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2024 Global Solar Report

2024 Global Solar Report

2024 Global Solar Report

Raptor Maps' Annual Performance Review of the Solar Industry

Raptor Maps' Annual Performance Review of the Solar Industry

Raptor Maps' Annual Performance Review of the Solar Industry

Raptor Maps' Annual Performance Review of the Solar Industry

Raptor Maps' Annual Performance Review of the Solar Industry

Up to $4.6 B

Preventable Revenue Loss for Industry

Up to $4.6 B

Preventable Revenue Loss for Industry

Up to $4.6 B

Preventable Revenue Loss for Industry

Up to $4.6 B

Preventable Revenue Loss for Industry

Up to $4.6 B

Preventable Revenue Loss for Industry

125 GWp

PV Systems Analyzed To-Date

125 GWp

PV Systems Analyzed To-Date

125 GWp

PV Systems Analyzed To-Date

125 GWp

PV Systems Analyzed To-Date

125 GWp

PV Systems Analyzed To-Date

Table of Contents

Table of Contents

Table of Contents

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Executive Summary

Executive Summary

Executive Summary

Executive Summary

The global solar market continued to boom in 2023, propelled by favorable legislation and decreases in the levelized cost of energy (LCOE) that greatly reduced development expenses. An estimated 413GW (source: BloombergNEF) of solar was installed globally in 2023, up 58% from the prior year, and solar comprises the vast majority of new generation capacity globally according to data from NREL. In the United States, utility-scale solar is expected to represent 58% of energy capacity additions in 2024—a record-setting 36.4GW of capacity. 


Solar’s extremely rapid growth, however, has led to numerous challenges that require bold innovations in solar operations and the adoption of emerging technology. Our analysis highlights the concerning trend of increasing underperformance due to asset health and equipment issues, resulting in up to $4.6 billion in annual revenue loss in 2023 for the entire industry. 


Addressing underperformance is critical for a financially successful and sustainable solar industry, requiring swift fault identification and resolution to minimize downtime and boost project returns. Tech-savvy owners and operators are increasingly turning to new technologies such as robotics and AI to help tackle these issues at scale. Succeeding in solar will increasingly require leveraging data derived from machine learning to optimize energy yield and margins.


Raptor Maps’ Global Solar Report quantifies and identifies leading drivers of equipment-related revenue loss, powered by Raptor Maps’ ever-growing dataset of 125GW+ of analyzed PV systems. The depth of the dataset reveals unique insights into the health of solar assets globally.


In this year’s edition, we found:

  • Underperformance from equipment faults and related issues rose from 3.13% in 2022 to 4.47% in 2023

  • The resulting annual revenue loss is pegged at $4,696 per MWdc, which translates to $4.6B in annual revenue loss for the industry worldwide

  • At larger sites (100MWdc+), annual revenue loss averaged $5,000 per MWdc

The global solar market continued to boom in 2023, propelled by favorable legislation and decreases in the levelized cost of energy (LCOE) that greatly reduced development expenses. An estimated 413GW (source: BloombergNEF) of solar was installed globally in 2023, up 58% from the prior year, and solar comprises the vast majority of new generation capacity globally according to data from NREL. In the United States, utility-scale solar is expected to represent 58% of energy capacity additions in 2024—a record-setting 36.4GW of capacity. 


Solar’s extremely rapid growth, however, has led to numerous challenges that require bold innovations in solar operations and the adoption of emerging technology. Our analysis highlights the concerning trend of increasing underperformance due to asset health and equipment issues, resulting in up to $4.6 billion in annual revenue loss in 2023 for the entire industry. 


Addressing underperformance is critical for a financially successful and sustainable solar industry, requiring swift fault identification and resolution to minimize downtime and boost project returns. Tech-savvy owners and operators are increasingly turning to new technologies such as robotics and AI to help tackle these issues at scale. Succeeding in solar will increasingly require leveraging data derived from machine learning to optimize energy yield and margins.


Raptor Maps’ Global Solar Report quantifies and identifies leading drivers of equipment-related revenue loss, powered by Raptor Maps’ ever-growing dataset of 125GW+ of analyzed PV systems. The depth of the dataset reveals unique insights into the health of solar assets globally.


In this year’s edition, we found:

  • Underperformance from equipment faults and related issues rose from 3.13% in 2022 to 4.47% in 2023

  • The resulting annual revenue loss is pegged at $4,696 per MWdc, which translates to $4.6B in annual revenue loss for the industry worldwide

  • At larger sites (100MWdc+), annual revenue loss averaged $5,000 per MWdc

The global solar market continued to boom in 2023, propelled by favorable legislation and decreases in the levelized cost of energy (LCOE) that greatly reduced development expenses. An estimated 413GW (source: BloombergNEF) of solar was installed globally in 2023, up 58% from the prior year, and solar comprises the vast majority of new generation capacity globally according to data from NREL. In the United States, utility-scale solar is expected to represent 58% of energy capacity additions in 2024—a record-setting 36.4GW of capacity. 


Solar’s extremely rapid growth, however, has led to numerous challenges that require bold innovations in solar operations and the adoption of emerging technology. Our analysis highlights the concerning trend of increasing underperformance due to asset health and equipment issues, resulting in up to $4.6 billion in annual revenue loss in 2023 for the entire industry. 


Addressing underperformance is critical for a financially successful and sustainable solar industry, requiring swift fault identification and resolution to minimize downtime and boost project returns. Tech-savvy owners and operators are increasingly turning to new technologies such as robotics and AI to help tackle these issues at scale. Succeeding in solar will increasingly require leveraging data derived from machine learning to optimize energy yield and margins.


Raptor Maps’ Global Solar Report quantifies and identifies leading drivers of equipment-related revenue loss, powered by Raptor Maps’ ever-growing dataset of 125GW+ of analyzed PV systems. The depth of the dataset reveals unique insights into the health of solar assets globally.


In this year’s edition, we found:

  • Underperformance from equipment faults and related issues rose from 3.13% in 2022 to 4.47% in 2023

  • The resulting annual revenue loss is pegged at $4,696 per MWdc, which translates to $4.6B in annual revenue loss for the industry worldwide

  • At larger sites (100MWdc+), annual revenue loss averaged $5,000 per MWdc

The global solar market continued to boom in 2023, propelled by favorable legislation and decreases in the levelized cost of energy (LCOE) that greatly reduced development expenses. An estimated 413GW (source: BloombergNEF) of solar was installed globally in 2023, up 58% from the prior year, and solar comprises the vast majority of new generation capacity globally according to data from NREL. In the United States, utility-scale solar is expected to represent 58% of energy capacity additions in 2024—a record-setting 36.4GW of capacity. 


Solar’s extremely rapid growth, however, has led to numerous challenges that require bold innovations in solar operations and the adoption of emerging technology. Our analysis highlights the concerning trend of increasing underperformance due to asset health and equipment issues, resulting in up to $4.6 billion in annual revenue loss in 2023 for the entire industry. 


Addressing underperformance is critical for a financially successful and sustainable solar industry, requiring swift fault identification and resolution to minimize downtime and boost project returns. Tech-savvy owners and operators are increasingly turning to new technologies such as robotics and AI to help tackle these issues at scale. Succeeding in solar will increasingly require leveraging data derived from machine learning to optimize energy yield and margins.


Raptor Maps’ Global Solar Report quantifies and identifies leading drivers of equipment-related revenue loss, powered by Raptor Maps’ ever-growing dataset of 125GW+ of analyzed PV systems. The depth of the dataset reveals unique insights into the health of solar assets globally.


In this year’s edition, we found:

  • Underperformance from equipment faults and related issues rose from 3.13% in 2022 to 4.47% in 2023

  • The resulting annual revenue loss is pegged at $4,696 per MWdc, which translates to $4.6B in annual revenue loss for the industry worldwide

  • At larger sites (100MWdc+), annual revenue loss averaged $5,000 per MWdc

Experts from Raptor Maps will be discussing the findings live on our April webinar.

Experts from Raptor Maps will be discussing the findings live on our upcoming webinar on April 3rd.

Experts from Raptor Maps will be discussing the findings live on our April webinar.

Rising Underperformance

Rising Underperformance

Rising Underperformance

Rising Underperformance

Data sources

Data sources

The Global Solar Report is powered by Raptor Maps’ dataset of more than 125GW of PV systems, with 37GW from 41 countries in 2023 alone. The data is collected from a wide variety of sources, including drones & robotics, APIs, and Internet of Things (IoT) sensors, allowing Raptor Maps to derive unique information on the health of solar assets across the industry.

The Global Solar Report is powered by Raptor Maps’ dataset of more than 125GW of PV systems, with 37GW from 41 countries in 2023 alone. The data is collected from a wide variety of sources, including drones & robotics, APIs, and Internet of Things (IoT) sensors, allowing Raptor Maps to derive unique information on the health of solar assets across the industry.

The Global Solar Report is powered by Raptor Maps’ dataset of more than 125GW of PV systems, with 37GW from 41 countries in 2023 alone. The data is collected from a wide variety of sources, including drones & robotics, APIs, and Internet of Things (IoT) sensors, allowing Raptor Maps to derive unique information on the health of solar assets across the industry.

The Global Solar Report is powered by Raptor Maps’ dataset of more than 125GW of PV systems, with 37GW from 41 countries in 2023 alone. The data is collected from a wide variety of sources, including drones & robotics, APIs, and Internet of Things (IoT) sensors, allowing Raptor Maps to derive unique information on the health of solar assets across the industry.

underperformance by site size

underperformance by site size

The largest solar sites tend to exhibit higher average power loss, with sites between 100-200MWdc averaging 4.75% power loss and sites above 200MWdc averaging 4.91% power loss. And that power loss comes with a heavy financial burden: Sites above 100MWdc averaged $5,000 per MWdc+ in annual revenue loss, surpassing the global average of $4,696 per MWdc.
The largest solar sites tend to exhibit higher average power loss, with sites between 100-200MWdc averaging 4.75% power loss and sites above 200MWdc averaging 4.91% power loss. And that power loss comes with a heavy financial burden: Sites above 100MWdc averaged $5,000 per MWdc+ in annual revenue loss, surpassing the global average of $4,696 per MWdc.
The largest solar sites tend to exhibit higher average power loss, with sites between 100-200MWdc averaging 4.75% power loss and sites above 200MWdc averaging 4.91% power loss. And that power loss comes with a heavy financial burden: Sites above 100MWdc averaged $5,000 per MWdc+ in annual revenue loss, surpassing the global average of $4,696 per MWdc.
The largest solar sites tend to exhibit higher average power loss, with sites between 100-200MWdc averaging 4.75% power loss and sites above 200MWdc averaging 4.91% power loss. And that power loss comes with a heavy financial burden: Sites above 100MWdc averaged $5,000 per MWdc+ in annual revenue loss, surpassing the global average of $4,696 per MWdc.
$ per MW Annual Revenue Loss Range, by Project Size (MWdc)
$ per MW Annual Revenue Loss Range, by Project Size (MWdc)
$ per MW Annual Revenue Loss Range, by Project Size (MWdc)
Figure 2: 25th percentile, average, and 75th percentile of $ per MW annual loss
Although larger sites lose more revenue per MWdc due to underperformance, power loss has increased for every site size over the past 5 years.* Underperformance at small sites persists at high levels: In 2023, it was above 4% for all sites 50MWdc or smaller. The biggest increases in underperformance in the past year were seen at larger sites, with 100-200MWdc and 200+ MWdc sites seeing average year-over-year underperformance increases of 40% and 20%, respectively. 
Although larger sites lose more revenue per MWdc due to underperformance, power loss has increased for every site size over the past 5 years.* Underperformance at small sites persists at high levels: In 2023, it was above 4% for all sites 50MWdc or smaller. The biggest increases in underperformance in the past year were seen at larger sites, with 100-200MWdc and 200+ MWdc sites seeing average year-over-year underperformance increases of 40% and 20%, respectively. 
Although larger sites lose more revenue per MWdc due to underperformance, power loss has increased for every site size over the past 5 years.* Underperformance at small sites persists at high levels: In 2023, it was above 4% for all sites 50MWdc or smaller. The biggest increases in underperformance in the past year were seen at larger sites, with 100-200MWdc and 200+ MWdc sites seeing average year-over-year underperformance increases of 40% and 20%, respectively. 
Although larger sites lose more revenue per MWdc due to underperformance, power loss has increased for every site size over the past 5 years.* Underperformance at small sites persists at high levels: In 2023, it was above 4% for all sites 50MWdc or smaller. The biggest increases in underperformance in the past year were seen at larger sites, with 100-200MWdc and 200+ MWdc sites seeing average year-over-year underperformance increases of 40% and 20%, respectively. 
Avg. % Power Loss, by Year, by Project Size (MWdc)
Avg. % Power Loss, by Year, by Project Size (MWdc)
Avg. % Power Loss, by Year, by Project Size (MWdc)
Figure 3: Average equipment-driven underperformance year-over-year, by project size (MWdc)
Average underperformance observed by Raptor Maps has increased in line with the average size of new projects. In 2019, the average solar project size was 13.9MWdc, according to data from the Solar Energy Industries Association (SEIA) Major Projects List, which leaped to 59.6MWdc by 2023. An analysis of Raptor Maps data showed the average underperformance rate rising from 1.61% in 2019 to 4.47% in 2023. 
Average underperformance observed by Raptor Maps has increased in line with the average size of new projects. In 2019, the average solar project size was 13.9MWdc, according to data from the Solar Energy Industries Association (SEIA) Major Projects List, which leaped to 59.6MWdc by 2023. An analysis of Raptor Maps data showed the average underperformance rate rising from 1.61% in 2019 to 4.47% in 2023. 
Average underperformance observed by Raptor Maps has increased in line with the average size of new projects. In 2019, the average solar project size was 13.9MWdc, according to data from the Solar Energy Industries Association (SEIA) Major Projects List, which leaped to 59.6MWdc by 2023. An analysis of Raptor Maps data showed the average underperformance rate rising from 1.61% in 2019 to 4.47% in 2023. 
Average underperformance observed by Raptor Maps has increased in line with the average size of new projects. In 2019, the average solar project size was 13.9MWdc, according to data from the Solar Energy Industries Association (SEIA) Major Projects List, which leaped to 59.6MWdc by 2023. An analysis of Raptor Maps data showed the average underperformance rate rising from 1.61% in 2019 to 4.47% in 2023. 
Avg. Project Size in MWdc (SEIA Major Projects List) vs. Avg. % Power Loss (Raptor Maps)
Avg. Project Size in MWdc (SEIA Major Projects List) vs. Avg. % Power Loss (Raptor Maps)
Figure 4: Average equipment-driven underperformance year-over-year, by project size (MWdc)
There are likely a wide variety of factors at play that drive this trend, such as:

  • Increasing component quality issues, with 32% of bills of materials (BOMs) experiencing at least one failure in PVEL’s 2023 Scorecard
  • Velocity of solar deployment coupled with labor constraints potentially causing workmanship, QA/QC, and operational issues
  • Constraints in asset management and O&M as the number of sites increases without effective tools to manage and address issues at-scale

While there is a confluence of factors driving this increase, the continued rise in underperformance presents a significant challenge to the industry as more solar is deployed, both in project size and in the quantity of sites.
There are likely a wide variety of factors at play that drive this trend, such as:

  • Increasing component quality issues, with 32% of bills of materials (BOMs) experiencing at least one failure in PVEL’s 2023 Scorecard
  • Velocity of solar deployment coupled with labor constraints potentially causing workmanship, QA/QC, and operational issues
  • Constraints in asset management and O&M as the number of sites increases without effective tools to manage and address issues at-scale

While there is a confluence of factors driving this increase, the continued rise in underperformance presents a significant challenge to the industry as more solar is deployed, both in project size and in the quantity of sites.
There are likely a wide variety of factors at play that drive this trend, such as:

  • Increasing component quality issues, with 32% of bills of materials (BOMs) experiencing at least one failure in PVEL’s 2023 Scorecard
  • Velocity of solar deployment coupled with labor constraints potentially causing workmanship, QA/QC, and operational issues
  • Constraints in asset management and O&M as the number of sites increases without effective tools to manage and address issues at-scale

While there is a confluence of factors driving this increase, the continued rise in underperformance presents a significant challenge to the industry as more solar is deployed, both in project size and in the quantity of sites.
There are likely a wide variety of factors at play that drive this trend, such as:

  • Increasing component quality issues, with 32% of bills of materials (BOMs) experiencing at least one failure in PVEL’s 2023 Scorecard
  • Velocity of solar deployment coupled with labor constraints potentially causing workmanship, QA/QC, and operational issues
  • Constraints in asset management and O&M as the number of sites increases without effective tools to manage and address issues at-scale

While there is a confluence of factors driving this increase, the continued rise in underperformance presents a significant challenge to the industry as more solar is deployed, both in project size and in the quantity of sites.

O&M Inefficiencies Exacerbate Issues

O&M Inefficiencies Exacerbate Issues

Rising underperformance across sites of different sizes requires varied operational strategies. However, time wastage and inefficient resource allocation are a concern at solar sites of all sizes, whether utility-scale or C&I. In a 2023 survey conducted by Raptor Maps, many operators identified preventative maintenance visual inspections as a significant source of time wastage, and a large number of respondents also identified validating nuisance alarms as another way time is wasted
Rising underperformance across sites of different sizes requires varied operational strategies. However, time wastage and inefficient resource allocation are a concern at solar sites of all sizes, whether utility-scale or C&I. In a 2023 survey conducted by Raptor Maps, many operators identified preventative maintenance visual inspections as a significant source of time wastage, and a large number of respondents also identified validating nuisance alarms as another way time is wasted
Rising underperformance across sites of different sizes requires varied operational strategies. However, time wastage and inefficient resource allocation are a concern at solar sites of all sizes, whether utility-scale or C&I. In a 2023 survey conducted by Raptor Maps, many operators identified preventative maintenance visual inspections as a significant source of time wastage, and a large number of respondents also identified validating nuisance alarms as another way time is wasted
Rising underperformance across sites of different sizes requires varied operational strategies. However, time wastage and inefficient resource allocation are a concern at solar sites of all sizes, whether utility-scale or C&I. In a 2023 survey conducted by Raptor Maps, many operators identified preventative maintenance visual inspections as a significant source of time wastage, and a large number of respondents also identified validating nuisance alarms as another way time is wasted
These responses underscore an industry trend: with growing installed capacity and escalating operational challenges, operations teams must achieve more with fewer resources. In response to SEIA’s solar generation goal for 2030, which necessitates tripling the solar workforce, the adoption of AI and robotics for automation is essential. This technology will streamline workforce development, diminish or eradicate the necessity for manual labor outlined in the survey, and empower operations teams to focus on detailed tasks.
These responses underscore an industry trend: with growing installed capacity and escalating operational challenges, operations teams must achieve more with fewer resources. In response to SEIA’s solar generation goal for 2030, which necessitates tripling the solar workforce, the adoption of AI and robotics for automation is essential. This technology will streamline workforce development, diminish or eradicate the necessity for manual labor outlined in the survey, and empower operations teams to focus on detailed tasks.
These responses underscore an industry trend: with growing installed capacity and escalating operational challenges, operations teams must achieve more with fewer resources. In response to SEIA’s solar generation goal for 2030, which necessitates tripling the solar workforce, the adoption of AI and robotics for automation is essential. This technology will streamline workforce development, diminish or eradicate the necessity for manual labor outlined in the survey, and empower operations teams to focus on detailed tasks.
These responses underscore an industry trend: with growing installed capacity and escalating operational challenges, operations teams must achieve more with fewer resources. In response to SEIA’s solar generation goal for 2030, which necessitates tripling the solar workforce, the adoption of AI and robotics for automation is essential. This technology will streamline workforce development, diminish or eradicate the necessity for manual labor outlined in the survey, and empower operations teams to focus on detailed tasks.

AI and Robotics in Solar: "Drone-in-a-box"

AI and Robotics in Solar: "Drone-in-a-box"

Autonomous drones (“drone-in-a-box”) are a remotely operated robotics technology that automatically captures data on a solar farm in a repeatable way, including data for construction monitoring, DC health inspections, tracker misalignment, erosion, fencing, and substation inspections. As the robotics unit is installed on-site, it allows for rapid response and increased safety by eliminating the need to deploy a human pilot to assess and collect data. 


A recent study by Raptor Maps found: 


  • A 200MWdc site is expected to see a 7X+ return on investment annually, when utilizing the autonomous drone

  • That same site would see over $700K in increased production

  • Assuming remediation, financial benefits would extend to a reduction in labor costs and traditional data collection expenses


Autonomous drones solve operational challenges on utility-scale sites by allowing for the regular, rapid collection and analyses of data from large and complex installations. 

Autonomous drones (“drone-in-a-box”) are a remotely operated robotics technology that automatically captures data on a solar farm in a repeatable way, including data for construction monitoring, DC health inspections, tracker misalignment, erosion, fencing, and substation inspections. As the robotics unit is installed on-site, it allows for rapid response and increased safety by eliminating the need to deploy a human pilot to assess and collect data. 


A recent study by Raptor Maps found: 


  • A 200MWdc site is expected to see a 7X+ return on investment annually, when utilizing the autonomous drone

  • That same site would see over $700K in increased production

  • Assuming remediation, financial benefits would extend to a reduction in labor costs and traditional data collection expenses


Autonomous drones solve operational challenges on utility-scale sites by allowing for the regular, rapid collection and analyses of data from large and complex installations. 

Autonomous drones (“drone-in-a-box”) are a remotely operated robotics technology that automatically captures data on a solar farm in a repeatable way, including data for construction monitoring, DC health inspections, tracker misalignment, erosion, fencing, and substation inspections. As the robotics unit is installed on-site, it allows for rapid response and increased safety by eliminating the need to deploy a human pilot to assess and collect data. 


A recent study by Raptor Maps found: 


  • A 200MWdc site is expected to see a 7X+ return on investment annually, when utilizing the autonomous drone

  • That same site would see over $700K in increased production

  • Assuming remediation, financial benefits would extend to a reduction in labor costs and traditional data collection expenses


Autonomous drones solve operational challenges on utility-scale sites by allowing for the regular, rapid collection and analyses of data from large and complex installations. 

Autonomous drones (“drone-in-a-box”) are a remotely operated robotics technology that automatically captures data on a solar farm in a repeatable way, including data for construction monitoring, DC health inspections, tracker misalignment, erosion, fencing, and substation inspections. As the robotics unit is installed on-site, it allows for rapid response and increased safety by eliminating the need to deploy a human pilot to assess and collect data. 


A recent study by Raptor Maps found: 


  • A 200MWdc site is expected to see a 7X+ return on investment annually, when utilizing the autonomous drone

  • That same site would see over $700K in increased production

  • Assuming remediation, financial benefits would extend to a reduction in labor costs and traditional data collection expenses


Autonomous drones solve operational challenges on utility-scale sites by allowing for the regular, rapid collection and analyses of data from large and complex installations. 

AI and Robotics in Solar: Instant Inspections

AI and Robotics in Solar: Instant Inspections

For the C&I sector, AI-powered solutions can enable operators to get the most out of each truck roll and service more sites, allowing them to tackle the rising underperformance Raptor Maps has observed in the industry.


As an example, a C&I owner with a national portfolio deployed an AI solution from Raptor Maps that produces inspection analytics within 45-90 minutes of data capture directly to on-site techs, allowing them to address faults and other equipment issues as part of a regular O&M checklist


Same-day inspection results mean that remediation of physical damage can be fast-tracked: 


  • The rapid detection, categorization, and geotagging of high-priority thermal anomalies 

  • Fewer truck rolls to the same site are required

  • Proactive remediation reduces the risk of fire and other critical shutdowns


Streamlining mobilization allows for more sites to be serviced, helping reduce overall fire risk and improving project margins across the fleet.

For the C&I sector, AI-powered solutions can enable operators to get the most out of each truck roll and service more sites, allowing them to tackle the rising underperformance Raptor Maps has observed in the industry.


As an example, a C&I owner with a national portfolio deployed an AI solution from Raptor Maps that produces inspection analytics within 45-90 minutes of data capture directly to on-site techs, allowing them to address faults and other equipment issues as part of a regular O&M checklist


Same-day inspection results mean that remediation of physical damage can be fast-tracked: 


  • The rapid detection, categorization, and geotagging of high-priority thermal anomalies 

  • Fewer truck rolls to the same site are required

  • Proactive remediation reduces the risk of fire and other critical shutdowns


Streamlining mobilization allows for more sites to be serviced, helping reduce overall fire risk and improving project margins across the fleet.

For the C&I sector, AI-powered solutions can enable operators to get the most out of each truck roll and service more sites, allowing them to tackle the rising underperformance Raptor Maps has observed in the industry.


As an example, a C&I owner with a national portfolio deployed an AI solution from Raptor Maps that produces inspection analytics within 45-90 minutes of data capture directly to on-site techs, allowing them to address faults and other equipment issues as part of a regular O&M checklist


Same-day inspection results mean that remediation of physical damage can be fast-tracked: 


  • The rapid detection, categorization, and geotagging of high-priority thermal anomalies 

  • Fewer truck rolls to the same site are required

  • Proactive remediation reduces the risk of fire and other critical shutdowns


Streamlining mobilization allows for more sites to be serviced, helping reduce overall fire risk and improving project margins across the fleet.

For the C&I sector, AI-powered solutions can enable operators to get the most out of each truck roll and service more sites, allowing them to tackle the rising underperformance Raptor Maps has observed in the industry.


As an example, a C&I owner with a national portfolio deployed an AI solution from Raptor Maps that produces inspection analytics within 45-90 minutes of data capture directly to on-site techs, allowing them to address faults and other equipment issues as part of a regular O&M checklist


Same-day inspection results mean that remediation of physical damage can be fast-tracked: 


  • The rapid detection, categorization, and geotagging of high-priority thermal anomalies 

  • Fewer truck rolls to the same site are required

  • Proactive remediation reduces the risk of fire and other critical shutdowns


Streamlining mobilization allows for more sites to be serviced, helping reduce overall fire risk and improving project margins across the fleet.

underperformance by U.S. Region

underperformance by U.S. Region

Solar underperformance was most expensive along the East Coast, averaging $6,108 per MWdc in the Northeast and $5,784 in the Southeast. Regional variances likely account for some cost discrepancies: In the Midwest and Plains Area, which are particularly prone to damage from hail storms, power loss from physical damage was on average higher for these regions versus other regions, trailed closely by average power loss in the storm-prone Southeast. 
Solar underperformance was most expensive along the East Coast, averaging $6,108 per MWdc in the Northeast and $5,784 in the Southeast. Regional variances likely account for some cost discrepancies: In the Midwest and Plains Area, which are particularly prone to damage from hail storms, power loss from physical damage was on average higher for these regions versus other regions, trailed closely by average power loss in the storm-prone Southeast. 
Solar underperformance was most expensive along the East Coast, averaging $6,108 per MWdc in the Northeast and $5,784 in the Southeast. Regional variances likely account for some cost discrepancies: In the Midwest and Plains Area, which are particularly prone to damage from hail storms, power loss from physical damage was on average higher for these regions versus other regions, trailed closely by average power loss in the storm-prone Southeast. 
Solar underperformance was most expensive along the East Coast, averaging $6,108 per MWdc in the Northeast and $5,784 in the Southeast. Regional variances likely account for some cost discrepancies: In the Midwest and Plains Area, which are particularly prone to damage from hail storms, power loss from physical damage was on average higher for these regions versus other regions, trailed closely by average power loss in the storm-prone Southeast. 

Figure 5: Region-level detail on average $ per MWdc annual loss

Although there is a wide variance in underperformance by U.S. state, average solar equipment underperformance in the United States is higher than the global average, and states with the largest solar farms and where solar is growing the fastest–including Texas, California, and Florida–are all seeing high rates of underperformance. 
Although there is a wide variance in underperformance by U.S. state, average solar equipment underperformance in the United States is higher than the global average, and states with the largest solar farms and where solar is growing the fastest–including Texas, California, and Florida–are all seeing high rates of underperformance. 
Although there is a wide variance in underperformance by U.S. state, average solar equipment underperformance in the United States is higher than the global average, and states with the largest solar farms and where solar is growing the fastest–including Texas, California, and Florida–are all seeing high rates of underperformance. 
Although there is a wide variance in underperformance by U.S. state, average solar equipment underperformance in the United States is higher than the global average, and states with the largest solar farms and where solar is growing the fastest–including Texas, California, and Florida–are all seeing high rates of underperformance. 
Avg. % Power Loss by U.S. State
Top 3 Solar States (source: SEIA)
Avg. % Power Loss by U.S. State
Top 3 Solar States (source: SEIA)
Figure 6: Average % power loss by top 3 U.S. states, ordered by MW of solar deployed (source: SEIA)

Figure 6: Average % power loss by top 3 U.S. states, ordered by MW of solar deployed (source: SEIA)

Solar Spotlight: Texas

Solar Spotlight: Texas

Texas has been a hotbed of solar activity, with some of the largest new sites in the United States, but its rate of underperformance is also higher than other states and much higher than the global average. Larger solar sites, as found in Texas, are generally more prone to revenue loss due to equipment faults and downtime. 


Key stats: 


  • Texas had an average 5.84% power loss due to underperformance in 2023, a $6,141 per MWdc loss

  • There was, on average, 5.99% underperformance for 200+ MWdc sites in 2023, a $6,300 per MWdc loss 

  • Texas’ rate of underperformance was 33% higher than the global average

Texas has been a hotbed of solar activity, with some of the largest new sites in the United States, but its rate of underperformance is also higher than other states and much higher than the global average. Larger solar sites, as found in Texas, are generally more prone to revenue loss due to equipment faults and downtime. 


Key stats: 


  • Texas had an average 5.84% power loss due to underperformance in 2023, a $6,141 per MWdc loss

  • There was, on average, 5.99% underperformance for 200+ MWdc sites in 2023, a $6,300 per MWdc loss 

  • Texas’ rate of underperformance was 33% higher than the global average

Texas has been a hotbed of solar activity, with some of the largest new sites in the United States, but its rate of underperformance is also higher than other states and much higher than the global average. Larger solar sites, as found in Texas, are generally more prone to revenue loss due to equipment faults and downtime. 


Key stats: 


  • Texas had an average 5.84% power loss due to underperformance in 2023, a $6,141 per MWdc loss

  • There was, on average, 5.99% underperformance for 200+ MWdc sites in 2023, a $6,300 per MWdc loss 

  • Texas’ rate of underperformance was 33% higher than the global average

Texas has been a hotbed of solar activity, with some of the largest new sites in the United States, but its rate of underperformance is also higher than other states and much higher than the global average. Larger solar sites, as found in Texas, are generally more prone to revenue loss due to equipment faults and downtime. 


Key stats: 


  • Texas had an average 5.84% power loss due to underperformance in 2023, a $6,141 per MWdc loss

  • There was, on average, 5.99% underperformance for 200+ MWdc sites in 2023, a $6,300 per MWdc loss 

  • Texas’ rate of underperformance was 33% higher than the global average

There are currently 125 solar farms larger than 100MWac in Texas either in operation, under construction, or planned, according to SEIA’s major project list. In 2023, Texas became the state with the most utility-scale solar in operation. Roughly 50GW of solar is expected to be active in the state within the next 5 years.

There are currently 125 solar farms larger than 100MWac in Texas either in operation, under construction, or planned, according to SEIA’s major project list. In 2023, Texas became the state with the most utility-scale solar in operation. Roughly 50GW of solar is expected to be active in the state within the next 5 years.

There are currently 125 solar farms larger than 100MWac in Texas either in operation, under construction, or planned, according to SEIA’s major project list. In 2023, Texas became the state with the most utility-scale solar in operation. Roughly 50GW of solar is expected to be active in the state within the next 5 years.

There are currently 125 solar farms larger than 100MWac in Texas either in operation, under construction, or planned, according to SEIA’s major project list. In 2023, Texas became the state with the most utility-scale solar in operation. Roughly 50GW of solar is expected to be active in the state within the next 5 years.

Power Loss Drivers

Power Loss Drivers

Power Loss Drivers

Power Loss Drivers

Anomalies and faults covered

Anomalies and faults covered

Remediating drivers of power loss requires both a precise geolocation of the impacted module(s) and an accurate categorization of the fault requiring attention. All anomalies spotlighted in the Raptor Maps’ Global Report are detected, classified, verified, and tied to precise geolocations on digital twins of solar farms. While the following section provides a high-level view of trends, the Raptor Solar platform also provides granular historical, comparative views of each “digitized” farm.


For the purposes of the Global Report, similar anomaly types were grouped into categories, such as the “Diode” category for both “Diode” (typically impacting 1/3 of the module) and “Diode Multi” (typically impacting 2/3 of the module) anomalies.


Raptor Maps analytics also utilize temperature readings to provide further granularity into the anomaly (e.g. “Cell Medium” which classifies if the area of the cell anomaly is 10° - 20°C higher than adjacent areas). For more information on the definitions for the anomaly & fault categories, please reference the Appendix: Anomaly Categories section.

Remediating drivers of power loss requires both a precise geolocation of the impacted module(s) and an accurate categorization of the fault requiring attention. All anomalies spotlighted in the Raptor Maps’ Global Report are detected, classified, verified, and tied to precise geolocations on digital twins of solar farms. While the following section provides a high-level view of trends, the Raptor Solar platform also provides granular historical, comparative views of each “digitized” farm.


For the purposes of the Global Report, similar anomaly types were grouped into categories, such as the “Diode” category for both “Diode” (typically impacting 1/3 of the module) and “Diode Multi” (typically impacting 2/3 of the module) anomalies.


Raptor Maps analytics also utilize temperature readings to provide further granularity into the anomaly (e.g. “Cell Medium” which classifies if the area of the cell anomaly is 10° - 20°C higher than adjacent areas). For more information on the definitions for the anomaly & fault categories, please reference the Appendix: Anomaly Categories section.

Remediating drivers of power loss requires both a precise geolocation of the impacted module(s) and an accurate categorization of the fault requiring attention. All anomalies spotlighted in the Raptor Maps’ Global Report are detected, classified, verified, and tied to precise geolocations on digital twins of solar farms. While the following section provides a high-level view of trends, the Raptor Solar platform also provides granular historical, comparative views of each “digitized” farm.


For the purposes of the Global Report, similar anomaly types were grouped into categories, such as the “Diode” category for both “Diode” (typically impacting 1/3 of the module) and “Diode Multi” (typically impacting 2/3 of the module) anomalies.


Raptor Maps analytics also utilize temperature readings to provide further granularity into the anomaly (e.g. “Cell Medium” which classifies if the area of the cell anomaly is 10° - 20°C higher than adjacent areas). For more information on the definitions for the anomaly & fault categories, please reference the Appendix: Anomaly Categories section.

Remediating drivers of power loss requires both a precise geolocation of the impacted module(s) and an accurate categorization of the fault requiring attention. All anomalies spotlighted in the Raptor Maps’ Global Report are detected, classified, verified, and tied to precise geolocations on digital twins of solar farms. While the following section provides a high-level view of trends, the Raptor Solar platform also provides granular historical, comparative views of each “digitized” farm.


For the purposes of the Global Report, similar anomaly types were grouped into categories, such as the “Diode” category for both “Diode” (typically impacting 1/3 of the module) and “Diode Multi” (typically impacting 2/3 of the module) anomalies.


Raptor Maps analytics also utilize temperature readings to provide further granularity into the anomaly (e.g. “Cell Medium” which classifies if the area of the cell anomaly is 10° - 20°C higher than adjacent areas). For more information on the definitions for the anomaly & fault categories, please reference the Appendix: Anomaly Categories section.

Examples of Anomalies and Equipment Faults
Examples of Anomalies and Equipment Faults

Figure 7a: Examples of thermal anomalies, such as string outages, diodes, and hot spots resulting from vegetation

Figure 7b: Examples of equipment defects detected in RGB imagery, such as helix faults and misaligned trackers

what causes power loss?

what causes power loss?

System-level faults continue to be the largest drivers of power loss, with inverter faults, string outages (including underperforming strings), and combiner faults impacting the most power as a percentage of total power inspected at 1.91%, 0.90%, and 0.81% respectively. Additionally, instances of tracker issues have also risen—from .26% in 2022 to .46% in 2023—which indicates a broader spectrum of challenges affecting system performance.


In previous years, string-level outages were the leading power loss driver identified in the Raptor Maps dataset. Historically, inverter faults were easily detectable without thermographic inspections and were remediated upon observation but, in many cases, they no longer are. A variety of other factors could also be contributing to this increase, including labor constraints and supply chain challenges to procure spare parts.


Note: All Raptor Maps inspection results include granular categorization, but several anomalies are grouped under “Module” and “Other” for the purposes of this report. All module- and submodule-level anomalies (e.g. cell defects and cracking) are grouped into “Module” in this chart. “Other” anomalies include helix faults, reverse polarity, lightning damage, amongst several other anomaly types.

System-level faults continue to be the largest drivers of power loss, with inverter faults, string outages (including underperforming strings), and combiner faults impacting the most power as a percentage of total power inspected at 1.91%, 0.90%, and 0.81% respectively. Additionally, instances of tracker issues have also risen—from .26% in 2022 to .46% in 2023—which indicates a broader spectrum of challenges affecting system performance.


In previous years, string-level outages were the leading power loss driver identified in the Raptor Maps dataset. Historically, inverter faults were easily detectable without thermographic inspections and were remediated upon observation but, in many cases, they no longer are. A variety of other factors could also be contributing to this increase, including labor constraints and supply chain challenges to procure spare parts.


Note: All Raptor Maps inspection results include granular categorization, but several anomalies are grouped under “Module” and “Other” for the purposes of this report. All module- and submodule-level anomalies (e.g. cell defects and cracking) are grouped into “Module” in this chart. “Other” anomalies include helix faults, reverse polarity, lightning damage, amongst several other anomaly types.

System-level faults continue to be the largest drivers of power loss, with inverter faults, string outages (including underperforming strings), and combiner faults impacting the most power as a percentage of total power inspected at 1.91%, 0.90%, and 0.81% respectively. Additionally, instances of tracker issues have also risen—from .26% in 2022 to .46% in 2023—which indicates a broader spectrum of challenges affecting system performance.


In previous years, string-level outages were the leading power loss driver identified in the Raptor Maps dataset. Historically, inverter faults were easily detectable without thermographic inspections and were remediated upon observation but, in many cases, they no longer are. A variety of other factors could also be contributing to this increase, including labor constraints and supply chain challenges to procure spare parts.


Note: All Raptor Maps inspection results include granular categorization, but several anomalies are grouped under “Module” and “Other” for the purposes of this report. All module- and submodule-level anomalies (e.g. cell defects and cracking) are grouped into “Module” in this chart. “Other” anomalies include helix faults, reverse polarity, lightning damage, amongst several other anomaly types.

System-level faults continue to be the largest drivers of power loss, with inverter faults, string outages (including underperforming strings), and combiner faults impacting the most power as a percentage of total power inspected at 1.91%, 0.90%, and 0.81% respectively. Additionally, instances of tracker issues have also risen—from .26% in 2022 to .46% in 2023—which indicates a broader spectrum of challenges affecting system performance.


In previous years, string-level outages were the leading power loss driver identified in the Raptor Maps dataset. Historically, inverter faults were easily detectable without thermographic inspections and were remediated upon observation but, in many cases, they no longer are. A variety of other factors could also be contributing to this increase, including labor constraints and supply chain challenges to procure spare parts.


Note: All Raptor Maps inspection results include granular categorization, but several anomalies are grouped under “Module” and “Other” for the purposes of this report. All module- and submodule-level anomalies (e.g. cell defects and cracking) are grouped into “Module” in this chart. “Other” anomalies include helix faults, reverse polarity, lightning damage, amongst several other anomaly types.

Avg. Power Loss Breakdown by Anomaly Category (% power loss)
Avg. Power Loss Breakdown by Anomaly Category (% power loss)
Figure 8: Breakdown of global power loss average by anomaly category

Figure 8: Breakdown of global power loss average by anomaly category

Figure 8: Breakdown of global power loss average by anomaly category

Module or submodule-level faults generally contribute less to immediate power loss observed during inspections, and module-level issues causing power loss stayed relatively steady year-over-year, decreasing by 12% to 0.21% of total power inspected. 


However, module-level issues can have more significant repercussions, potentially affecting entire strings or inverter blocks. As climate change progresses, the frequency and intensity of extreme weather phenomena are expected to increase, leading to equipment issues such as module cracking or flooding on site. According to a recent IEEE study, the median annual power loss attributed to weather events is approximately 1%, but there are instances where extreme weather conditions result in losses of up to 60%.

Module or submodule-level faults generally contribute less to immediate power loss observed during inspections, and module-level issues causing power loss stayed relatively steady year-over-year, decreasing by 12% to 0.21% of total power inspected. 


However, module-level issues can have more significant repercussions, potentially affecting entire strings or inverter blocks. As climate change progresses, the frequency and intensity of extreme weather phenomena are expected to increase, leading to equipment issues such as module cracking or flooding on site. According to a recent IEEE study, the median annual power loss attributed to weather events is approximately 1%, but there are instances where extreme weather conditions result in losses of up to 60%.

Module or submodule-level faults generally contribute less to immediate power loss observed during inspections, and module-level issues causing power loss stayed relatively steady year-over-year, decreasing by 12% to 0.21% of total power inspected. 


However, module-level issues can have more significant repercussions, potentially affecting entire strings or inverter blocks. As climate change progresses, the frequency and intensity of extreme weather phenomena are expected to increase, leading to equipment issues such as module cracking or flooding on site. According to a recent IEEE study, the median annual power loss attributed to weather events is approximately 1%, but there are instances where extreme weather conditions result in losses of up to 60%.

Module or submodule-level faults generally contribute less to immediate power loss observed during inspections, and module-level issues causing power loss stayed relatively steady year-over-year, decreasing by 12% to 0.21% of total power inspected. 


However, module-level issues can have more significant repercussions, potentially affecting entire strings or inverter blocks. As climate change progresses, the frequency and intensity of extreme weather phenomena are expected to increase, leading to equipment issues such as module cracking or flooding on site. According to a recent IEEE study, the median annual power loss attributed to weather events is approximately 1%, but there are instances where extreme weather conditions result in losses of up to 60%.

Distribution of Annual Power Loss % by Weather Event
Distribution of Annual Power Loss % by Weather Event
Annual Power Loss by Weather Event

Figure 9: Annual power loss % distribution by weather event (Source: IEEE)

Highlighting the necessity of regular asset health assessments, the same IEEE study reveals potential longer-term production impacts on sites. Following a hail storm, for instance, the median performance loss ratio increased across hail size categories larger than 25 mm, indicating sustained damage from weather events. This underscores the critical importance of operational resilience and the accurate measurement of performance and asset health both pre- and post-weather events. 


Such measures are essential for ensuring prompt remediation, efficient insurance claims processing, and careful management of the enduring effects of severe weather events. Particularly in regions prone to extreme weather with significant PV capacity, such as Texas, emphasizing proactive management will become increasingly vital.

Highlighting the necessity of regular asset health assessments, the same IEEE study reveals potential longer-term production impacts on sites. Following a hail storm, for instance, the median performance loss ratio increased across hail size categories larger than 25 mm, indicating sustained damage from weather events. This underscores the critical importance of operational resilience and the accurate measurement of performance and asset health both pre- and post-weather events. 


Such measures are essential for ensuring prompt remediation, efficient insurance claims processing, and careful management of the enduring effects of severe weather events. Particularly in regions prone to extreme weather with significant PV capacity, such as Texas, emphasizing proactive management will become increasingly vital.

Highlighting the necessity of regular asset health assessments, the same IEEE study reveals potential longer-term production impacts on sites. Following a hail storm, for instance, the median performance loss ratio increased across hail size categories larger than 25 mm, indicating sustained damage from weather events. This underscores the critical importance of operational resilience and the accurate measurement of performance and asset health both pre- and post-weather events. 


Such measures are essential for ensuring prompt remediation, efficient insurance claims processing, and careful management of the enduring effects of severe weather events. Particularly in regions prone to extreme weather with significant PV capacity, such as Texas, emphasizing proactive management will become increasingly vital.

Highlighting the necessity of regular asset health assessments, the same IEEE study reveals potential longer-term production impacts on sites. Following a hail storm, for instance, the median performance loss ratio increased across hail size categories larger than 25 mm, indicating sustained damage from weather events. This underscores the critical importance of operational resilience and the accurate measurement of performance and asset health both pre- and post-weather events. 


Such measures are essential for ensuring prompt remediation, efficient insurance claims processing, and careful management of the enduring effects of severe weather events. Particularly in regions prone to extreme weather with significant PV capacity, such as Texas, emphasizing proactive management will become increasingly vital.

Module Tech Deep Dive

Module Tech Deep Dive

Module Tech Deep Dive

Module Tech Deep Dive

When considering PV system health by module technology, systems built with thin-film modules exhibited less power loss in 2023 than monocrystalline- or polycrystalline-based systems—under 4% for thin-film based systems, on average. The type of PV module, however, does not necessarily correlate with underperforming behavior, and the variance is likely explained by a multitude of factors, such as site age, operational strategies, geography-specific challenges, and more. 

When considering PV system health by module technology, systems built with thin-film modules exhibited less power loss in 2023 than monocrystalline- or polycrystalline-based systems—under 4% for thin-film based systems, on average. The type of PV module, however, does not necessarily correlate with underperforming behavior, and the variance is likely explained by a multitude of factors, such as site age, operational strategies, geography-specific challenges, and more. 

When considering PV system health by module technology, systems built with thin-film modules exhibited less power loss in 2023 than monocrystalline- or polycrystalline-based systems—under 4% for thin-film based systems, on average. The type of PV module, however, does not necessarily correlate with underperforming behavior, and the variance is likely explained by a multitude of factors, such as site age, operational strategies, geography-specific challenges, and more. 

When considering PV system health by module technology, systems built with thin-film modules exhibited less power loss in 2023 than monocrystalline- or polycrystalline-based systems—under 4% for thin-film based systems, on average. The type of PV module, however, does not necessarily correlate with underperforming behavior, and the variance is likely explained by a multitude of factors, such as site age, operational strategies, geography-specific challenges, and more. 

Avg. Power Loss (%) by PV Technology
Avg. Power Loss (%) by PV Technology
Figure 10: System-level (e.g. inverter or string defects) vs. module-level (e.g. diode, hot spot, or cracking), by PV technology

Figure 10: System-level (e.g. inverter or string defects) vs. module-level (e.g. diode, hot spot, or cracking), by PV technology

Figure 10: System-level (e.g. inverter or string defects) vs. module-level (e.g. diode, hot spot, or cracking), by PV technology

When examining defects where the impact is confined to individual modules, thin-film modules exhibited a lower average power loss from such defects but showed a higher incidence of physical damage: Twice as many incidences of physical damage as polycrystalline and 3.5 times more likely than monocrystalline modules. 


The increased occurrence of module-level anomalies in polycrystalline modules correlates with the average age of modules in the 2023 dataset. Thin-film farms averaged 1.5 years, monocrystalline averaged 1.6 years, and polycrystalline averaged 3.2 years, suggesting a potential relationship between module age and defect occurrence.

When examining defects where the impact is confined to individual modules, thin-film modules exhibited a lower average power loss from such defects but showed a higher incidence of physical damage: Twice as many incidences of physical damage as polycrystalline and 3.5 times more likely than monocrystalline modules. 


The increased occurrence of module-level anomalies in polycrystalline modules correlates with the average age of modules in the 2023 dataset. Thin-film farms averaged 1.5 years, monocrystalline averaged 1.6 years, and polycrystalline averaged 3.2 years, suggesting a potential relationship between module age and defect occurrence.

When examining defects where the impact is confined to individual modules, thin-film modules exhibited a lower average power loss from such defects but showed a higher incidence of physical damage: Twice as many incidences of physical damage as polycrystalline and 3.5 times more likely than monocrystalline modules. 


The increased occurrence of module-level anomalies in polycrystalline modules correlates with the average age of modules in the 2023 dataset. Thin-film farms averaged 1.5 years, monocrystalline averaged 1.6 years, and polycrystalline averaged 3.2 years, suggesting a potential relationship between module age and defect occurrence.

When examining defects where the impact is confined to individual modules, thin-film modules exhibited a lower average power loss from such defects but showed a higher incidence of physical damage: Twice as many incidences of physical damage as polycrystalline and 3.5 times more likely than monocrystalline modules. 


The increased occurrence of module-level anomalies in polycrystalline modules correlates with the average age of modules in the 2023 dataset. Thin-film farms averaged 1.5 years, monocrystalline averaged 1.6 years, and polycrystalline averaged 3.2 years, suggesting a potential relationship between module age and defect occurrence.

% of Modules with Module-Level Anomalies
% of Modules with Module-Level Anomalies
Figure 11: Module-level anomalies (e.g. cell defects), by PV technology, by year

Figure 11: Module-level anomalies (e.g. cell defects), by PV technology, by year

Figure 11: Module-level anomalies (e.g. cell defects), by PV technology, by year

Figure 12: Module- (and sub-module) level anomalies by PV technology

Figure 12: Module- (and sub-module) level anomalies by PV technology

Figure 12: Module- (and sub-module) level defects by PV technology (2023 dataset)
Figure 12: Most common module- (and sub-module) level defects by PV technology

Figure 12: Module- (and sub-module) level anomalies by PV technology

In regions susceptible to extreme weather events, like Texas in the Plains area, higher rates of physical damage to modules were observed. 


Workmanship issues during installation can also be another factor driving physical damage, underscoring the importance for developers and asset owners to maintain comprehensive documentation before transitioning to the operations team. As the equipment landscape continues to evolve, the work required during installation is becoming more varied with new technology and component models. There could be conflicting information on how different components interact with each other during installation, and system designs could also overlook incompatibilities in “minor” equipment (e.g. MC4 connectors) that can lead to additional failures and power loss later in the project’s lifecycle.


As the solar workforce attracts non-solar talent and grows, standardized best practices and training would greatly benefit the industry. 

In regions susceptible to extreme weather events, like Texas in the Plains area, higher rates of physical damage to modules were observed. 


Workmanship issues during installation can also be another factor driving physical damage, underscoring the importance for developers and asset owners to maintain comprehensive documentation before transitioning to the operations team. As the equipment landscape continues to evolve, the work required during installation is becoming more varied with new technology and component models. There could be conflicting information on how different components interact with each other during installation, and system designs could also overlook incompatibilities in “minor” equipment (e.g. MC4 connectors) that can lead to additional failures and power loss later in the project’s lifecycle.


As the solar workforce attracts non-solar talent and grows, standardized best practices and training would greatly benefit the industry. 

In regions susceptible to extreme weather events, like Texas in the Plains area, higher rates of physical damage to modules were observed. 


Workmanship issues during installation can also be another factor driving physical damage, underscoring the importance for developers and asset owners to maintain comprehensive documentation before transitioning to the operations team. As the equipment landscape continues to evolve, the work required during installation is becoming more varied with new technology and component models. There could be conflicting information on how different components interact with each other during installation, and system designs could also overlook incompatibilities in “minor” equipment (e.g. MC4 connectors) that can lead to additional failures and power loss later in the project’s lifecycle.


As the solar workforce attracts non-solar talent and grows, standardized best practices and training would greatly benefit the industry. 

In regions susceptible to extreme weather events, like Texas in the Plains area, higher rates of physical damage to modules were observed. 


Workmanship issues during installation can also be another factor driving physical damage, underscoring the importance for developers and asset owners to maintain comprehensive documentation before transitioning to the operations team. As the equipment landscape continues to evolve, the work required during installation is becoming more varied with new technology and component models. There could be conflicting information on how different components interact with each other during installation, and system designs could also overlook incompatibilities in “minor” equipment (e.g. MC4 connectors) that can lead to additional failures and power loss later in the project’s lifecycle.


As the solar workforce attracts non-solar talent and grows, standardized best practices and training would greatly benefit the industry. 

% of Observed Modules with Physical Damage
% of Observed Modules with Physical Damage
Figure 12: Physical damage detected by U.S. region, by PV Technology

Figure 12: Physical damage detected by U.S. region, by PV Technology

Figure 12: Physical damage detected by U.S. region, by PV Technology

Appendix

Appendix

Appendix

Equipment fault and anomaly definitions can be found here.

More details on data collection and analysis levels can be found here.

Interested in discussing the results?

Interested in discussing the results?

© 2023 Raptor Maps, Inc.

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Somerville, MA 02143

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© 2023 Raptor Maps, Inc.

444 Somerville Ave.
Somerville, MA 02143

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© 2023 Raptor Maps, Inc.

444 Somerville Ave.
Somerville, MA 02143

Stay Up to Date

Subscribe to our newsletter and stay informed about innovations in solar asset optimization, deploying robotics for solar, our research and testing with OEMs, the latest in our product development, and more.

© 2023 Raptor Maps, Inc.

444 Somerville Ave.
Somerville, MA 02143

Stay Up to Date

Subscribe to our newsletter and stay informed about innovations in solar asset optimization, deploying robotics for solar, our research and testing with OEMs, the latest in our product development, and more.