Solar Irradiance Maps
Buy and sell solar irradiance maps data. Solar energy potential by location with hourly granularity. Solar project developers and rooftop solar AI need precise irradiance data.
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Find Me This Data →Overview
What Is Solar Irradiance Maps?
Solar irradiance maps are high-resolution spatial datasets that measure solar radiation intensity at ground level across geographic regions with hourly or sub-hourly time granularity. These maps combine ground-based measurements from meteorological stations, satellite observations, and reanalysis models to create comprehensive estimates of Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI). The data enables accurate quantification of solar energy potential across different locations and weather conditions. Solar project developers and rooftop solar AI systems rely on these maps to assess site viability, optimize system design, and forecast energy generation. Modern solar irradiance datasets achieve high accuracy—with model mean percentage errors as low as 0.73% in hourly time series—and can be generalized across regions with different solar climates. The data supports both financing decisions and engineering design for utility-scale and distributed solar installations.
Market Data
Hourly and sub-hourly (10-minute)
Temporal Resolution
Source: arXiv
90 m horizontal resolution
Spatial Resolution
Source: ResearchGate
0.73% mean percentage error
Model Accuracy (GHI)
Source: ResearchGate
Multiple regions: USA, Europe, Chile, Ireland
Data Coverage
Source: Springer
GHI, DNI, DHI, cloud cover, air temperature
Key Measurements
Source: Springer
Who Uses This Data
What AI models do with it.do with it.
Solar Project Development
Developers use hourly irradiance maps to assess solar energy potential, validate project viability, and support financing decisions for utility-scale and distributed solar installations.
Rooftop Solar AI Systems
AI platforms analyze high-resolution irradiance data to optimize system design, predict energy generation, and assess roof-level solar potential in urban and residential settings.
Solar Energy Resource Management
Energy planners and operators use spatio-temporal irradiance maps to forecast solar output, manage grid integration, and optimize dispatch of solar generation across regions.
PV System Performance Modeling
Engineers use irradiance components (GHI, DNI, DHI) with efficiency factors to model photovoltaic system performance and calculate expected energy yields.
What Can You Earn?
What it's worth.worth.
Historical Hourly Maps
Varies
Multi-year datasets (e.g., 2004–2016) with hourly granularity; pricing depends on geographic coverage and validation level.
Real-Time Sub-Hourly Data
Varies
10-minute resolution irradiance forecasts; premium pricing for high-accuracy, low-latency delivery to grid operators.
Regional Validated Datasets
Varies
Country or regional datasets validated against 140+ ground stations; higher value for emerging solar markets.
What Buyers Expect
What makes it valuable.valuable.
High Temporal Frequency
Hourly or sub-hourly (10-minute) resolution required for accurate solar forecasting and grid balancing; coarser intervals limit accuracy.
Spatial Precision
Fine horizontal resolution (90 m or better) to resolve micro-siting effects and enable site-specific solar potential assessment.
Multiple Irradiance Components
Data must include GHI, DNI, and DHI with associated atmospheric variables (cloud cover, air temperature) for comprehensive PV performance modeling.
Ground Validation
Datasets validated against dense networks of meteorological stations or other ground-truth measurements; mean percentage error <1% expected for premium products.
Geographic Coverage
Continuous coverage over target regions (especially complex terrains); gaps or sparse weather station density reduce data utility.
Companies Active Here
Who's buying.buying.
Project viability assessment, site evaluation, and financing support for utility-scale and rooftop installations.
Solar resource forecasting, grid integration planning, and dispatch optimization across regions.
Global satellite and reanalysis-derived irradiance products for commercial solar markets.
Ground-based measurement datasets (SRM Dataset) and spatio-temporal modeling for solar resource assessment.
FAQ
Common questions.questions.
What is the difference between GHI, DNI, and DHI?
Global Horizontal Irradiance (GHI) is total solar radiation on a horizontal surface. Direct Normal Irradiance (DNI) is radiation from the sun disk alone, useful for concentrating solar systems. Diffuse Horizontal Irradiance (DHI) is scattered radiation from the sky, critical for fixed flat-plate PV systems. Together, they provide a complete picture of solar energy resource.
How accurate are solar irradiance maps?
High-quality datasets achieve mean percentage errors as low as 0.73% in hourly Global Horizontal Irradiance time series, validated against 140+ ground stations. Accuracy depends on data sources (satellite, reanalysis, ground measurements), weather conditions, and local terrain complexity.
What spatial resolution should I require?
90 m horizontal resolution is considered industry standard for solar project development and rooftop assessment. This resolution is fine enough to resolve micro-siting effects and enable site-specific potential estimates while remaining computationally efficient.
Can these maps work in regions with complex terrain or cloudy climates?
Yes, modern statistical and satellite-based models can handle both clear-sky and cloudy conditions. However, capturing region-specific variability in complex terrains and highly changeable climates remains challenging; local weather station density and satellite model choice significantly affect performance in such areas.
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