolarBench harmonizes more than six million sky and satellite images from 11 diverse sites spanning a decade, together with irradiance or PV output and auxiliary atmospheric data. An accompanying toolbox supports reproducible data access, processing, model development, and evaluation. Using SolarBench, we benchmark representative models and reveal a gap between average forecasting accuracy and the ability to capture rapid solar fluctuations. We further quantify predictability across cloud regimes and demonstrate data-efficient adaptation to new PV systems. SolarBench provides an extensible foundation for fair comparison and methodological innovation in solar nowcasting.

Figures & tables

Figure 1: Conceptual framework of SolarBench . SolarBench provides a unified benchmark infrastructure for solar nowcasting that combines a curated multimodal database and an open machine learning toolbox for standardized data access, processing, model development, and evaluation. By combining a multi-continental harmonized database with reproducible benchmarking pipelines, SolarBench enables fair cross-site evaluation, reproducible research, and scalable development and cross-site adaptation of solar nowcasting systems.

Figure 2: Dataset characteristics . a , Geographic distribution of the 11 SolarBench sites overlaid on long-term mean global downward surface solar radiation from ERA5 for 1980–2020. b , Short-term solar variability across sites, characterized by distributions of absolute 5-min changes in clear sky index ( $|\Delta\mathrm{CSI}|$ ). Black diamonds indicate the root mean square of $|\Delta\mathrm{CSI}|$ , defined as the variability index, and percentages indicate the fraction of 5-min intervals with $|\Delta\mathrm{CSI}|>0.1$ , used here to identify large short-term solar fluctuations. c , Availability and temporal coverage of the three data categories across sites: ground-based observations, remote sensing data, and atmospheric model products, with irradiance or PV output indicated as the forecast target. Labels within the bars indicate the temporal resolution of the corresponding data.

Figure 3: Benchmarking of level and ramp prediction performance across lead times and locations. a , Aggregated forecasting performance. (i) Level prediction performance quantified by forecast skill (%), defined as the improvement in RMSE related to persistence (i.e., persistence has a forecast skill of zero). Solid lines denote the median across locations, and shaded bands denote the interquartile range. (ii) Ramp recall, defined as the fraction of observed ramp events successfully captured within a 30% relative error threshold, aggregated across sites in the same manner. b , Example irradiance forecasts from representative models at a 60 min lead time under relatively stable and highly variable conditions. The stable case highlights the competitiveness of persistence under limited variability, whereas the highly variable case shows that image-based deep learning models achieve better level prediction accuracy but produce smoother forecasts that miss many rapid irradiance transitions, revealing a gap between aggregate accuracy and ramp event detection. Observed and successfully predicted ramp events are highlighted. c , Site-level forecasting performance. (i) Best-performing model at each site and lead time for forecast skill and ramp recall. Each circle represents a site–lead time pair, and marker color denotes the best-performing model. (ii), Irradiance forecasting RMSE of the ViT model across sites and lead times. Locations are sorted by RMSE at the 10 min lead time.

Figure 4: Predictability across sites and cloud regimes . a , Relationship between forecasting error and persistence-based predictability across sites for 10-, 60-, and 120-min lead times. Persistence rRMSE is used as a proxy for forecasting difficulty, with higher values indicating lower predictability. Results are shown for representative models (MLP, OmniVision, and ViT), with Pearson correlation coefficients reported for each lead time. b , Persistence-based predictability across cloud regimes and lead times. Bar plots show persistence rRMSE across sites for Clear, Thin, Thick, and Patchy cloud regimes at 10-, 60-, and 120-min lead times. Solid horizontal lines indicate category median. Dashed lines in the Patchy panels denote the overall persistence error pattern across all cloud regimes, illustrating its close alignment with the cross-location error pattern under Patchy conditions. Representative sky image sequences for each cloud regime are shown above the first row.

Figure 5: Towards scalable deployment of solar forecasting systems. a , Scaling of local forecasting performance with increasing training data across sites with different predictability levels. Deep learning models generally require approximately six months of local observations to achieve break-even performance relative to persistence, while more stable forecast skill typically emerges after $\sim$ 12 months of training data. Sites with higher predictability generally require more local data before deep learning models outperform persistence. b , Transfer learning from geographically distributed irradiance forecasting datasets to two real-world PV systems: a 30 kW rooftop PV installation at Stanford University, USA, and a 3.275 MW hybrid PV system at the Gatton Solar Research Facility at the University of Queensland, Australia. Pre-training on geographically distributed irradiance data followed by local fine-tuning substantially reduces local PV data requirements while maintaining competitive forecasting performance.

Appendix figures & tables18 assets

Supplementary material from the paper’s appendix.

Appendix

Supplementary Figure 1: Example solar irradiance variability and corresponding cloud cover observations . Left: measured GHI over a representative day. Three time points (i–iii) representing distinct irradiance conditions are highlighted. Right: corresponding ground-based sky images (left column) and geostationary satellite images (right column). The red circle indicates the location of the sky imaging system within each satellite scene. These examples illustrate how cloud observations from ground-based sky cameras and geostationary satellites provide information about short-term variability in surface solar irradiance.

Supplementary Figure 2: Ground-based measurement device setup at the Akwatia site. The measurement system consists of (1) a GeoVision all-sky camera and (2) a Kipp & Zonen RT1 radiometer for GTI measurements. The inset shows a close-up view of the RT1 radiometer. The Kologo site uses the same measurement setup.

Supplementary Figure 3: Ground-based measurement device setup at the Sankt Augustin site. The system includes (1) a GeoVision all-sky camera, (2) a SOLYS2 sun tracker, (3) an SP Lite2 pyranometer for global horizontal irradiance (GHI) measurements, (4) a CMP11 pyranometer for global horizontal irradiance (GHI) measurements, (5) a GHP1 pyrheliometer for direct normal irradiance (DNI) measurements, and (6) a CMP11 pyranometer for diffuse horizontal irradiance (DHI) measurements. Instruments (3)–(6) are mounted on the SOLYS2 sun tracker. Enlarged views of the all-sky camera and the solar radiation measurement system are shown in the lower panels.

Supplementary Figure 4: Ground-based measurement device setup at the Hong Kong site. The system includes (1) a Vivotek FE9380-HV network camera for capturing sky images, (2) a Delta-T SPN1 pyranometer measuring GHI and DHI, from which DNI is calculated, and (3) a Lufft WS500 weather station providing measurements of air temperature, relative humidity, atmospheric pressure, wind speed, and wind direction.

Supplementary Figure 5: Ground-based measurement device setup at the Nottingham site. Panel (a) shows the rooftop site at the University of Nottingham’s Jubilee Campus and the locations of Base A and Base B, enlarged in panels (b) and (c). The system includes (1) a Mobotix Q26B fisheye camera, (2) a Kipp & Zonen RaZON+ sun tracker with a PR1 pyranometer for DHI and a PH1 pyrheliometer for DNI, from which GHI is derived, (3) a CMP10 pyranometer for GHI cross-validation, and (4) a Gill Instruments GMX600 weather station.

Supplementary Figure 6: Relationship between forecasting error and the variability index. rRMSE of the MLP, OmniVision, and ViT models is plotted against the variability index for the 10-, 60-, and 120-min lead times. Each point represents one SolarBench site, and solid lines indicate least-squares linear fits. Pearson correlation coefficients between rRMSE and the variability index are reported for each model. The variability index is computed using a fixed 5-min interval following Stein et al. [ 47 ] for all forecast lead times. The weak and inconsistent correlations across models and lead times indicate that this aggregate variability metric alone does not adequately characterize forecasting difficulty.

Supplementary Figure 7: Distribution of cloud regimes across SolarBench sites. Stacked bar charts showing the fraction of samples assigned to the four cloud regimes (Clear, Thin, Patchy, and Thick) at each site. Percentages denote the proportion of samples in each regime, highlighting substantial differences in cloud regime composition across the benchmark locations.

Supplementary Figure 8: Representative cloud regime examples across SolarBench sites. Sky image sequences illustrating clear, thin cloud, patchy cloud, and thick cloud conditions at Puy de Dôme, Akwatia, Golden, and Nottingham. Each example shows a 15-min historical sequence at 5-min intervals ( $t-15$ , $t-10$ , $t-5$ , and $t$ ), highlighting cross-site differences in the visual appearance of each cloud regime.

Site	Dataset Name (on Hugging Face)	Lat., Lon. ( ∘ )	Köppen–Geiger Climate Class	Data Period	Duration (years)
Akwatia, Ghana	EnerSHelF-Akwatia	6.047, -0.797	Aw: Tropical savanna with dry winters	2022-04-02 – 2022-09-23	0.5
Folsom, USA	UCSD-Folsom	38.642, -121.148	Csa: Hot-summer Mediterranean	2014-01-02 – 2016-12-30	3.0
Gatton, Australia	UQueensland	-27.559, 152.343	Cfa: Humid subtropical	2020-01-23 – 2020-05-06	0.3
Golden, USA	SRRL-BMS	39.742, -105.180	Dfb: Warm-summer humid continental	2021-04-17 – 2024-03-22	2.9
Hong Kong, China	HK-PolyU	22.305, 114.180	Cwa: Dry-winter humid subtropical	2023-02-18 – 2023-12-31	0.9
Kologo, Ghana	EnerSHelF-Kologo	10.737, -1.078	Aw: Tropical savanna with dry winters	2022-04-02 – 2022-10-04	0.5

Appendix

Supplementary Table 1: Site Characteristics.

Site	Sources	Temp. Res. (min)	Size (GB)	Samples	Available Data
Akwatia, Ghana	SolarBench	1	0.6	89,953	Sky image, GTI
Folsom, USA	[ 28 ]	1	5.9	765,140	Sky image, GHI, DNI, DHI, Air temperature, Relative humidity, Pressure, Wind speed, Wind direction, Max wind speed, Precipitation
Gatton, Australia	[ 40 ]	1	0.4	69,347	Sky image, PV output
Golden, USA	[ 26 ]	1	2.9	367,878	Sky image, GHI, DNI, DHI, Air temperature, Relative humidity, Pressure, Wind speed, Wind direction, Max wind speed, Precipitation
Hong Kong, China	SolarBench	1	1.5	223,812	Sky image, GHI, DHI, Air temperature, Relative humidity, Pressure, Wind speed, Wind direction
Kologo, Ghana	SolarBench	1	0.7	92,182	Sky image, GTI

Appendix

Supplementary Table 2: Details of the ground-based dataset for each site.

Site	Sources	Temp. Res. (min)	Size (GB)	Samples	Bands	Access link
Akwatia, Ghana	MSG	15	3.3	13,720	12	https://user.eumetsat.int/resources/user-guides/eumetsat-data-access-client-eumdac-guide & https://github.com/openclimatefix/satellite-consumer
Folsom, USA	N/A	N/A	N/A	N/A	N/A	N/A
Gatton, Australia	Himawari-8	10	3.3	9,833	16	https://noaa-himawari8.s3.amazonaws.com/index.html#AHI-L1b-FLDK/ 1
Golden, USA	GOES-16	5	48.5	118,598	16	https://noaa-goes16.s3.amazonaws.com/index.html#ABI-L1b-RadC/
Hong Kong, China	Himawari-9	10	9.5	29,305	16	https://noaa-himawari9.s3.amazonaws.com/index.html#AHI-L1b-FLDK/ 2
Kologo, Ghana	MSG	15	3.4	13,882	12	https://user.eumetsat.int/resources/user-guides/eumetsat-data-access-client-eumdac-guide & https://github.com/openclimatefix/satellite-consumer

Appendix

Supplementary Table 3: Details of geostationary satellite imagery data for each site.

Description	Variable Name	Unit
Total precipitation	tp	mm
10m U wind component	u10	m/s
10m V wind component	v10	m/s
100m V wind component	v100	m/s
100m U wind component	u100	m/s
2m temperature	t2m	K

Appendix

Supplementary Table 4: Processed ERA5 variables.

Description	Variable Name	Unit
Precipitable water for entire atmosphere	precipitable_water_entire_atmosphere	kg m ${}^{\text{-2}}$
Relative humidity 2m above ground	relative_humidity_2m_above_ground	%
Specific humidity 2m above ground	specific_humidity_2m_above_ground	Mass fraction
Temperature 2m above ground	temperature_2m_above_ground	∘ C
U component of wind 10m above ground	u_component_of_wind_10m_above_ground	m/s
V component of wind 10m above ground	v_component_of_wind_10m_above_ground	m/s

Appendix

Supplementary Table 5: Processed GFS variables.

Site	Model	RMSE [W m -2 ]	FS [%]	rRMSE	Ramp recall [%]	MAE [W m -2 ]	MBE [W m -2 ]
Akwatia	Persistence	126.5	0.0	0.291	8.7	69.2	0.7
	MLP	121.2	4.2	0.278	5.0	77.7	-14.3
	SUNSET	120.1	5.1	0.276	0.6	82.2	-11.2
	OmniVision	130.8	-3.4	0.3	5.0	79.0	-24.0
	ViT	127.6	-0.8	0.293	5.0	80.6	-15.1
Golden	Persistence	107.5	0.0	0.209	7.1	45.1	2.7

Appendix

Supplementary Table 6: Benchmark performance of the forecasting models at a 10-min lead time. The best-performing value(s) for each site and evaluation metric are highlighted in bold . Ramp recall is evaluated using a 30% relative error threshold.

Site	Model	RMSE [W m -2 ]	FS [%]	rRMSE	Ramp recall [%]	MAE [W m -2 ]	MBE [W m -2 ]
Akwatia	Persistence	144.5	0.0	0.327	5.1	86.8	2.3
	MLP	154.2	-6.8	0.349	4.5	113.6	-28.2
	SUNSET	138.4	4.2	0.313	3.2	102.7	-12.9
	OmniVision	149.0	-3.1	0.337	2.6	90.8	-15.9
	ViT	136.9	5.2	0.31	2.6	88.4	-8.6
Golden	Persistence	146.2	0.0	0.281	6.8	65.6	8.6

Appendix

Supplementary Table 7: Benchmark performance of the forecasting models at a 30-min lead time. The best-performing value(s) for each site and evaluation metric are highlighted in bold . Ramp recall is evaluated using a 30% relative error threshold.

Site	Model	RMSE [W m -2 ]	FS [%]	rRMSE	Ramp recall [%]	MAE [W m -2 ]	MBE [W m -2 ]
Akwatia	Persistence	173.5	0.0	0.385	10.1	110.5	3.7
	MLP	210.1	-21.1	0.466	3.4	165.9	-42.7
	SUNSET	166.9	3.8	0.37	0.7	132.5	-10.7
	OmniVision	168.1	3.1	0.373	9.4	121.4	-7.8
	ViT	156.7	9.7	0.347	1.3	112.1	-22.2
Golden	Persistence	172.4	0.0	0.326	1.7	83.6	17.2

Appendix

Supplementary Table 8: Benchmark performance of the forecasting models at a 60-min lead time. The best-performing value(s) for each site and evaluation metric are highlighted in bold . Ramp recall is evaluated using a 30% relative error threshold.

Site	Model	RMSE [W m -2 ]	FS [%]	rRMSE	Ramp recall [%]	MAE [W m -2 ]	MBE [W m -2 ]
Akwatia	Persistence	175.0	0.0	0.38	5.5	118.8	3.3
	MLP	245.8	-40.4	0.534	0.7	196.9	-53.8
	SUNSET	185.2	-5.8	0.403	2.1	150.9	-15.9
	OmniVision	208.6	-19.2	0.454	3.4	161.0	-40.1
	ViT	182.7	-4.4	0.397	2.1	136.4	-3.4
Golden	Persistence	194.2	0.0	0.358	1.7	99.7	27.9

Appendix

Supplementary Table 9: Benchmark performance of the forecasting models at a 90-min lead time. The best-performing value(s) for each site and evaluation metric are highlighted in bold . Ramp recall is evaluated using a 30% relative error threshold.

Site	Model	RMSE [W m -2 ]	FS [%]	rRMSE	Ramp recall [%]	MAE [W m -2 ]	MBE [W m -2 ]
Akwatia	Persistence	191.8	0.0	0.411	6.2	137.1	3.3
	MLP	288.3	-50.3	0.617	1.4	233.8	-67.4
	SUNSET	201.5	-5.0	0.431	0.7	160.5	-6.4
	OmniVision	205.7	-7.2	0.44	4.2	154.7	-1.1
	ViT	199.3	-3.9	0.427	3.5	147.9	15.3
Golden	Persistence	205.4	0.0	0.372	0.0	110.8	38.9

Appendix

Supplementary Table 10: Benchmark performance of the forecasting models at a 120-min lead time. The best-performing value(s) for each site and evaluation metric are highlighted in bold . Ramp recall is evaluated using a 30% relative error threshold.

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