Solar irradiance ground measurements

In this document

We will discuss how, when properly managed, solar irradiance measurement campaigns provide significant value to solar projects, supporting energy strategies, financial planning, and research studies at both regional and project levels.

Overview

Establishing solar measurement campaigns provides a strategic advantage by enabling the adaptation and validation of radiation models at both regional and project levels. These campaigns can generate high-quality data that are essential for decision-makers and investors.

Solar irradiance ground-based sensors, in particular, require regular maintenance and rigorous quality control procedures; otherwise, the measurements may lose their value, become unreliable, or introduce errors that could negatively impact decision-making processes.

In addition to solar irradiance measurements, deploying supplementary sensors for temperature, wind speed, humidity, atmospheric pressure, and precipitation is vital. These additional parameters play a key role in climate analysis and the evaluation of expected system performance.

Solargis has extensive experience in quality control of the measured data and provides best practices guidance on:

design of the measurement campaign,
operation and maintenance of the meteorological stations,
and data processing with the aim of using it in site adaptation of satellite model data.

*Representation of ground-measured data after quality control*

Solar measuring campaigns

Solar measuring campaigns are essential for the accurate assessment of solar resources and the optimization of photovoltaic (PV) system performance. These campaigns provide site-specific data critical for reducing uncertainty in solar energy projections, adapting satellite-based models, and ensuring reliable system operation:

Planning phase

Operational phase

Regional mapping

During the planning phase of a solar project, the primary objective of on-site measurements is to capture accurate local meteorological characteristics. These measurements help adapt satellite-based models to the specific conditions of the site, reducing uncertainty in long-term time series data and aggregated energy estimates. The most common instrumentation during this phase includes a pyranometer for measuring global horizontal irradiance (GHI) and a pyrheliometer for direct normal irradiance (DNI). Together, these instruments provide critical inputs for resource assessments and energy yield simulations.

In the operational phase of a solar plant, on-site measurements are vital for monitoring plant performance and identifying potential failures. Ground measurements are often supplemented by satellite time series, which serve as an independent reference for quality control and optimization of ground-based data. A common practice during this phase is the use of pyranometers mounted on tilted planes to measure global tilted irradiance (GTI), which corresponds more directly to the plane of the PV modules. The number of sensors required depends on the size and complexity of the plant, with larger plants typically requiring multiple sensors for comprehensive monitoring.

For regional solar resource mapping, careful selection of monitoring sites is crucial to represent the full climatic diversity of the area. Solar irradiance can vary significantly due to geographic and atmospheric factors, such as altitude, cloud cover, and aerosol concentrations. By strategically placing ground sensors in locations that reflect this diversity, it is possible to collect a comprehensive dataset that accurately captures regional variations. This data supports the creation of robust solar resource assessments applicable across the region, improving the accuracy of solar energy projections and enhancing the validation of satellite-based models.

Solar irradiance sensors

A variety of solar irradiance sensors are used in the solar industry, each designed to measure different components of solar radiation, such as Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI). These instruments vary in precision, functionality, and cost, making them suitable for different applications and stages of solar project development. Below is an overview of the most commonly used solar irradiance sensors and their key characteristics:

Pyranometers are used to measure Global Horizontal Irradiance (GHI). Pyranometers use a thermopile sensor that converts the heat absorbed from incoming solar radiation into an electrical signal. A glass dome covers the sensor to protect it and to allow the capture of a wide spectrum of solar radiation. The electrical signal is proportional to the total irradiance received by the pyranometer.
Pyrheliometers are designed to measure Direct Normal Irradiance (DNI). A pyrheliometer uses a case —called collimator tube— to allow only direct sunlight to hit the sensor, blocking diffuse sunlight. The sensor uses a thermopile to measure the solar radiation that enters the tube, converting the absorbed heat into an electrical signal. The instrument is typically mounted on a solar tracker to continuously align it with the sun’s position throughout the day.
Rotating Shadowband Radiometers (RSRs) measure both global horizontal irradiance (GHI) and diffuse horizontal irradiance (DIF). By periodically shading the sensor with a rotating shadowband, RSRs can differentiate between direct and diffuse solar radiation and estimate direct normal irradiance (DNI) indirectly from the relationship between GHI, DIF, and DNI. RSRs are less sensitive to soiling than thermopile sensors, which reduces cleaning frequency and lowers operational costs. They are commonly used at secondary meteorological stations where a lower maintenance burden is a priority.
Silicon-based sensors, such as PV reference cells, measure the electric current generated by a photo-sensitive diode. Their lower cost has made them popular in the industry; however, their limited accuracy and susceptibility to drift and instability do not qualify them as standard measuring solutions for solar resource assessment. Only Class A thermopile instruments are recommended where data quality and bankability are required.

Note: The SPN1 sunshine pyranometer is sometimes used in the industry as an alternative for DNI measurement. Solargis does not recommend this instrument. Its measurement uncertainty is ±5% daily under ideal conditions and higher in real-world operation. Time series from SPN1 instruments consistently show artifacts caused by the internal shading mask design, which can resemble soiling events but occur regardless of cleanliness, making quality control unreliable.

Instrument maintenance

Maintaining solar irradiance sensors, such as pyranometers, pyrheliometers, and rotating shadowband radiometers (RSRs), is essential for ensuring accurate energy production estimates, optimizing system performance, maintaining financial stability, complying with regulations, and extending equipment longevity.

Most common maintenance activities involve the following actions:

Regular Cleaning: The glass dome must be kept clean to avoid measurement errors due to dirt, dust, or water spots. In moderate environments, cleaning 1 to 2 times per week is generally sufficient. In areas with high soiling risk, such as high aerosol concentrations, sandstorms, or extended dry periods, daily cleaning may be required.
Recalibration: Periodic recalibration (typically every 1-2 years) is necessary to ensure accuracy.
Inspection: Visual inspections to ensure there are no cracks or issues with the dome or sensor.
Solar Tracker Maintenance (for pyrheliometers): Regular checking and maintenance of the solar tracker to ensure accurate alignment.
Shadowband Calibration and Maintenance (for RSRs): The rotating mechanism needs to be regularly checked and calibrated to ensure smooth operation and accurate shading.
Regular checks of data loggers, communication systems, power supplies, and data processing software are essential to avoid data loss.
Cleaning event logging: Cleaning events should be recorded using a push-button event logger - a switch connected to the datalogger that is pressed before and after each cleaning. This timestamps cleaning events directly alongside measured data, making them available for quality control and post-processing.

Neglecting sensor maintenance can lead to inaccurate data, reduced system efficiency, financial losses, non-compliance with regulations, and costly equipment failures.

Data quality control

Ground sensors used to measure solar irradiance are subject to various environmental and technical factors that can affect data accuracy. Environmental conditions such as dust, humidity, dew, and snow, as well as technical issues like misalignment, miscalibration, or data logger malfunctions, can introduce errors in the measurements. To ensure that ground-measured data is reliable and suitable as a “ground reference,” it is essential to perform comprehensive quality control and error filtering procedures.

A thorough quality control process is required to validate and analyze ground-measured solar data effectively. The solar data quality assessment methodology employed by Solargis follows these key steps:

Time-related issue correction: Accurate time alignment is critical for all subsequent quality checks. Shifts, time drifts, and diurnal asymmetry (morning vs. afternoon discrepancies) are identified and corrected. This can involve manual analysis by data analysts or automated algorithms that detect and suggest corrections for these time-related issues.
Detection of invalid values: Data is systematically checked for nighttime/daytime inconsistencies, artificial static values, values exceeding physical limits, and discrepancies in solar irradiance components. Each problematic record is flagged with predefined markers. Additional flags may indicate specific conditions, such as tracker malfunctions, where the sun-tracking mechanism fails to operate correctly.
Visual inspection: Flagged data values undergo a final visual inspection to confirm their validity. This step ensures that flagged records are accurately identified and appropriately annotated. The flagged data is then saved with the dataset, updating its quality status.
Post-processing: In this final stage, unnecessary flag markers are removed to simplify the dataset for further analysis. This step refines the dataset by discarding redundant or less valuable flags, streamlining the overall data quality assessment process.

At the end of this rigorous process, errors are identified, and invalid data records are flagged and excluded from subsequent analyses. Common issues flagged and discarded include:

Invalid records or values below the horizon.
Measurements below minimum physical limits.
Consecutive static values.
Inconsistencies in solar irradiance components (GHI, DIF, DNI).
Failed two-component tests (e.g., GHI vs. DIF).
Data affected by shading or tracker malfunctions.

By applying this comprehensive quality control framework, the dataset becomes a reliable reference for accurate solar resource assessments and performance evaluations, supporting more precise energy yield predictions and project optimization.

Usage in the Solargis platform

Solar irradiance ground measurements are an essential part of the Solargis Evaluate application to simulate the data. Additionally, we use it when generating TMY and TS data available via API and as a part of the consultancy services.