Reliability Analysis of Residential Photovoltaic Systems Across Five Climatic Zones: Performance, Degradation, and Fault Trends

Abstract

This study analyses the long-term reliability of 100 residential PV systems monitored for 7–11 years across 33 countries and five climate zones. System performance was evaluated using the temperature-corrected performance ratio (${PR}_{Tcorr}$), following IEC 61724-1, while annual degradation rates were calculated using RdTools, which separates long-term trends from interannual variability. Inverter and MPPT reliability were assessed through a defined fault-likelihood metric, representing the percentage of time the measured AC or DC power fell more than 15% below the manufacturer-reported expected power for at least two consecutive intervals. The results show a strong association between climate conditions and system performance. Mediterranean climates record the highest ${PR}_{Tcorr}$ (0.84 ± 0.02), while desert regions show the lowest values (0.73 ± 0.04). Annual degradation rates range from −0.69 ± 0.15%/year in Mediterranean climates to −3.13 ± 0.64%/year in desert climates. Inverter fault likelihood is highest in desert regions (4.05%) and lowest in temperate climates (1.5%), while MPPT fault likelihood ranges from 10.25% in desert zones to 5.4% in Mediterranean zones. By integrating ${PR}_{Tcorr}$, long-term degradation trends, and inverter/MPPT fault behavior within a cross-climate framework, the study provides an evidence-based understanding of how environmental stressors shape PV system reliability.

1. Introduction

Photovoltaic systems are a cornerstone of the renewable energy transition, providing sustainable and clean electricity for residential, commercial, and utility-scale applications. Their widespread adoption across diverse geographical regions has made it imperative to evaluate their performance and reliability under varying climatic and environmental conditions. However, achieving consistently high efficiency and operational stability remains a challenge due to the influence of local factors such as irradiance, temperature, and maintenance practices [1]. These variations necessitate a detailed understanding of the key drivers affecting PV system performance and reliability globally.

The performance ratio (PR) is a critical metric used to assess PV system efficiency, indicating the ratio of actual energy output to the theoretical maximum [2]. PR is influenced by several factors, including module technology, temperature, irradiance, and system configuration [3]. Climates such as Mediterranean regions often report higher PRs due to moderate temperatures and stable irradiance [4]. In contrast, desert climates experience lower PR [5], often due to significant soiling and extreme thermal conditions. Understanding the global variation in PR provides valuable insights into optimizing system design for diverse environments.

Degradation is a key factor impacting the long-term viability of PV systems, representing the annual decline in energy output [2,6]. Environmental factors such as extreme temperatures, humidity, and soiling play a significant role in accelerating degradation, particularly in harsh climates like deserts [7] and tropics [8]. Reliable estimation tools, such as RdTools (version 3.0.0) [9], enable the quantification of degradation rates with uncertainties, providing essential data for evaluating the economic and operational lifespans of PV installations. Accurately capturing these trends is vital for stakeholders to prioritize investments in durable technologies and tailored maintenance practices.

The reliability of PV systems relies on the stability and efficiency of components, particularly inverters and Maximum Power Point Tracking (MPPT) device [10], which are crucial for achieving optimal energy conversion and overall system performance. Environmental stressors such as thermal cycling, shading, soiling, and humidity-induced corrosion often contribute to system inefficiencies and operational downtime [11,12]. Regions with frequent shading events, such as tropical climates, face challenges in MPPT performance due to rapid fluctuations in irradiance, which hinder the ability of MPPT devices to continuously optimize power output [13].

Despite the critical role of MPPT devices in PV system operation, there is a notable lack of research focusing on their reliability. A limited number of studies, such as ref. [14], have explored the lifespan and operational stability of MPPT devices, emphasizing the need for enhanced tracking algorithms and hardware improvements to mitigate thermal stress and extend device longevity. These findings underscore the importance of addressing MPPT reliability as part of a broader effort to improve PV system uptime and reduce energy losses under diverse environmental conditions. Expanding this area of research is essential for optimizing system designs and operational strategies tailored to specific climates and site conditions.

Inverters are another critical component in PV systems, playing a vital role in converting DC power generated by PV modules into usable AC electricity. However, their reliability is often challenged by environmental and operational stressors. Recent studies have highlighted the significant impact of thermal cycling, high irradiance, and humidity on inverter lifetimes. For example, as reported in ref. [15], inverters operating under desert-like conditions experience accelerated wear due to prolonged exposure to extreme temperatures and fine dust particles, which can infiltrate and degrade internal components. Further analysis by [16] underscores the need for robust multilevel inverter designs in grid-connected PV systems. It reveals that while advancements in inverter technology have enhanced power quality and reduced harmonic distortion, key components such as IGBT (Insulated Gate Bipolar Transistor) switches remain vulnerable to mission-profile-induced stresses. These components often dictate overall system reliability, particularly in environments with high thermal variability.

Additionally, ref. [17] provides a comparative reliability assessment of inverters in bifacial PV systems, revealing the compounded thermal and electrical stresses imposed on inverters by these high-yield systems. This study emphasizes the critical role of mission-profile-based reliability analysis to predict inverter performance under diverse climatic conditions.

Residential PV systems often experience higher operating temperatures and larger diurnal temperature variations compared to free-standing utility-scale PV plants due to restricted airflow, especially in building-applied or building-integrated configurations. In turn, this can lead to accelerated material degradation and increased power loss rates. In addition, shading events occur more frequently due to the presence of nearby buildings and shading objects like chimneys and dormers, putting additional strain on MPPT devices and increasing the risk for hotspot formation.

While several studies have examined aspects of PV degradation, performance trends, and component reliability, the literature on MPPT and inverter failures remains relatively limited and fragmented. Existing works typically focus on specific case studies, short monitoring periods, or single-climate analyses, which makes it difficult to generalize failure behavior across regions. Consequently, there is a lack of long-term, multi-climate datasets that jointly assess performance, degradation rates, and fault likelihoods at a global scale.

This study examines a dataset of 100 residential PV systems equipped with SolarEdge monitoring, each providing at least seven years of operational data between 2014 and 2024. The systems span 33 countries and five major climate zones, enabling a consistent cross-climate comparison. The analysis is structured around three complementary axes: (i) evaluation of long-term performance using temperature-corrected performance ratio; (ii) estimation of annual degradation rates using the RdTools framework; and (iii) assessment of inverter and MPPT fault likelihood across different climatic conditions. While the study places particular emphasis on power electronics reliability, we also acknowledge that PV module degradation, arising from microcracks, hotspots, potential-induced degradation (PID), delamination, and other defect mechanisms, plays an equally important role in overall system reliability, especially as modules are directly exposed to environmental stressors such as thermal cycling, humidity, and soiling. By integrating performance, degradation, and failure metrics within a unified comparative framework, this work aims to provide insight into how climatic and operational conditions jointly influence long-term PV reliability.

2. Methodology

2.1. Data Sources and Monitoring Platform

To ensure a robust and standardized dataset for analyzing the reliability of PV systems, data for this study was sourced from the SolarEdge monitoring platform. SolarEdge is a globally recognized provider of PV inverters and advanced energy monitoring solutions, offering a comprehensive database of real-time and historical performance metrics from PV systems worldwide.

This paper analyses operational data from 100 residential PV systems spanning 33 countries, detailed in

Table 1. Distribution of PV systems studied in this work across 33 countries in five climate zones.

Climate	Country (N° of PV Systems)	Total N° of PV Systems
Desert	Egypt (3), Algeria (3), Morocco (4), Saudi Arabia (3), United Arabe Emirates (2)	15
Mediterranean	Portugal (2), Spain (3), Italy (2), Greece (2)	9
Subtropical	Mexico (2), Argentina (2), South Africa (4), Turkey (3), Pakistan (2), Thailand (2), Australia (6)	21
Temperate	United States (6), Canada (2), United Kingdom (3), Denmark (3), Germany (3), Poland (2), Japan (2), New Zealand (2), Russia (2)	25
Tropical	Brazil (7), Colombia (2) Kenya (2), Philippines (2), Vietnam (2) India (5), Chile (2), China (8)	30

and Figure 1. PV systems were selected according to three criteria: (i) a minimum of seven consecutive years of monitoring data; (ii) absence of major undocumented system modifications; and (iii) sufficient data completeness (>95%) after preprocessing. Systems with persistent shading issues, missing metadata, or evidence of prolonged outages were excluded to minimize selection bias.

Furthermore, the countries were selected based on their PV market maturity, geographic and climatic diversity (tropical, subtropical, Mediterranean, temperate, and desert climates), and the availability of reliable monitoring data through the SolarEdge platform. The dataset includes systems operating under extreme environmental conditions, such as desert climates in Saudi Arabia, high humidity in the Philippines, and temperate climates such as Germany. The diversity of these environmental contexts ensures that the study reflects a broad range of real-world operational conditions.

The dataset covers a minimum of 7 years of continuous operation for all examined PV systems, with some systems having up to 11 years of operational data (2014–2024). This temporal scope enables a robust long-term analysis of reliability, performance trends, and degradation rates. The studied PV systems range in capacity from 3.6 kW to 18.6 kW, with an average capacity of approximately 6.2 kW, representing typical residential-scale installations. All systems are installed with tilts ranging from 22° to a maximum of 38°, reflecting typical residential and small-scale PV setups. All systems utilize polycrystalline silicon PV modules, a widely used technology known for its low degradation rates (typically 0.5–1% per year [18]). This standardization ensures comparability of performance metrics across different regions and climates. In this study, ‘nominal power’ refers specifically to the inverter AC rating as reported by the SolarEdge monitoring platform, rather than the DC peak capacity of the PV array.

Climate-zone assignments were based on the Köppen–Geiger classification map. In addition to climatic conditions, the built environment itself also affects PV operating temperatures and long-term system reliability. Systems installed on single-family homes typically benefit from greater rooftop ventilation, lower thermal mass, and reduced anthropogenic heat release. In contrast, PV installations in high-density residential areas often experience elevated rooftop temperatures due to reduced airflow, extensive impervious surfaces, and the local enhancement of urban heat island effects. Although our dataset includes systems from a range of residential building forms, these micro-scale variations in urban morphology may help explain some of the intra-zone variability observed in the fault likelihood and degradation results.

SolarEdge monitoring platform provides high-resolution data (summarized in

Table 2. Key parameters (dataset) retired from the SolarEdge platform.

Parameter	Unit	Sampling Frequency	Use in Analysis
DC and AC Power output	W	15 min/hourly	PR, RdTools
Irradiance (POA)	W/m2	Hourly	PR, RdTools normalization
Ambient Temperature	°C	Hourly	PR correction
Module Temperature (if available)	°C	Hourly	PR correction
Inverter Status/Events	-	Event-based	Fault likelihood
Energy yield (daily/monthly)	kWh	Daily/Monthly	Performance trends

), including key parameters such as hourly, daily, and monthly energy yields, average hourly solar irradiance (not including soiling or shading perception), and hourly average ambient temperatures. This wealth of information facilitates a comprehensive analysis of system reliability, enabling us to investigate the performance degradation and identify trends in the examined PV systems efficiency and failure rates. While the SolarEdge monitoring platform provides high-resolution data, its proprietary algorithms and measurement standards may introduce biases that limit generalizability.

To prepare the dataset for analysis, several preprocessing steps were undertaken. Abnormal data points arising from logging errors, communication interruptions, or maintenance activities were identified using interquartile range (IQR) filtering and cross-checked against operator maintenance logs. In total, between 0.8% and 2.3% of daily data points per system were flagged as outliers and removed. Missing data occurred mainly in short gaps (typically 1–3 days) caused by communication dropouts. These gaps represented less than 3–5% of the total dataset for most systems. Short gaps were reconstructed using linear interpolation, while longer gaps (rare cases exceeding 7 days) were reconstructed using nearest-neighbor trends from the same season in adjacent years. Validation using complete datasets from comparable PV installations in each climate zone confirmed that these corrections did not materially influence the calculated performance ratios, degradation rates, or fault likelihoods.

The data used in this study complies with ethical considerations and platform terms and conditions, ensuring the responsible handling of proprietary information. All data were anonymized, and no sensitive or personally identifiable information was included in the analysis. This approach ensures that the study adheres to ethical standards while providing valuable insights into the reliability of PV systems worldwide.

2.2. Analysis of Performance Ratio and Degradation Rates

PR is a critical metric for evaluating the operational efficiency of PV systems. It provides insights into system performance by normalizing energy output against irradiance levels, ensuring meaningful comparisons across systems and conditions. To improve accuracy, this study employs temperature-corrected PR (${PR}_{Tcorr}$) according to the IEC 61724-1 [19], which accounts for temperature variations that affect PV module energy output and calculated using Equation (1).

$${PR}_{Tcorr}={\displaystyle \frac{{\sum }_k{P}_{out,k}/P_{STC}}{{\sum }_K[\frac{G_{POA,k}}{G_{STC}}·(1+\gamma ·(T_{mod,k}-T_{mod,ref}))]}}$$

(1)

where $P_{out,k}$ is the output power of the PV system at time step $k$ (in W), $P_{STC}$ is the nominal power of the PV system at standard test conditions (STC), $G_{POA,k}$ is the plane of array irradiance at time step $k$ (in W/m²), $G_{STC}$ is the irradiance at STC (1000 W/m²), $\gamma$ is the temperature coefficient of power, $T_{mod,k}$ represents the PV module temperature at time step $k$ (in °C), and $T_{mod,ref}$ is the reference PV module temperature at STC (25 °C). Reference irradiance ($G_{STC}$) and module temperature coefficients were taken from manufacturer datasheets and cross-checked with system specifications provided in the SolarEdge platform to ensure consistent PR calculation across sites.

The temperature correction follows the IEC 61724-1:2021 standard [20], incorporating the module temperature coefficient ($\gamma$), a parameter typically provided by the manufacturer or derived from time-series data. This adjustment aligns PR calculations with real-world environmental conditions, reducing errors caused by temperature-induced seasonal variations and ensuring consistent assessments across different climates. In this work, the temperature coefficients were sourced from manufacturer datasheets for each system to ensure accuracy across diverse module types and climates.

The temperature correction involves estimating the module temperature at each interval and comparing it to a reference temperature, typically the STC temperature of 25 °C. Using high temporal resolution data (e.g., sub-daily), this methodology ensures that temperature effects on performance are precisely quantified, minimizing inaccuracies due to data averaging. By applying this approach, the adjusted ${PR}_{Tcorr}$ provides a more accurate reflection of system efficiency under varying environmental conditions, offering critical insights into performance trends, degradation rates.

To evaluate the long-term reliability of PV systems, this study also investigates degradation rates, a key parameter for assessing the annual reduction in energy output due to material aging, environmental stressors, and operational inefficiencies. The degradation analysis utilizes the RdTools framework, an open-source software developed by the National Renewable Energy Laboratory (NREL) [9], which is widely recognized for its standardized and reproducible methodologies. RdTools processes high-resolution operational data, including daily DC power output, solar irradiance, and temperature, to derive degradation rates with precision.

The RdTools workflow begins with preprocessing the raw time-series data to filter outliers and fill gaps. The data are then normalized using clear-sky irradiance and the temperature-corrected performance ratio (${PR}_{Tcorr}$). This ensures consistency across all systems and observation periods. In addition, RdTools incorporates statistical filters and uncertainty modeling that explicitly separate long-term degradation signals from interannual variability, ensuring that short-term weather anomalies do not bias the estimated degradation rates. Incomplete calendar years (e.g., partial installation years) were excluded from the Year-on-Year (YoY) regression in accordance with RdTools recommendations. Outliers within the YoY distribution were removed using the built-in robust regression filter. For each system, RdTools also provides ±1σ confidence intervals.

Degradation rates are determined using YoY linear regression applied to the ${PR}_{Tcorr}$, which isolates true performance trends from environmental and seasonal fluctuations. The annual degradation rate ($Rd$) is calculated as the change in ${PR}_{Tcorr}$ over the observation period, expressed as Equation (2).

$$Rd={\displaystyle \frac{∆{PR}_{Tcorr}}{∆t}}$$

(2)

where $Rd$ represents the annual degradation rate (in %/year), $∆{PR}_{Tcorr}$ is the change in temperature-corrected performance ratio over the observation period, and $∆t$ is the total observation period (in years).

The full analytical workflow, spanning data acquisition, temperature-corrected PR computation, degradation estimation with RdTools, and inverter/MPPT fault likelihood assessment, was standardized across all 100 systems. These steps enabled a consistent evaluation of long-term performance and reliability while minimizing the influence of data gaps, outliers, and climatic variability. Finally, all metrics were integrated into a comparative cross-climate framework to assess how environmental conditions shape PV system behavior across the five Köppen–Geiger climate zones. A visual summary of the complete methodological pipeline is presented in Figure 2.

3. Results and Discussion

3.1. Performance Ratio Analysis

The analysis of PR across different climate zones, as shown in Figure 3, reveals significant variability influenced by environmental and operational factors. PV systems in Mediterranean climates achieved the highest average PR, with a mean of 0.84 and a standard deviation (std) of 0.02. This superior performance can be attributed to consistent solar irradiance levels, minimal seasonal variations, and moderate temperatures that limit thermal losses. For instance, PV systems in Italy and Greece benefit from optimal rooftop installations with favorable tilt angles, resulting in enhanced energy yields and minimal shading.

In temperate regions, the average PR was slightly lower at 0.82 (std = 0.02). Reduced solar irradiance during winter months due to shorter daylight hours and frequent cloud cover impacts energy production. However, moderate temperatures in these regions help mitigate thermal degradation, maintaining relatively stable performance. Examples include systems in Germany and the UK, where frequent cloud cover reduces available irradiance [21], impacting energy production. While PR calculations aim to correct for irradiance variability, shading effects can introduce discrepancies between measured and actual irradiance, influencing PR evaluation. Additionally, partial shading can affect inverter efficiency and MPPT performance, further contributing to variations in PR.

Subtropical zones displayed an average PR of 0.80 (std = 0.03), reflecting a balance between high irradiance and occasional overcast conditions. Systems in Mexico and Argentina experience slightly reduced PRs due to increased temperatures, which exacerbate thermal losses [22]. However, these systems still outperform tropical regions, where high humidity and frequent rainfall contribute to soiling and dirt accumulation on panels [8]. For example, PV systems in Brazil and the Philippines, operating in tropical climates, face such challenges despite the abundant solar resource, resulting in a lower average PR of 0.76 (std = 0.03).

Desert climates reported the lowest average PR (mean = 0.73, std = 0.04), with variability attributed to dust accumulation, frequent sandstorms, and extreme temperatures contributing to significant thermal losses. These challenges necessitate frequent cleaning schedules to restore panel efficiency. Systems in Algeria and Saudi Arabia exemplify the struggles faced by desert PV installations, where soiling and overheating are significant factors in reducing energy output despite high solar irradiance [5,7].

Figure 4 highlights the performance trends for the best-performing PV system in Italy and the worst-performing system in Egypt. The Italian PV system, a 4.6 kW residential rooftop installation located in Rome, demonstrates consistent high performance. With a tilt angle of 13 degrees, the system benefits from a Mediterranean climate, characterized by stable irradiance and limited seasonal fluctuations. Starting at a PR of 0.87 in 2016, the system exhibits a gradual decline to 0.82 in 2024, corresponding to a degradation rate of approximately 0.6% per year. This is typical for polycrystalline silicon PV modules and reflects aging rather than operational inefficiencies.

While climatic drivers explain a large proportion of the observed differences in PR, module-level factors such as microcracks, hotspots, delamination, and encapsulant browning are also known to influence long-term energy yield. These intrinsic degradation mechanisms may contribute to part of the intra-zone PR variability observed in Figure 2 and Figure 3, particularly in older systems or those exposed to mechanical stress. Although module defects are beyond the scope of the present dataset, acknowledging these effects provides a more complete interpretation of the observed trends.

3.2. PV Systems Inverter and MPPT Faults Trends

The inverter fault likelihood (%) reflects the probability of system inefficiencies or downtime caused by inverter-related issues, such as power loss, hardware failure, or performance delays. This also accounts for delayed inverter startup mechanisms, where the inverter experiences significant delays in commencing operations due to internal degradation of power electronic components or thermal stress [15,16]. Such delays can compromise energy capture during periods of peak irradiance, further affecting overall system performance.

The MPPT fault likelihood (%) is a measure of inefficiencies arising from suboptimal tracking of the PV system’s maximum power point. This was quantified by comparing actual power output data from each PV system, as measured through the SolarEdge monitoring platform, against the theoretical predicted power output under ideal operating conditions. Deviations between these values were used to determine MPPT inefficiencies, factoring in issues such as shading, rapid irradiance fluctuations, and hardware or algorithmic limitations [14]. This approach ensures a robust evaluation of MPPT performance relative to expected system behavior under varying environmental conditions.

A deviation was classified as a fault when the measured AC power dropped more than 15% below the expected power derived from SolarEdge’s performance model for at least two consecutive sampling intervals. A 3-interval median filter was applied to avoid transient spikes. Periods affected by shading events, grid curtailment, or known external disturbances (as indicated in the event logs) were excluded to ensure that only genuine technical faults (inverter or MPPT related) contributed to the fault-likelihood metric.

Additionally, it is important to note that the “expected power” used for MPPT fault identification is not generated by an independent model developed in this study. Instead, this value is directly provided by the SolarEdge monitoring platform, which computes it using proprietary performance algorithms based on irradiance, temperature, and system specifications. Our analysis therefore relies on this manufacturer-provided benchmark, and the deviations used to quantify MPPT inefficiency reflect departures from SolarEdge’s predicted performance.

The analysis of inverter fault likelihood shown in Figure 5 highlights significant differences across climate zones, influenced by environmental and operational factors. Desert regions, including Saudi Arabia, United Arab Emirates, Morocco, Egypt, and Algeria, exhibit the highest median inverter fault likelihood of 4.05%, primarily due to extreme temperatures and dust accumulation. These harsh conditions increase thermal stress and lead to the degradation of power electronic components, such as capacitors and semiconductors, which can delay inverter startup and reduce overall efficiency.

Tropical regions, such as Brazil, Colombia, and the Philippines, follow with a median fault likelihood of 2.1%, driven by high humidity and frequent temperature fluctuations that accelerate the aging of electronic components. However, the higher levels of ambient moisture in tropical climates pose less severe thermal cycling compared to desert regions.

Subtropical regions, including Mexico, Argentina, and Thailand, exhibit a median inverter fault likelihood of 2.5%, reflecting a balance between moderate irradiance conditions and localized environmental stressors. Temperate regions, including the United States, Germany, and New Zealand, consistently demonstrate one of the lowest inverter fault likelihoods, with a median of 1.5%. These regions benefit from moderate temperatures and reduce thermal cycling, which minimizes stress on power electronics and ensures higher inverter reliability. Occasional extreme weather events, such as snow or severe storms in temperate countries like Poland and Denmark, may cause slight variations but do not significantly impact overall fault rates [23]. Mediterranean regions, such as Spain, Italy, and Greece, exhibit slightly higher inverter fault likelihoods with a median of 1.6%, reflecting the impact of warmer summer temperatures combined with moderate dust accumulation [24,25,26].

The results of Figure 5 reveal notable variations in MPPT fault likelihood across different climate zones. Desert regions, including Saudi Arabia, United Arab Emirates, and Morocco, exhibit the highest median MPPT fault likelihood of 10.25%, driven by soiling caused by persistent dust accumulation and minimal rainfall [27,28]. These environmental challenges significantly hinder MPPT performance, as the MPPT struggles to maintain optimal power tracking under reduced irradiance and high temperatures. Mitigating MPPT inefficiencies in desert climates requires enhanced tracking algorithms designed to compensate for rapid irradiance fluctuations and frequent cleaning schedules to address soiling.

Tropical regions, such as Brazil, Colombia, and the Philippines, report the second-highest MPPT fault likelihood, with a median of 9.2%. These inefficiencies stem from frequent shading caused by cloud cover and vegetation, as well as rapid irradiance fluctuations, particularly in densely forested or equatorial areas. In contrast, subtropical regions, such as Mexico, Argentina, and Thailand, demonstrate moderate MPPT fault likelihoods of 6.3%, benefiting from relatively predictable irradiance levels but still experiencing localized shading challenges. Temperate regions, including the United States, Germany, and New Zealand, show improved MPPT performance with a median fault likelihood of 5.7%, benefiting from relatively moderate and stable irradiance levels. However, occasional extreme weather events, such as overcast conditions or snow, and seasonal shading from vegetation slightly affect performance.

Mediterranean regions, including Spain, Italy, and Greece, exhibit the lowest MPPT fault likelihood, with a median of 5.4%, owing to favorable climatic conditions and stable solar resource availability. These conditions help maintain optimal MPPT performance, as there is less fluctuation in irradiance levels and minimal shading effects.

Table 3. Summary of median inverter and MPPT fault likelihoods (%) across different climate zones.

Climate Zone	Median Inverter Fault Likelihood (%)	Median MPPT Fault Likelihood (%)
Desert	4.05	10.25
Mediterranean	1.6	5.4
Subtropical	2.5	6.3
Temperate	1.5	5.7
Tropical	2.1	9.2

summarizes the variations in faulty likelihoods across different climate zones. Desert regions exhibit the highest fault likelihoods, with 4.05% for inverters and 10.25% for MPPT systems, driven by extreme heat and soiling effects. Tropical climates follow with notable MPPT fault likelihoods (9.2%) due to rapid irradiance fluctuations, while subtropical zones exhibit moderate MPPT fault likelihoods (6.3%), benefiting from relatively stable environmental conditions. Temperate and Mediterranean regions show low inverter fault likelihoods (1.5% and 1.6%, respectively), with Mediterranean regions demonstrating the best MPPT performance (5.4%) due to consistent solar resource availability.

Additionally, Figure 6 presents the relationship between inverter and MPPT faults across the five climatic zones, with a regression trend line applied to all countries studied. The United Arab Emirates exhibits the highest overall correlation, with elevated fault rates for both inverters and MPPT systems, reflecting the extreme environmental conditions in desert climates. Morocco shows substantial MPPT failures, aligning with the challenges posed by high temperatures and dust accumulation. Colombia, despite its low inverter fault rates, experiences relatively high MPPT failures, potentially influenced by system design and maintenance factors. In contrast, Canada records the lowest inverter faults, benefiting from a temperate climate with minimal thermal stress. The observed fault trends underscore the significant impact of environmental conditions on system reliability, reinforcing the need for climate-specific mitigation strategies.

Although inverter and MPPT behavior plays a central role in short-term system reliability, PV modules themselves remain a major source of long-term failures. Field studies consistently show that modules experience the highest exposure to thermal cycling, humidity ingress, ultraviolet (UV) stress, mechanical loading, and soiling abrasion. These cumulative stresses influence both power electronics performance and degradation behavior. While module-specific fault data were not available in this study, their contribution to overall system reliability should be recognized, particularly when interpreting climates with elevated degradation rates.

To assess statistical differences in fault likelihood between the five climate zones, non-parametric statistical tests were performed because several variables did not follow a normal distribution. A Kruskal–Wallis H-test was applied to compare inverter and MPPT fault likelihood across the five groups. The H statistic represents the degree to which the distributions differ between groups after ranking all samples, with higher values indicating larger separation. The accompanying p-value reflects the probability that the observed differences arise by chance; values below 0.05 indicate statistically significant differences between at least one pair of climate zones.

The results presented in

Table 4. Statistical significance of inverter and MPPT fault likelihood differences between climate zones.

Metric	H Statistic	p-Value	Effect Size	Significant Differences (Dunn Test, p < 0.05)
Inverter Faults (%)	19.1626	0.00073	0.5415	Desert > Mediterranean (p = 0.0366); Desert > Temperate (p = 0.000572)
MPPT Faults (%)	19.7784	0.000552	0.5635	Desert > Subtropical (p = 0.0281); Desert > Mediterranean (p = 0.00479); Desert > Temperate (p = 0.0153)

showed significant differences for both inverter fault likelihood (H = 19.16, p = 0.00073) and MPPT fault likelihood (H = 19.78, p = 0.00055). Effect sizes were large (η² = 0.54 and 0.56), indicating that climate zone is a major explanatory factor. Post hoc pairwise comparisons were performed using Dunn’s test with Bonferroni correction. The desert climate exhibited significantly higher inverter fault likelihood than the Mediterranean (p = 0.0366) and temperate (p = 0.00057) zones, and significantly higher MPPT fault likelihood than the subtropical (p = 0.0281), Mediterranean (p = 0.00479), and temperate (p = 0.0153) zones. These statistical results reinforce the reliability trends observed in the empirical analysis.

3.3. PV Systems Degradation Rate

The distribution of median degradation rates for the studied PV systems, as analyzed using RdTools, is shown in Figure 7. These degradation rates reflect the annual performance decline of PV systems due to environmental stressors, material aging, and operational inefficiencies. Similar degradation trends have been documented in prior field-based studies [29,30,31,32]. The inclusion of uncertainties adds robustness to the analysis by accounting for variations in measurement precision across different regions. Uncertainties stem from measurement errors in irradiance and temperature sensors, temporal gaps in data, and variability in local environmental conditions such as shading and soiling.

Countries in desert climates, such as Algeria (−2.23 ± 0.25%/year), Morocco (−2.67 ± 0.33%/year), and Egypt (−3.13 ± 0.64%/year), exhibit the highest degradation rates. These regions are characterized by extreme irradiance, intense heat, and significant soiling, all of which accelerate material wear and operational challenges. For instance, heavy dust accumulation in these environments exacerbates thermal stress on modules, reducing their reliability over time.

In tropical climates, degradation rates, while lower than in desert regions, remain significant. Countries such as Brazil (−1.63 ± 0.22%/year), Colombia (−1.06 ± 0.11%/year), and Vietnam (−1.27 ± 0.25%/year) exhibit moderate declines. This is primarily due to high humidity, frequent rainfall, and rapid temperature fluctuations, which contribute to material degradation and electrical inefficiencies.

Temperate regions, including countries like the United Kingdom (−0.77 ± 0.21%/year), Germany (−0.69 ± 0.11%/year), and Denmark (−0.81 ± 0.13%/year), report some of the lowest degradation rates. The moderate climates in these regions, characterized by reduced thermal cycling, lower irradiance, and minimal soiling, help preserve PV module performance over time. These findings reinforce the observation that milder climates enhance system longevity and reduce wear on critical components.

Subtropical and Mediterranean regions demonstrate intermediate degradation rates. For instance, subtropical countries such as Mexico (−1.12 ± 0.13%/year) and Turkey (−0.93 ± 0.27%/year) exhibit rates reflecting moderate environmental stressors, including higher temperatures and some soiling. Similarly, Mediterranean countries like Italy (−0.73 ± 0.18%/year) and Spain (−0.69 ± 0.15%/year) benefit from relatively stable climates but still face occasional stressors such as windborne particulates and local soiling.

Interestingly, Australia (−0.83 ± 0.17%/year) demonstrates a degradation rate similar to temperate regions, likely due to the widespread adoption of advanced PV technologies designed to withstand environmental challenges. In contrast, countries with diverse climatic zones, such as China (−1.15 ± 0.09%/year) and India (−1.74 ± 0.33%/year), report higher degradation rates. These variations stem from the presence of harsher conditions in certain regions, such as deserts and tropical zones, contributing to overall higher declines.

The analysis highlights a clear correlation between climate conditions and PV system degradation rates. Desert and tropical zones, characterized by extreme environmental stressors such as high irradiance, elevated temperatures, and humidity, exhibit the highest degradation rates. In contrast, Temperate and Mediterranean zones, which benefit from milder weather and reduced thermal cycling, report the most stable performance. However, subtropical regions such as Mexico and Turkey, while generally more moderate, still show degradation influenced by localized environmental factors. The addition of uncertainties underscores the variability within these regions, reflecting localized influences like soiling, shading, and extreme weather events.

3.4. Comparative Analysis, Discussion, Limitations and Future Perspectives

The degradation rates obtained in this study are summarized in

Table 5. Comparative analysis of PV systems degradation rates across different countries with reference to previous studies.

Country [Ref.]	Degradation Rate (%/year)
	Prior Studies	This Work
Algeria [5]	−0.88 to −2.44	−2.23 ± 0.25
Morrocco [7]	−1.45 to −3.41	−2.67 ± 0.33
India [13]	−0.6 to −5.0	−1.74 ± 0.33
UK [21]	−1.05 to −1.16	−0.77 ± 0.21
Brazil [8]	−2.3 to −3.7	−1.63 ± 0.22
Mexico [22]	−1.4 to −1.5	−1.12 ± 0.13
Poland [23]	Averaged at −3.0	−0.95 ± 0.13
Spain [24]	Averaged at −1.3	−0.69 ± 0.15
Italy [25]	Averaged at −0.8	−0.73 ± 0.18
Greece [26]	−1.0 to −4.0	−1.09 ± 0.14
Egypt [33]	−0.76 to −4.39	−3.13 ± 0.64

, showing clear climate-dependent trends. Desert climates, such as Algeria (−2.23 ± 0.25%/year), Morocco (−2.67 ± 0.33%/year), and Egypt (−3.13 ± 0.64%/year), exhibit the highest degradation rates due to extreme irradiance, high temperatures, and severe soiling effects. Tropical regions, including Brazil (−1.63 ± 0.22%/year) and India (−1.74 ± 0.33%/year), show moderately high degradation, influenced by high humidity and frequent rainfall, though soiling impact is lower. In contrast, temperate regions like the UK (−0.77 ± 0.21%/year) and Poland (−0.95 ± 0.13%/year) display significantly lower degradation rates, benefiting from reduced thermal stress. Mediterranean climates, such as Spain (−0.69 ± 0.15%/year) and Italy (−0.73 ± 0.18%/year), align closely with prior research, with stable irradiance mitigating excessive module wear. These findings reinforce the strong correlation between climate conditions and PV degradation trends, where harsher environments accelerate degradation, while moderate climates have a stable long-term performance.

Overall, the comparative analysis highlights the critical influence of local climatic conditions on PV module longevity and performance. Regions with high temperatures and irradiance experience accelerated degradation, while moderate climates provide more favorable operating conditions that enhance PV system reliability.

It is important here to note that while the climate-dependent patterns observed are clear, the interpretation is made cautiously, acknowledging that climatic influences operate alongside design-specific and operational factors. Therefore, statements regarding climate impacts are framed as tendencies rather than deterministic relationships, aligning the discussion with established findings in the degradation and inverter reliability literature.

While our study categorizes systems using broad Köppen-style climate zones, the underlying climatic parameters, such as module operating temperature, daily irradiance variability, humidity, wind speed, and airborne particulate load, have a direct influence on PV performance, MPPT behavior, and long-term degradation. Recent climate research shows that substantial spatial and temporal variability exists within global climate categories. For example, ref. [34] demonstrate that Köppen climate zones have shifted measurably since the 1980s due to anthropogenic warming, with arid and tropical zones expanding into mid-latitudes. Such transitions reflect underlying changes in temperature patterns, hydrological cycles, and atmospheric moisture, all of which are known to affect PV module aging and inverter reliability.

Moreover, urban-scale studies highlight that local microclimates may deviate significantly from regional climate classifications. The Local Climate Zone (LCZ) framework [35] provides evidence that built environment characteristics, such as building density, surface imperviousness, and vegetation cover, strongly influence land surface temperature and thermal regulation. These localized effects can produce surface urban heat islands (SUHIs) that elevate PV module operating temperatures by up to several degrees, increasing thermal stress on inverters and accelerating degradation. Consequently, PV systems installed in dense urban areas within the same global climate zone may experience similar operating conditions to systems located in traditional warmer zones. This context helps explain some of the intra-zone variability observed in our dataset and highlights the importance of microclimatic factors when assessing PV reliability.

While the ideal scenario for isolating climatic effects would involve analyzing the same PV system design deployed across multiple climate zones, such datasets are not typically available for residential installations. The systems included in this study therefore reflect normal market diversity in terms of module technology, inverter models, mounting configurations, and system ages. These factors can introduce additional variability in performance and degradation. However, because our dataset spans over 100 PV systems distributed across 33 countries, the effect of individual system differences is statistically diluted. More importantly, the magnitude of the climate-driven patterns observed, for example, the consistently lower performance ratios and higher degradation in desert climates compared with temperate and Mediterranean climates, exceeds the typical performance variation caused by system-specific characteristics. Thus, although system heterogeneity introduces some noise, it does not alter the overall conclusions on the dominant role of climatic conditions in long-term PV performance and reliability.

Although the results presented in this study provide a comprehensive assessment of PR, fault likelihood, and degradation across diverse climatic zones, several limitations should be acknowledged. First, all systems analyzed originate from a single monitoring platform (SolarEdge), which introduces dependencies on manufacturer-defined metrics such as expected power and fault categorization, potentially limiting comparability with installations using alternative inverter technologies or performance-modeling approaches. Second, the five broad Köppen–Geiger climate categories used here do not fully capture microclimatic variability within regions, which may lead to wider intra-zone dispersion than the classification suggests. Third, while system-level performance and degradation metrics provide valuable long-term insights, they do not incorporate module-specific defect data such as microcracks, hotspots, PID or encapsulant aging, mechanisms extensively documented in international reliability studies by national renewable energy laboratory (NREL) and IEA PVPS Task 13 as major contributors to accelerated degradation and field underperformance [36,37]. Incorporating such defect-level metrics, along with improved KPI-quality routines and contextual performance evaluation as recommended by recent PV reliability analyses [38,39], would enable a more complete and mechanistic understanding of PV system aging. Future work combining system-level monitoring with physical inspection data (e.g., outdoor electroluminescent imaging [40]) and defect-oriented reliability frameworks would therefore significantly strengthen long-term PV reliability assessment across climatic zones.

Future research should therefore include PV systems from multiple inverter manufacturers, integrate imaging-based defect data, and employ finer-resolution climate classification to better represent local conditions. Combining PV system-level performance indicators with module-level diagnostics would provide a more holistic and physically grounded understanding of long-term PV reliability.

4. Conclusions

This study provides a comprehensive assessment of the long-term performance and reliability of 100 residential PV systems monitored between 2014 and 2024 across five climate zones. The results show clear, climate-dependent patterns in key operational metrics. Temperature-corrected PR values range from an average of 0.84 in Mediterranean climates to 0.73 in desert regions, reflecting the combined influence of irradiance conditions, temperature, and soiling. Annual degradation rates calculated with RdTools also vary markedly, from approximately −0.69% ± 0.15%/year in Mediterranean climates to −3.13% ± 0.64%/year in desert climates, demonstrating the role of high temperatures, soiling, and humidity in accelerating module aging. Fault-likelihood analysis further confirms that harsh environments increase operational stress: desert systems exhibit median inverter and MPPT fault likelihoods of 4.05% and 10.25%, respectively, whereas temperate and Mediterranean regions show substantially lower values, corresponding to more favorable thermal and environmental conditions.

The findings translate into several practical implications for PV system design, operation, and maintenance. In desert climates, where systems face the highest thermal loading and soiling rates, measures such as improved inverter heat-dissipation design, dust-resistant hardware, and steeper module tilt angles can help mitigate degradation and reduce MPPT instability. Tropical regions would benefit from corrosion-resistant components and MPPT algorithms capable of responding rapidly to cloud-induced irradiance fluctuations. In Mediterranean climates, where performance is more stable, targeted management of seasonal soiling and summer heat remains essential to maintain high annual yields. Temperate regions require attention to winter-related challenges, including snow shedding strategies, moisture protection, and inverters capable of efficient low-irradiance tracking. For subtropical climates, a combination of moisture protection, thermal management, and periodic inspection for humidity-driven failure modes is recommended. These climate-specific operational insights can support improved operation and maintenance (O&M) planning, more accurate warranty expectations, and region-adapted repowering strategies.

While the analysis provides valuable evidence of climate-driven performance differences, several limitations should be acknowledged. All systems in this study rely on a single monitoring platform (SolarEdge), which constrains comparability with other inverter manufacturers and proprietary algorithms, particularly in the case of expected-power and fault-detection metrics. The five-zone climate classification, although useful for global comparison, does not capture the finer microclimatic variations that can influence module temperature, soiling, and humidity exposure. Finally, the study does not incorporate module-level defect data (e.g., microcracks, hotspots, delamination), which are known to contribute significantly to long-term degradation.

Future work should therefore extend the analysis to include systems from multiple manufacturers and monitoring platforms, integrate inspection-derived module defects (e.g., thermography or electroluminescent imaging), and utilize finer-resolution climatic datasets to better represent local environmental conditions. These steps would enhance the robustness of climate-specific reliability assessments and support the development of more comprehensive predictive models for long-term PV system performance.