Methodology & Specifications

1. Introduction

This document describes the technology, methods, and standards utilized by the Denowatts solar performance benchmarking and testing service. The intent is to describe the methodology adequately for a technical understanding of the Denowatts service by an independent review. The methods and definitions are modeled after IEC 61724-series (2016-2021) and ASTM E2848 (2018) performance management and evaluation standards with supporting standards described in relevant sections.

This document is divided into three general sections:

Sensors and Metrology
Benchmarking, Energy Accounting, and Performance Reporting
Capacity Testing

To request further clarification on any item in this document, please email support@denowatts.com or call 978.309.6688 x1

2. About Denowatts

Denowatts Solar, LLC is a solar performance testing and analytics company with a mission to utilize advanced Digital Twin Benchmarking technology to deliver faster, more accurate, and more efficient business intelligence to the solar industry. An ISO/IEC 17025 (2017) Accredited Calibration and Testing Laboratory, Denowatts combines innovative technology with the solar industry's most recognized standards to achieve the highest confidence and bankability in solar performance evaluation and management.

Denowatts Solar, LLC maintains accredited scopes under ISO/IEC 17025 (2017), including calibration of Deno Sensor pyranometers, ASTM E2848 and IEC 61724-2 Capacity Testing, and IEC 61724-3 Energy Testing. Search Denowatts:

Traditional Solar Performance Benchmarking

Benchmark: standard or point of reference against which things may be compared or assessed.

Traditionally, weather stations are installed as part of a solar monitoring service to measure local irradiance, temperature, wind speed, and other conditions. Weather stations are often a general design that support many uses from agriculture to climate studies. Pyranometers are specified to ISO 9060 classifications for accuracy to quantify measurement uncertainty of benchmarking. The highest accuracy weather stations utilize flat spectrum ISO 9060 Class A pyranometers optimized for ideal incident angle and broad-spectrum response. Anemometers and temperature sensors capture wind speed and ambient temperature, respectively. Conversion models, informed by plant assumptions and empirically derived data, then estimate the amount of irradiance absorbed by the solar cells and their operating temperature.

Finally, the absorbed irradiance and module silicon cell temperature are the primary inputs into the performance energy model to create Expected Energy, sometimes called the "weather adjusted" performance benchmark. Expected Energy is the fundamental benchmark of performance success for the solar asset.

Weather stations are typically deployed by the construction (EPC) contractor, commissioned and managed by the monitoring company, maintained by the O&M service company, and validated by the asset owner's independent engineer. Within this process of the lifecycle, deployment, and management of the benchmark are contributing risks to measurement uncertainty that increase over the lifecycle of the solar asset without careful management, such as sensor alignment and calibration, metrology equipment cleanliness and serviceability, and data quality management.

Denowatts Benchmarking Technology

Deno Digital Twin Benchmarking (DTB) technology is a new approach to managing measurement uncertainty risks using capabilities previously unseen in the solar industry. Deno DTB technology uses optimized sensors, edge computation, and remote management capability to deliver enhanced benchmarking data. The physical hardware that is the heart of the Deno DTB technology is the Deno Sensor.

Optimized Sensors

Denowatts optimizes irradiance measurement with pyranometers specifically designed and calibrated to measure the absorbed irradiance of a solar module, most commonly known as "Effective Irradiance." This sensor design approach means that the sensor's responsivity and measurement uncertainty analysis with respect to the incident angle, spectrum, and response time is optimized to a typical crystalline solar module.

Deno temperature sensors include both a back of module (T_bom) and an internal temperature sensor (T_m). (T_bom) sensors are adhered to the backside of solar modules to measure the backside module temperature. As a benchmarking input, (T_bom) and (T_m) measurements are converted to cell temperature (T_cell) using a standard formula.

In addition to the purpose-built sensor design, the overall form factor of the Deno Sensor reduces measurement uncertainty in several areas:

A flat, low-profile design allows the sensors to mount in the module plane with reduced risk of misalignment; There are no alignment fasteners to adjust.
Wireless and self-powered operation allow the sensors to be easily located in the sunniest part of the array reducing the risk of near-shading error.
Star-cluster deployment means many Deno sensors can be cost-effectively deployed to "blanket" a solar array, reducing overall benchmark to power alignment error.
Dual POA pyranometers allow redundancy and assist data quality management.
Auxiliary pyranometer allows rear side or horizontal pyranometers to plug into each Deno Sensor.
Redundant (Tbom) and (Tm) temperature sensors reduce the risk of data quality error.
Enhanced data architecture to manage temperature and drift effects.

Edge Computation and Data Backup

In addition to measuring irradiance and temperature conditions, Deno Sensors process data and compute critical benchmarks in real-time for backup purposes. Often referred to as "edge" or "fog" computing, this data processing architecture performs computation as close to the sensor as possible, enhancing benchmarking resolution while minimizing bandwidth requirements for transmitting data to the cloud. This enhanced resolution results in lower benchmarking uncertainty, especially during intermittent and unstable irradiance conditions. Additionally, the Deno Sensor logs cumulative irradiation and expected energy values for continuity and backup during a cloud communication failure.

Remote Management and Data Quality Control

Remote management of devices, commercially known as the "Internet of Things" (IoT), and Edge computation enable a paradigm shift in the approach to metrology. Traditional sensor deployments means are "read-only" devices that are mounted and read. However, the IoT and Edge computation architecture of Deno DTB technology enable capabilities to remote manage data quality control and minimize measurement uncertainty.

Active benchmark quality management is critical to minimizing performance measurement uncertainty. The Denowatts service includes standardized processes, including energy model validation, sensor commissioning, data quality assurance, and enhanced measurement processing to reduce the error introduced by a host of risks that can compound testing measurement uncertainty well beyond the range of the sensors alone. Examples include:

Human error, such as mistyping a calibration factor, is eliminated because each Deno Sensor connects directly to the server that includes their calibration.
Data quality management processes can identify sensor errors and switch to redundant or backup sources.
Temperature correction is applied locally at the sensor level.
Sensor drift correction is applied locally at the sensor level.
Sensor grouping processes reduce benchmark misalignment.
Incident angle correction algorithms reduce benchmark misalignment.

3. Sensors and Measurement

Sensors

Irradiance Measurement and Usage Overview

Effective (i.e., absorbed) irradiance is the most crucial measured input into the performance energy model. Effective irradiance is the measurement of the usable irradiation by a solar module, accounting for spectral, incidence angle, shading, and soiling losses. Deno Sensors are designed to measure effective irradiance rather than global incident irradiance to minimize benchmarking uncertainty. Deno effective irradiance measurements use pyranometer technology similar to the photovoltaic module technology. Shading and soiling due to snow irradiance losses are not measured directly by the Deno Sensor but are detected via the Learned Profile algorithms. Details of this approach are defined in Table 1 Pyranometer Parameters below.

Pyranometers

The Deno Sensor consists of two inboard a-Si photodiode pyranometers operated in photoconductive mode. The photodiodes have a spectral response of 400-1100 nm with a peak at ~900 nm and a response/sampling time of under 10 ms. The diffuser corrects the angle of incidence response of the sensor to match that of a typical photovoltaic module. Deno pyranometers have opposing 180-degree rotation, and samples of the two pyranometers average together to reduce directional error. Irradiance readings have a measurement resolution of 0.1 w/m2 and a range from 0 to 1500 w/m².

The Deno Sensor may include a detachable Deno Auxiliary pyranometer that uses the same photodiode and diffuser components as the inboard pyranometers. The Auxiliary pyranometer may mount in the horizontal or reverse plane of array orientation.

3rd Party Pyranometer Attachment

Users may optionally connect a 0-20 mV 3rd party pyranometer to the Deno Sensor. For example, this allows a user to add a Class A spectrally flat thermopile pyranometer to measure Global Horizontal Irradiance (GHI). Measurements and logging are the same frequency as the onboard Deno sensors.

The Reference Channel calibration is part of the ISO/IEC 17025:2017 Calibration Laboratory Accreditation Scope with a CMC of 1.0% of Reading.

Calibration

Denowatts Solar, LLC is an ISO/IEC 17025:2017 Accredited Calibration Laboratory (Accreditation #106276). The Scope of Accreditation includes Deno pyranometer calibration with a Calibration and Measurement Capability (CMC) of 1.0% of Reading. This CMC includes the Pyranometer measurement uncertainty and the signal processing and logging uncertainty. Hence, the Calibration Uncertainty defined on the Calibration Certificate is the total uncertainty of the final record value delivered to the user.

Each Deno Sensor includes a link to download the Calibration Certificate. An example Calibration Certificate is available on request.

Table 1 ISO 9060:2018 and PV Module Baseline Characteristics (Deno Model 3.17 and higher) Note: This table applies to Deno model 3.17 and higher. Full details are available on the Deno DTB Specification Sheet May 2022

Parameter	ISO 9060 - Value	ISO 9060 - Class	Photovoltaic Module Basline (value)
Response Time	< 10 ms	A⁴	-
Zero Offset¹	-	-	-
Non-Stability	+/- 1.5 %	B	-
Non-Linearity	+/- 0.5 %	A	-
Directional Response (up to 75°)²	+/- 41.6 W/m²	-	+/- 18.7 W/m²
Clear Sky Spectral Error	+/- 3.1 %	C	+/- 1.7 %
Temperature Response	+/- 0.15 %	A	-
Tilt Response ¹	-----	-	-
Additional Signal Processing Error	+/- 1 W/m²	A	-
Max Absorbed Irradiance Error ³	-	-	+/- 13.7 W/m²
Mean Absorbed Irradiance Error ³	-	-	-3.1 W/m² / -0.5 %

¹ Not relevant to photodiode responsivity
² PV Module cosine response modeled with Fresnel equations
³ Outdoor test under varying sky conditions (>300 w/m², cloudy to clear, < 60° incident angle)
⁴ Fast-Response Category

Temperature Measurement and Usage Overview

Module cell temperature is the second critical input into performance energy models. ASTM E2848 and IEC 61724-series standards reference methods that measure ambient temperature and wind speed to calculate cell temperature. Wind speed and ambient temperature are measured parameters of most historical weather data sets, and hence methods have been developed to compute cell temperature from them.

Denowatts does not utilize ambient temperature and wind speed to calculate cell temperature. Instead, Denowatts uses a back of module sensor (T_BOM) with irradiance gain technique to arrive at cell temperature for capacity testing. This method is supported by IEC 61724-2 when found to improve the reproducibility of the measurement. Denowatts has also observed that independent reviewers widely support this approach.

In addition to the detachable back of module temperature sensor, the Deno Sensor includes an inboard temperature sensor used as the primary cell temperature for ordinary benchmarking or backup cell temperature reading for capacity testing.

Cell Temperature Derived from Back of Module Sensor

The Deno external temperature sensor is attached to the Deno sensor utilizing an IP67 connector and a 3' cable. This sensor is a precision CMOS sensor with a typical accuracy of 0.4 °C that is mounted within a sealed canister and digitally measured. This sensor generates a back of module temperature (T_BOM) measurement. The sensor head is an aluminum case that is adhered to the back of the module using thermally conductive tape and a weather-tight adhesive patch.

Denowatts utilizes the IEC 61724-2 method for calculating cell temperature (T_c) from the back of module temperature (T_BOM) sensor. The standard provides the following heat transfer model to calculate the cell temperature of solar modules.

T_c = T_BOM + (G/1000) dT_cond

Where T_BOM is the back of module temperature, T_c is the cell temperature, and G is the plane of array irradiance. The constant dT_cond represents the conduction coefficient from the back of module temperature to the cell temperature. The conduction coefficient derives from empirical data for different module and mount types. By default, Denowatts uses the conduction coefficient for glass/cell/polymer sheet module on an open rack.

Cell Temperature Derived from Integrated Sensor

The Deno integrated temperature sensor is a surface-mounted chip on the underside of the Deno circuit board. This sensor is a precision CMOS sensor with typical accuracies of 0.4 °C measured with analog circuitry. This sensor generates measured temperature (T_m).

Cell temperature is calculated as a function of plane of array irradiance (G) and the measured temperature (T_m). This equation is a modified version of the IEC model of converting the back of module temperature to the cell temperature. Constant a has been determined through experimentation and is dependent on the mount type of the system.

(T_c) = (T_m) + aG

Measurement

Deno Sensor Position and Attachment

The Deno Sensor must be installed as instructed in the Installation Specifications. Deno Sensors are primarily mounted in the Plane of Array (POA), though they may optionally be oriented to measure horizontal irradiance.

POA-oriented Deno Sensors must be mounted at the top of the module in the sunniest part (least potential for shading) of the array in the "center mass" orientation of the array. Sensors are mounted between solar modules or may be attached to 1^5/8" strut in a similar orientation. POAs are grouped by tilt, azimuth, mounting (i.e., tracker vs. fixed) type, and module (i.e., monofacial vs. bifacial) type. A minimum of one Deno sensor is required for each unique POA.

When benchmarking single-axis trackers (SAT), Deno Sensors should be mounted above the torque tube or as close to the center rotating axis as possible.

Rear plane of array (rPOA) mounted Auxiliary pyranometers should not be located in a perimeter row of an array or at the end of a row. On a fixed tilt structure, the rPOA sensor should be mounted at 2/3's up the total module height. For SATs, the rPOA sensor should be mounted close to the center axis and must be atleast 15 feet in from the end of the row. rPOA sensors must be at least 18" from any objects, such as structural members, that could impact the measurements.

IEC 61724-1 (2020) Table Matrix (Summarized)

The Denowatts service utilizes a standard Deno sensor, optional 3rd party sensor, and remote weather services to meet Class B.

Table 2. IEC 61724-1 Summary

Parameter	Units	Deno Standard	Class	Notes
Sampling Interval	Seconds	5(day), 60(twilight), 900(night)	A
Record/Transmit Interval	Seconds	5(day), 60(twilight), 900(night)	A
Plane of Array Irradiance (POA)	W/m²	Ground-Based	B	See Table 1
Reverse Plane of Array Irradiance (rPOA)	W/m²	Ground-Based	B	See Table 1
Global Hoizontal Irradiance (GHI)	W/m²	Ground-Based	A*	*When fitted with Optional Class A Thermopile Sensor
PV Module Temperature	°C	Ground-Based	A	Wired T_bom sensor with thermally conductive tape
Cell Temperature (Derived from T_m)	°C	Ground-Based	N/A	Derived value not contemplated in 61724-1
Ambient Air Temperature	°C	Remote Weather Service	B	TWC Enhanced
Wind Speed	m/s²	Remote Weather Service	B	TWC Enhanced
Wind Direction	Degrees	Remote Weather Service	N/A	Not considered for Class B
Rainfall	cm	Remote Weather Service	B	TWC Enhanced

4. Benchmarking and Accounting

Power/Energy Models

Simple Model (Expected, Compared, and Learned)

IEC 61724-2 (2016) categorizes an energy model as either Simple or Complex. The simple model defined below is a modification to the NREL PVWatts model:

L_age = 1 - (1 - L_{age_factor})^age

L_static = 1 - ∑︀(1 - L_i)

P'_dc = (G/1000) P_dc0 (1 + γ (T_cell -25)) (1 - L_static)

P_dc = P'_dc (1 - LF' x L_{dc_ohmic})

where P_dc is the dc power after dc ohmic losses, P'_dc is the dc power before dc ohmic losses, P_dc0 is the dc capacity, γ is the temperature coefficient of the modules, L_static is the combined static losses of the array, L_{dc_ohmic} is the dc ohmic loss at standard test conditions, G is the effective POA irradiance, and T_cell is the cell temperature of the modules. Static losses include age derate, light-induced degradation, mismatch, module quality, and other constant losses.

LF = P_dc / (P_ac / η )

P'_ac = P_dc x (η / 0.9637) x (-0.0162 x LF - (0.0059 / LF) + 0.9858) x (1 - LF x *L_{ac_ohmic})

P_ac = min (P_ac,P_ac0 x (1 - L_{ac_ohmic}))

where P'_ac is the ac output of the array before clipping, P_ac is the ac output of the array after clipping, P_ac0 is the array ac clip, η is the inverter CEC efficiency, L_{ac_ohmic}) is the ac ohmic loss at stc, and LF is the load fraction.

Expected power/energy is calculated using the defined Simple model. All constants are defined in the Denowatts block management section.

Compared Energy is calculated using the defined simple model. The temperature coefficient is set to -0.42%/°C, static losses are set to 3%, inverter efficiency is set to 98%, and all other parameters are defined on the block management page.

The Learned model is an IEC 61724 simple model fit to the site based on measured irradiance, temperature, and site generation. The P_dc0 and P_ac0 parameters are calculated based on measured data, static losses are set to 0%, and all other parameters are defined in the block management section. Sensor misalignment corrections may be applied if necessary to reduce hysteresis error. See the Learned Energy section for more information.

Complex Model (Single Diode)

Denowatts uses the single diode model and Sandia inverter model from the PVLIB toolkit to calculate the IEC 61724 Complex Expected Energy. PVLIB is an open-source toolkit for Python and MATLAB initially developed by Sandia National Laboratories that provides common solar calculations. The single diode model is used to characterize the I-V curve of a solar module. The Sandia inverter model uses a set of empirical coefficients to characterize the efficiency curve of an inverter. Parameters for the modules and inverters are generated from the CEC module and inverter library provided in the NREL SAM database.

Customer Model Fit

The Customer Model Fit is a simple linear regression applied to a user-supplied hourly model. This user-supplied model fits a line (y= m * x) to the temperature corrected site generation against the irradiance. The resulting coefficient is used with the temperature coefficient to approximate hourly Expected Power.

Predicted

Predicted Energy utilizes the energy model report monthly supplied by the customer. Predicted Energy is calculated from historical weather conditions. User-supplied monthly predicted values estimate the average daily Predicted Energy.

Denowatts reconstructs the Investor Simulation using a Proxy Model and the Degradation Schedule to produce Year N Simulations. All Predicted Energy accounting is reported against these reconstructed Proxy Model Simulations, not the original Investor’s Model, though the Predicted Energy values should be very closely aligned. See Model Management and Models & Benchmarks.

Learned Energy

Learned Power is a benchmark developed by Denowatts to describe the observed power profile of a site, including shading impacts and electrical characteristics. Once the Deno Sensors have been commissioned, the process of learning the power profile of a site begins. Each night machine learning algorithms analyze site performance from the previous day and characterize the "fully in-service" operating profile. Measured power, solar position, solar resource, and temperature data are used to calculate the Learned Profile.

The Learned Profile is comprised of a Learned Model (simple model), Commissioned Model (simple model), and a shading profile. The Learned Model is used to calculate the Learned Power based on weather conditions. The Learned Model is updated daily. Learned Power represents how much power the site typically produces, assuming the weather conditions are homogeneous across the site. The Commissioned Model is a Learned Model that represents the most optimal performance observed at the site. The Commissioned Model is updated when a significant change has been made to the site, such as the initial commissioning or annual recommissioning. The shading profile is used to determine how much underperformance should be allocated to shade loss based on solar position mapping of in-service site performance.

The Learned Power profile is initially estimated based on the Expected Model, though updates gradually as measured irradiance, temperature, and site generation are gathered. Learned Power attributes primarily consist of the inverter power DC input and maximum AC power output.

In addition to electrical characteristics, Learned Power calculations may compensate for benchmark misalignment. If a Deno Sensor is installed with an observed misalignment from the measured power orientation, an adjustment may be set by Denowatts personnel. The benchmark misalignment is estimated in the Deno Commissioning Report.

Energy Accounting

The Learned Power is calculated following each day for each commissioned site that has “learning” enabled in the Denowatts site management portal. Denowatts machine learning algorithms tag outage losses with a likely outage reason. Possible outages are provided to Denowatts and customer staff for technician verification, classification, and event details. Automatically flagged outage reasons include:

Snow (if there was recorded snowfall)
Recloser/Grid Protection (if site generation is zero or missing)
Inverters Offline (if the outage loss is constant)
Inverters Derating (if the outage loss increases near peak performance)
Undetermined (everything else)

If a possible outage is detected, the Learned Profile will not be updated. After an outage has been deleted, data from its time period will be learned.
Shade losses are calculated from the shading profile. These represent consistent underperformance at specific solar positions. If it is determined that there were no outages during the day, all underperformance on a shaded time interval will be considered shade loss. Misalignment error may be falsely classified as shade loss if the misalignment is not corrected.

Systemic losses are calculated from the difference between the Commissioned Power and the Learned Power. These represent long-term losses such as soiling (non-snow), open DC circuits (reversible), and DC degradation (non-reversible).

Outage losses are calculated from the difference between the Learned Power and the measured site generated power. These represent short-term losses that can be resolved, such as snow and hardware non-availability.

Key Performance Indices (KPIs)

The site Energy Performance Index (EPI) is the ratio of the Measured Generation to the Expected Generation. Shade and snow losses are removed from the Expected Generation calculation so the index only represents the electrical system performance. A performance index can be "All-In" or "In-Service". "All-In" includes outage losses, while "In-Service" represents the fully functioning part of the site.

EPI_all-in = E_m / (E_e - L_shade - L_snow)

EPI_in-service = E_m / (E_e - L_shade - L_snow - L_outage)

Where E_m is the measured generation, E_e is the expected generation, L_shade is the shade losses, L_snow is the snow losses, and L_outage is the outage losses. The EPI, as defined in the IEC 61724-3, calculates Expected Energy using the same model that calculated the Predicted Energy. A variant of the EPI is the Baseline Energy Performance Index (BEPI), which uses the Predicted Generation (P50 model) instead of the Expected Generation. The EPI (the Expected Index) and BEPI (the Predicted Index) are displayed in the portal's Site Overview KPI table, alongside In Service EPI, Energy Availability, and Equipment Availability.

In addition to the Expected Index and Predicted Index, Denowatts provides the Learned Index, Commissioned Index, and Compared Index by replacing the expected generation with Learned, Commissioned, and Compared, respectively. The Learned Index represents the site's performance relative to its normal operation. Assuming no outages, this performance index should be close to 100%. The Commissioned Index represents the performance of the site compared to when it was last re/commissioned. A low Commissioned Index indicates that there are soiling or systemic issues at the site. The Compared Index is used to compare different sites to each other. A higher Compared Index indicates that the site makes better use of the available solar resource than a site with a lower Compared Index.

The ratio of the "In-Service" EPI and the "All-In" EPI represents the Energy Availability (EA) of the site. Energy Availability represents how much of the site is fully functioning.

EA = EPI_all-in/ EPI_in-service

EA = (E_e - L_shade - L_snow - L_outage) / (E_e - L_shade - L_snow)

Primary and Backup Calculations

Calculations are processed on both the cloud and the Deno Sensor. The same models and parameters are used in both cases. As data records are reported from the Deno Sensor, any necessary corrections to the data are applied. Expected Energy is then calculated using the corrected values for each record and aggregated into 5-minute records. If the communication between the Deno Sensor and the Denowatts server is interrupted, uncorrected cumulative values from the Deno Sensor will be used.

5. Capacity Test

Denowatts offers two parallel capacity tests, IEC TS 61724-2 2016 ("IEC") (unconstrained and constrained) and ASTM E2848 2013 ("ASTM"). Three energy models are used to calculate expected Energy: the Customer Model (fit from the hourly "8760" file), the Complex Single Diode Model (using PVLIB), and the Simple Expected Model. This produces six DC Capacity test results and three AC Capacity test results. The capacity test report is downloaded as a ZIP folder, containing a .pdf report and .csv data file.

The 8760 file must uploaded based on the template file. Effective irradiance should be used in the 8760 file. If there are multiple Deno Groups (i.e., planes of array), the effective irradiance, back of module irradiance, and array temperature are combined by weighted average with respect to the dc capacity of the Deno Groups. Users may generate a draft capacity test after entering the relevant information into the block management page and uploading an 8760 file. Users may define specific target reference conditions, filters, and soiling assumptions. A notes field is available to express any relevant conditions of the test (e.g., trackers stowed, or panels washed).

Users may also select which tests should be part of the final test result; All tests will be run and shown on the capacity report for comparative purposes regardless of the selection. Upon request, the Denowatts team will review the draft capacity test for data quality and errors. Once the draft is validated, a final report is generated by Denowatts and uploaded to the site's Denobox.

The capacity test uses three primary data fields as inputs: effective irradiance (W/m2), back of module temperature (°C), and site generation (kW). Effective irradiance is measured by the dual a-Si pyranometers on the Deno. The back of module temperature (TBOM) is used to calculate cell temperature (°C). If the TBOM sensor is not available or is in error, cell temperature measured by the Deno integrated temperature sensor (Tm) may be used instead. Site generation is provided by the user via local Modbus polling (preferred), or alternatively, by API GET requests or HTTP Post.

Sites with trackers may have difficulty finding periods with enough eligible data points for a capacity test during the summer months due to the combination of inverter clipping and inter-row shading. If this is the case, Denowatts recommends the trackers be stowed in the horizontal position for the duration of the test. Complete methods and detailed information on the capacity test can be found in the sample capacity test report.

References

IEC 61724-1:2021, Photovoltaic System Performance – Part 1: Monitoring
IEC TS 61724-2:2016, Photovoltaic System Performance – Part 2: Capacity evaluation method
IEC TS 61724-3:2016, Photovoltaic System Performance – Part 3: Energy evaluation method
ASTM E2848-13 (2018) Standard Test Method for Reporting Photovoltaic Non-Concentrator System Performance
ASTM E2939-13 (2013) Standard Practice for Determining Reporting Conditions and Expected Capacity for Photovoltaic Non-Concentrator Systems
ISO 9060 (2018) Solar energy—Specification and classification of instruments for measuring hemispherical solar and direct solar radiation
ISO 9847 (1992) Solar energy – Calibration of field pyranometers by comparison to a reference pyranometer
ISO/IEC 17025 (2017) General requirements for the competence of testing and calibration laboratories
Dobos, A.; PVWatts Version 5 Manual. NREL/TP-6A20-62641. 2014.
PVsyst Physical Models Used, https://www.pvsyst.com/help/
William F. Holmgren, Clifford W. Hansen, and Mark A. Mikofski. "pvlib python: a python package for modeling solar energy systems." Journal of Open Source Software, 3(29), 884, (2018). https://doi.org/10.21105/joss.00884
System Advisor Model Version 2020.11.29 (SAM 2020.11.29). National Renewable Energy Laboratory. Golden, CO. Accessed April 29, 2022. https://sam.nrel.gov

Last updated on October 2, 2026

Methodology & Specifications

1. Introduction#

2. About Denowatts#

Traditional Solar Performance Benchmarking#

Denowatts Benchmarking Technology#

Optimized Sensors#

Edge Computation and Data Backup#

Remote Management and Data Quality Control#

3. Sensors and Measurement#

Sensors#

Irradiance Measurement and Usage Overview#

Pyranometers#

3rd Party Pyranometer Attachment#

Calibration#

Temperature Measurement and Usage Overview#

Cell Temperature Derived from Back of Module Sensor

Cell Temperature Derived from Integrated Sensor

Measurement#

Deno Sensor Position and Attachment#

IEC 61724-1 (2020) Table Matrix (Summarized)#

4. Benchmarking and Accounting#

Power/Energy Models#

Simple Model (Expected, Compared, and Learned)#

Complex Model (Single Diode)#

Customer Model Fit#

Predicted#

Learned Energy#

Energy Accounting#

Key Performance Indices (KPIs)#

Primary and Backup Calculations#

5. Capacity Test#

References#