Evaluation of Solar Energy Performance in Green Buildings Using PVsyst: Focus on Panel Orientation and Efficiency

Abstract

This study explores the optimization of solar energy harvesting in Truro City in the UK using PVSyst simulations integrated with real-time meteorological data. Focusing on panel orientation, tilt angle, shading, and albedo, the research aimed to enhance both energy efficiency and economic viability of photovoltaic (PV) systems in green buildings. A 100 kWp rooftop solar installation served as the case study. Energy outputs derived from spreadsheet-based models and PVSyst simulations were compared to validate results. Optimal tilt angles were identified between 35° and 39°, and the azimuth angle of 0° yielded the highest energy gain without requiring solar tracking. Fixed configurations with a 5 m pitch showed only a 10% shading loss, requiring 1680 m² of space and generating an average of 646.83 kWh/m² monthly. Compared to recent works, our integration of real-time climate data improved simulation accuracy by 6–9%, refining operational planning and decision-making processes. This included better timing of high-load activities and enhanced prediction for grid feedback. The study demonstrates that data-driven optimization significantly improves performance reliability and system design, offering practical insights for solar infrastructure in similar temperate climates. These results provide a benchmark for urban energy planners seeking to balance performance and spatial constraints in PV deployment.

1. Introduction

The transition to sustainable energy systems is a pivotal goal in the global response to climate change, and integrating solar photovoltaic (PV) technologies into green buildings plays a central role in this transformation. Solar energy systems harness abundant and renewable sunlight to produce electricity, significantly reducing dependence on fossil fuels and lowering greenhouse gas emissions [1,2]. Photovoltaic panels convert solar radiation into usable energy, offering a decentralized and clean power source that enhances the energy autonomy and carbon performance of buildings [3,4]. These systems can be incorporated seamlessly into architectural structures, such as rooftops and façades, enabling optimized exposure to sunlight and supporting sustainable urban development [5].

The effective deployment of solar PV systems in buildings relies on rigorous simulation and design practices. Modeling tools such as PVsyst, SAM (System Advisor Model), and Helioscope have become indispensable for evaluating system feasibility and performance under varying environmental and architectural conditions [6]. These simulation tools enable designers to analyze key technical and economic parameters, including solar irradiance, shading, orientation, and tilt, thereby maximizing system efficiency and financial returns [7,8,9]. For instance, Saikia et al. [7] demonstrated how PVsyst could accurately assess the energy potential and cost-effectiveness of a 40 kW off-grid solar microgrid, tailored to meet specific load demands at an institutional site. Similarly, Mishra et al. [8] validated PVsyst’s predictive power by comparing simulation results with actual performance metrics of a 5 MW rooftop PV system, affirming the model’s utility for real-world system design. Mubarak et al. [9] extended the application of PVsyst to large-scale grid-connected systems, simulating a 400 MW solar farm in Riyadh and confirming the tool’s capability to optimize component sizing and performance across diverse PV module types.

The foundation of green technology is rooted in reducing environmental impact through sustainable design and energy efficiency. Angshuman Khan et al. [10] emphasize the concept of “greening” telecommunication, outlining eco-friendly equipment, energy-efficient architectures, and waste reduction strategies that can be extended to energy infrastructures like PV systems. The integration of green principles into electronic systems—including power amplifiers and antennas—sets a precedent for optimizing energy use across sectors, including smart buildings. The evolution of solar cell technologies has further strengthened the role of PV systems in the renewable energy transition. Khan et al. [11] provide a comprehensive review of solar cell development, from crystalline silicon to quantum dot technologies, highlighting trends and challenges in material science and efficiency enhancement. This evolutionary insight is essential for understanding the trajectory of PV system applications in building environments. Moreover, Khan et al. [12] categorize solar technologies into three generations, examining their material composition, cost-performance trade-offs, and applicability in developing countries. Their work underscores a growing need for economically viable yet efficient solar energy solutions, particularly in environments with fluctuating resource availability or limited grid access. However, these studies often lack a focus on simulation-based, real-world applications of PV systems tailored to specific climatic conditions.

Understanding environmental inputs is crucial to simulating reliable solar outputs. Figure 1 illustrates the trends in annual PV capacity deployment in the U.S. (2011–2022), showing a dominant role for utility-scale projects in driving national capacity growth [13]. Such data underscore the importance of system size and scale in policy-driven solar adoption. Meanwhile, the accurate assessment of solar irradiance and weather variability remains central to optimizing system output. Localized irradiance profiles, influenced by daily and seasonal solar angles, are foundational in selecting suitable configurations for maximizing energy harvest [14,15].

Figure 2 presents the more recent modeled annual technical generation potential of utility-scale PV systems across the United States, based on an updatable and data-driven modeling framework. It demonstrates the significant regional variation in solar energy potential, highlighting states like Texas, California, and Arizona as top contributors. The data consider system performance, solar resource availability, land use, and environmental constraints, offering an upper bound for possible generation. This figure emphasizes the strategic importance of PV system application and optimization in national energy planning. By visualizing the scalable and location-specific potential of solar power, it underscores the role of advanced simulation tools and real-time data integration in enhancing the effectiveness and reliability of PV deployment across diverse climatic and geographic contexts.

Orientation and tilt angles play a similarly vital role. Simulations must consider geographic latitude, structural limitations, and seasonal sun paths to determine the optimal azimuth and inclination for the PV modules. Research by Mohamed et al. [16] emphasizes that even small deviations in tilt can significantly alter energy yield; their study on bifacial modules in Malaysia found an optimal tilt angle of 5°, enabling enhanced energy production and lower levelized electricity costs. Studies by others [17,18] reinforce that both fixed and adaptive tilts, if optimized, can dramatically improve performance, particularly in off-grid or under-electrified regions like Mali, where Ali [18] showed that properly aligned standalone systems could sustain healthcare infrastructure.

Shading remains a critical issue. Simulation platforms evaluate shading from surrounding buildings, vegetation, and seasonal sun trajectories to predict output loss and guide strategic placement [19]. Meanwhile, component specifications—including PV module efficiency, inverter selection, and temperature coefficients—must be modeled accurately to estimate the system’s actual yield [20]. Similarly, building load profiles must be integrated into the simulation to align generation with demand, ensuring economic and operational efficiency [21].

Economic and financial modeling is indispensable for decision-making. Factors such as capital expenditure, maintenance costs, subsidies, and return on investment determine the project’s viability. Studies [22,23,24] explore how these economic inputs, when incorporated into simulation tools, guide stakeholders in assessing payback periods and long-term benefits. For example, El-Shimy [22] highlighted the importance of life-cycle cost analysis in system selection, while Kumar et al. [23] provided a methodology for integrating feed-in tariffs and policy incentives into financial feasibility modeling. Abid et al. [24] demonstrated that economic optimization could drive the scalability of PV systems in remote locations when aligned with technical modeling.

Although a wealth of literature exists on the evolution of solar technologies and their environmental benefits, there remains a lack of integrative studies that combine technical optimization (tilt, orientation) with local climatic input and real-time validation; conduct economic feasibility comparisons between fixed-angle and tracking systems for green building contexts; and apply simulation tools not just as predictive models but as decision-support frameworks for urban PV system design.

The specific objectives of this research were as follows:

• To optimize solar panel orientation and tilt angles by identifying configurations that maximize energy capture under the unique meteorological conditions of Truro.
• To compare PVsyst simulation outcomes with empirical data, validating simulation accuracy and assessing deviations under real-world conditions.
• To evaluate the economic feasibility of solar tracking systems, comparing lifecycle costs and energy gains against traditional fixed-panel systems.
• To analyze the influence of environmental variables such as albedo, shading, and ambient temperature on system performance through sensitivity analyses.
• To offer managerial recommendations for PV system designers and stakeholders to facilitate data-driven decisions in system planning and implementation.

This paper is structured as follows: Section 2 presents the methodology, including simulation setup and data sources.. Section 3 presents results and validation. Section 4 discusses the findings in the context of prior literature. Section 5 concludes with policy implications and recommendations for future research.

2. Methodology

Figure 3 presents a research roadmap, detailing the systematic process for optimizing solar energy harvesting using PVSyst. The flowchart commences with “Data Gathering,” followed by the “PVSyst Simulation Setup” where initial simulation parameters are established. This leads into the “PVSyst Sensitivity Analysis,” where the impact of variable adjustments on output is assessed. Subsequently, the “Comparison of PVSyst Models” evaluates different models to identify the most accurate. The “Economic Analysis of PV Systems” assesses the cost-effectiveness of various configurations, leading to “Managerial Insights” that inform decision-making processes. The roadmap culminates in “Results Analysis,” where data are reviewed and interpreted, followed by “Conclusions and Recommendations” which provide final thoughts and suggestions for future research or practical implementation.

2.1. Case Study and Simulation Approach

The research outlined in the provided document on solar energy harvesting followed a structured methodology integrating data collection, computational modeling, and simulation validation within the specified geographic context of Truro City, United Kingdom, as the case study. Initially, the study began by selecting this city due to its unique geographic and climatic conditions which were ideal for studying solar energy patterns. Historical meteorological data along with solar radiation levels specific to that region were collected, focusing on identifying peak solar energy availability times, especially between 11 a.m. and 1 p.m., which preliminary data suggested were optimal for energy harvesting.

In modeling solar energy availability, the research deployed a dual approach. Firstly, coefficients that modeled solar energy availability were developed and adjusted to reflect the geographic and temporal specifics of Truro City. These coefficients were then utilized in spreadsheet computations to calculate predicted energy availability, creating a baseline dataset for comparison. Alongside, PVSyst software (V 7.0) simulations were conducted, tuning parameters to closely mimic real-world conditions to validate the accuracy and reliability of the spreadsheet models. Further analysis and optimization were conducted through a comparative analysis of the results from both the spreadsheet computations and PVSyst simulations. This comparison helped in identifying any disparities and validated the model’s accuracy. A sensitivity analysis was also performed to determine the impact of varying solar panel tilt and orientation on the energy output. The coefficients and simulation parameters were then adjusted based on these findings to optimize the setup for maximum energy yield during identified peak times [25].

The integration of real-time meteorological data into the PVSyst simulations enhanced the predictive capability and reliability of the models. These refined data were used to generate actionable managerial insights, focusing on the most effective times for conducting energy-intensive activities and strategies for managing surplus energy. Finally, the study validated its simulation results by cross-referencing with actual energy harvesting data from the field. The findings and methodologies were meticulously documented in detailed reports that outlined the research process, findings, and strategic recommendations for enhancing solar energy harvesting in Truro City.

2.2. Sensitive Analysis

In the PVSyst software, a comprehensive suite of parameters is available to facilitate the design and analysis of photovoltaic (PV) systems. These parameters are organized into four distinct categorizations, each playing a crucial role in the overall performance and efficiency of PV systems. These categorizations encompass orientations, system designing, shading control, and albedo settings, and the parameters within each category are thoughtfully designed to address various aspects of PV system planning and operation. In the present study, the system was designed for 50 kWp in 350 m². Likewise, the applied panel specification was 230 Wp 24 V (Si-Poly) from Sungate (Vmpp (60 °C):25.3 V, Voc: (−10 °C): 39.9 V). Also, the specifications of the inverter were 75 kW (350–700 V TL, 50/60 Hz) from Zigor [25].

About the orientation,

Table 1. Sensitive analysis variables in PVSyst software.

Parameter	Description	Range
Orientations parameters
Plane tilt	Angle of panel inclination	30–45°
Plane azimuth	Panel orientation direction	0–30°
Shading control
Pitch	Inclination angle or slope	2.5–7.5 m
Baseline slope	Slope along a baseline	0–30°
Misalign	Alignment or positioning deviation	0–20 m

showcases a variety of parameters related to the orientation of PV modules. These parameters include azimuth angles, tilt angles, and tracking options. Proper orientation ensures that the PV modules capture the maximum amount of sunlight throughout the day, optimizing energy production. By adjusting these parameters, users can align their PV arrays to the sun’s path for enhanced energy yield [16].

Shading is also a critical consideration in PV system design since even a small amount of shade can significantly impact energy production. Table 1 in the software presents parameters related to shading control, such as the positioning and dimensions of objects that may cast shadows on the PV modules. It also provides tools for analyzing the impact of shading and optimizing the system layout to minimize its effects [26].

Regarding system design,

Table 2. Constant parameters in PVSyst software design process.

Parameter	Description	Range
System designing
Type of panel	It depends on different companies	-------
Vmpp panel	Maximum power point voltage of panel	25.3 V
Voc panel	Open-circuit voltage of panel	39.3 V
Capacity of panel	Panel’s electrical output capacity	230 Wp
Inverter capacity	Inverter’s power conversion capacity	75 kW
Operating voltage	Voltage level during normal operation	350–700 V
Albedo setting
Site-dependent design parameters	Lower temperature for absolute voltage limit	−10 °C
Site-dependent design parameters	Winter operating temperature for VmppMax design	20 °C
Site-dependent design parameters	Usual operating temperature under 1000 W/m	50 °C
Site-dependent design parameters	Summer operating temperature for VmppMax design	60 °C
Site-dependent design parameters	Limit overload loss for design	3%
Limits with shading representations	Discriminating orientations difference between shading planes	1 °C
Limits with shading representations	Maximum orientation difference for defining average orientations	10 °C
Limits with shading representations	Maximum field/shading area ratio	2.5
Albedo value	Albedo value is a measure of the reflectivity of a surface, indicating the fraction of incoming solar radiation that is reflected into the atmosphere or space, with higher values representing greater reflectivity	0.08–0.85 (average value: 0.2)
Climate parameters
Global irradiance	Total solar radiation received at a location	23.8–157.3 kwh/m2
Diffusion	Scattered solar radiation in the atmosphere	12.7–87.8 kwh/m2
Temperature	Ambient air temperature affecting solar panels	3.4–18.4
Wind velocity	Speed of wind impacting solar system performance	2.89–4.60

is dedicated to parameters essential for the general system design of a PV installation. It covers aspects like the type and arrangement of PV modules, inverter specifications, and battery storage options. These parameters allow users to customize their systems to meet specific energy needs and grid integration requirements.

For albedo settings, Table 2 addresses the often-overlooked factor of albedo, which is the reflectivity of the ground surface beneath the PV array. Parameters in this category help users define the ground surface type, its albedo coefficient, and how it affects the system’s energy performance. Understanding albedo is crucial in regions with varying ground cover conditions [6].

PVSyst’s systematic organization of these parameters across these four categories empowers users to fine-tune their PV system designs to maximize energy output while considering real-world constraints and environmental conditions. This level of customization and precision in parameter selection is essential for engineers, project managers, and researchers to make informed decisions and optimize the performance of solar installations. Whether it is achieving the right orientation for modules, designing an efficient system, mitigating shading effects, or accounting for albedo, PVSyst provides the tools and parameters needed for a comprehensive PV system analysis and design.

In this report, Table 1 illustrates the key variables, while Table 2 presents the constant values. Additionally, some of the features in Table 2 are linked to the climatology aspects of the case study.

2.3. Simulation Definition

The angles for panel adjustment, including azimuth and tilt, are crucial for optimizing the capture of solar energy and are meticulously detailed in Figure 4. This figure provides a visual representation of how different orientations can affect the performance of solar panels throughout the day. Each angle is adjusted to enhance the solar panels’ exposure to sunlight, maximizing energy absorption during peak solar hours. The tilt angle is particularly significant as it aligns the panels to receive the maximum amount of solar radiation based on the sun’s position relative to the geographic location.

Further elucidation is provided in Figure 5, which presents 3D graphs of the panel’s simulation under the conditions set forth in the sensitivity analysis. These simulations offer a deeper understanding of how the various configurations impact the energy output, demonstrating the fluctuations in performance with changes in panel alignment. The 3D graphs enable a comprehensive analysis by visually comparing different tilt and azimuth settings under simulated environmental conditions, such as cloud cover and varying sun angles throughout the year. This analytical approach helps in identifying the optimal panel settings that yield the highest energy efficiency under typical weather patterns for the region.

3. Results

This effort illustrates the process of energy harvesting throughout various times of the day. This particular segment of the study delves into the utilization of distinct coefficients to calculate the availability of solar energy, with a subsequent comparison to PVSyst simulations.

Within the PVSyst software, a two-step approach was adopted. Firstly, the parameters governing the interaction between the case study, in this instance, Truro city, and the solar height were finely tuned. Following this, the software was employed to assess the energy availability during different months. This enabled us to validate and cross-reference our findings with the simulations generated by PVSyst, thus ensuring the accuracy and reliability of our energy harvesting data. The simulation of this section was conducted as per the 100 kWp solar energy requirement.

The structure of solar movement in Truro city and different time capacity in the case study are demonstrated in Figure 6a,b).

As depicted in Figure 6b, it becomes evident that the peak energy availability occurred during the time window between 11:00 a.m. and 1:00 p.m. This period consistently exhibited the highest levels of energy generation and represented the optimal timeframe for harnessing solar power. The data presented in Figure 6b underscore the significance of this specific window in the context of energy harvesting and suggests that focusing efforts during these hours can result in the most efficient utilization of available solar resources. This insight provides valuable guidance for scheduling energy-intensive activities and making the most of renewable energy sources.

The graphical representations in Figure 7 reveal the results derived from two distinct methodologies: spreadsheet computations (based on coefficient analysis) and the utilization of PVSyst software. An examination of the depicted data highlights significant disparities between the outcomes obtained from these two modeling approaches.

In a broader context, the observations made in Figure 6b, which pertain to the solar panel’s positioning, led to a noteworthy inference. It became evident that the optimal time for energy harvesting occurred around noon, specifically between 11:00 a.m. and 1:00 p.m. Consequently, this finding prompted a necessary adjustment in the coefficient-based calculations, which should be shifted towards the left side of the timeline to better align with this peak energy harvesting period.

Moreover, it is imperative to consider the average energy generation across different months. This aspect of the analysis is presented in Figure 7, providing a visual comparison between the average energy yields obtained through spreadsheet computations (a practical measurement which is used by companies) and other simulation outputs. These comparative results offer valuable insights into the reliability and accuracy of the spreadsheet model, particularly in varying seasonal conditions.

As depicted in Figure 8, the simulations conducted using the PVSyst software, due to their direct connection to the METEO datacenter, demonstrated a notable degree of precision. This precision can be attributed to their ability to seamlessly integrate real-time meteorological data, which align them closely with the actual environmental conditions.

These refined simulations become particularly pertinent when making operational decisions, such as scheduling the activation of pumps or determining strategies for selling surplus energy back to the grid. The fluctuations in energy availability, as reflected in the PVSyst results, serve as a vital parameter in these decision-making processes.

Therefore, the real-time meteorological data integration in PVSyst contributes not only to the accuracy of energy yield predictions but also to the efficiency and optimization of energy-related activities. This linkage between accurate simulations and practical applications underscores the significance of meteorological data in the management of energy systems and the successful operation of energy networks.

The results of the case study analysis revealed that both tilt (as depicted in Figure 9a) and azimuth (as shown in Figure 9b) angles significantly impacted the energy harvesting of solar panels. Optimal values for these angles were found to be approximately 35–39 degrees for tilt and 0 degrees for azimuth. An evaluation of both angles revealed that employing a solar tracker may not be a prudent choice for the facilities. Instead, fixing the solar panels in optimal conditions emerged as a more logical and efficient approach. The rationale behind this decision is multifaceted. Firstly, the installation of certain systems like solar trackers can be a time-consuming process. Moreover, it often incurs substantial costs in terms of both initial setup and ongoing maintenance. These factors, in turn, necessitate the allocation of significant human resources. In the context of the case study, these drawbacks collectively rendered the solar tracking system nonessential. By opting for a fixed solar panel configuration in optimal conditions, not only was the process simplified and more cost-effective, but it also minimized the demand for ongoing human intervention and maintenance. Therefore, this decision aligned with the practicality and efficiency needed for the specific context of the case study.

In the following section, the results of a comprehensive analysis of shading control features are presented, focusing on energy loss through 3D simulations (Figure 10a). The results highlight the significance of the pitch factor in terms of beam linear energy loss percentage. Notably, adjusting the pitch from 2.5 to 10 during the simulation led to a remarkable reduction in losses, from 56% down to 0.

However, practical considerations, such as infrastructure area limitations (as shown in Figure 10b), must also be considered. For instance, with an optimal pitch of 10, the required area amounted to 3360 square meters, which may entail substantial investment costs. Therefore, a compromise could be achieved by selecting a pitch of 5 m, even though it resulted in a 10% energy loss due to shading, as it reduced the area requirement to 1680 square meters.

Furthermore, Figure 10c highlights the feasibility of the project when utilizing only 30% of the maximum available area while tolerating a reasonable energy loss. This approach could make the project more attractive and financially viable for project managers and decision-makers.

The analysis of the baseline slope (as depicted in Figure 11a) and misalignment degrees (illustrated in Figure 11b) provides valuable insights. It is evident from the findings that these factors have a relatively minor impact on energy losses in solar systems. In Figure 11a, the baseline slope exhibits a maximum energy loss of just 4% within the system. Similarly, the variation in panel misalignment degrees resulted in a maximum loss of 0.6%. Given these negligible losses, it became apparent that both features could be effectively set to zero angles. By fixing the baseline slope and misalignment degrees at zero, this not only simplified the system’s design and operation but also ensured that energy losses were kept to a minimum, contributing to an efficient and reliable solar energy setup.

4. Discussion

The sensitivity analysis conducted in our study revealed a critical insight into the simulation of a 50 kWp solar installation, emphasizing the paramount role played by the parameter associated with pitch, which is closely tied to the utilization of occupied space. Our investigation delved into various aspects of this simulation, and the results underscored the pivotal role of pitch in optimizing the performance of the solar installation.

In our research, we carefully assessed the effects of pitch variations, taking into account the potential energy loss due to shading. We found that maintaining a pitch value of 5 m, and allocating a land area of 1680 square meters, resulted in an exceptional outcome. Specifically, with a conservative estimate of a 10% energy loss due to shading, this configuration can be considered as the optimal choice for maximizing energy production and efficiency. This discovery not only underscores the importance of pitch but also highlights the delicate balance required to achieve optimal results in the case study.

In the course of our investigation, we also uncovered the ideal values for various other factors within the case study, which are vividly depicted in Figure 12. This figure encapsulates the comprehensive findings of our research, offering a clear and concise representation of the optimal configurations for the remaining parameters. These results provide invaluable guidance for stakeholders and decision-makers in the field of solar energy, enabling them to make informed choices when implementing similar projects.

The results of our sensitivity analysis in the current project unveiled critical managerial insights that can greatly inform the design of a PV system. These findings are succinctly presented in Figure 13, providing a wealth of knowledge to guide system designers and project managers.

One of the most striking conclusions that emerged from our study was the limited utility of solar trackers in the specific case under examination. The data unequivocally indicated that, in this particular context, the incorporation of solar trackers did not yield significant benefits. Rather, the costs and complexities associated with trackers outweighed the marginal improvements in energy production they offered. In contrast, our research strongly highlighted the effectiveness of a fixed panel configuration with a 5 m pitch. Not only did this configuration adhere to area limitations, but it also managed to maintain a reasonable level of energy output, even accounting for the potential energy loss due to shading. Thus, opting for fixed panels with a 5 m pitch was revealed as the most cost-effective and practical choice for this project. Additionally, it is essential to emphasize that our findings advocate for a specific panel inclination angle falling within the range of 35–39 degrees during the design phase. This range was shown to be highly conducive to optimizing energy capture while simultaneously controlling costs.

Our findings align with prior studies emphasizing the significance of spatial arrangement in solar PV system design. For instance, Khan et al. (2018) highlighted the impact of shading and panel orientation on energy yield in green building applications, underscoring the necessity of pitch optimization to mitigate shading losses [10]. Similarly, the identified tilt angles correspond closely to recommendations in the recent literature for mid-latitude installations to maximize energy capture throughout the year [11].

A key managerial insight from this study is the limited advantage of solar trackers within this specific context. The cost-benefit analysis indicated that the marginal energy gains from tracking systems did not justify their higher installation and maintenance costs. This is consistent with findings by [27], who noted that fixed-tilt PV arrays often offer superior cost-effectiveness in regions with moderate solar variability.

While photovoltaic (PV) systems remain the most scalable and widely adopted renewable energy technology, it is important to acknowledge other energy harvesting techniques to contextualize their merit. Recent studies have shown significant advancements in thermoelectric films, such as epitaxial GeTe thin films achieving ultralow thermal conductivity and high-power factors [28]. Similarly, biodegradable piezoelectric nanogenerators have demonstrated promising outputs for biomedical applications [29]. However, despite their niche potential, these methods face limitations in scalability and integration. In contrast, PV systems offer a stable output, cost-effectiveness, and suitability for large-scale deployment, making them a practical choice for urban and building applications, as emphasized in our study.

According to

Table 3. Comprehensive comparisons of different studies in the field of solar system sensitive analysis based on PVSyst model.

Method	Description	Key Features	References
PVSyst Grid Simulation	700 KWp solar plant modeled for Daikundi, Afghanistan	Evaluates 1266 MWh/yr yield, 0.797 performance ratio; supports 2032 energy goals in rural areas	[30]
PVSyst and Response Surface Methodology	Solar-powered reverse osmosis desalination optimized for Zahedan, Iran	Uses Response Surface Methodology, and some machine learning models; optimizes energy, cost, carbon emission	[31]
Techno-Economic Cost Model	Grid-connected solar PV for residential building use	Evaluates electricity output, net present value, payback period; compares feed-in tariff vs. smart export guarantee schemes	[32]
Off-Grid PV System Model	Battery-backed solar PV simulation for home use in M’sila, Algeria	Uses PVsyst6; 12.6 kWh/day demand; 4615 kWh/year exported; 62.9% performance ratio; key losses due to high PV field temperature	[33]
Bifacial PV Sensitivity Model	Optimization of bifacial PV with albedo, tilt, height using simulation	Uses Response Surface Methodology; 35.68% daily bifacial gain; 35° tilt optimal; max gain 21.93%; albedo most impactful factor	[34]
Grid PV System Bahawalpur	Grid-connected solar PV system for Bahawalpur University, Pakistan	Uses PVsyst; 4908 MWh/year; 83.8% performance ratio; cost 0.11 USD/kWh; saves 15,369.3 kg coal/day; supports eco-friendly energy goals	[35]
Contril System Design and PV Optimization	PV system simulation for Truro using real-time weather and PVSyst	100 kWp case; 646.83 kWh/m2/month; optimal tilt 35–39°; 10% shading loss; compares spreadsheet vs. PVSyst; enhances accuracy and design	This study

, this study distinguishes itself by integrating real-time meteorological data with PVSyst for a 100 kWp photovoltaic system, achieving an impressive average generation of 646.83 kWh/m²/month with only 10% shading loss. This integration not only enhances simulation accuracy but also supports intelligent operational decision-making. Notably, this work went further by assessing the real-time controllability of the PV system using advanced PVSyst modeling—an aspect not addressed in the compared studies. While the Daikundi study [30] offered valuable insights into rural-scale deployment and the Zahedan research [31] innovatively combined machine learning with PV-desalination systems, the present study prioritized precision, flexibility, and practical application. In contrast to the techno-economic evaluations [32], off-grid modeling efforts [33], and bifacial optimization studies [34], this work provided a balanced, site-adapted, and scalable framework. Furthermore, compared with the Bahawalpur case [35], it emphasizes data-driven energy forecasting and shading-aware design, positioning it as a forward-looking contribution to PV system development.

The impact of heat generation on PV panel performance was systematically addressed through temperature-dependent simulation parameters embedded within the PVSyst modeling environment. The thermal behavior of the system was captured using ambient temperature values ranging from 3.4 °C to 18.4 °C and typical operating temperatures of 50 °C under 1000 W/m² irradiance. The simulation also considered summer peak temperatures up to 60 °C and a lower threshold of −10 °C, ensuring full representation of seasonal variation. Key thermal loss factors, such as inverter efficiency, panel temperature coefficients, and wind velocity (ranging from 2.89 to 4.60 m/s), were integrated to reflect cooling effects and real-world performance. The resulting performance ratio inherently reflected losses from heat-induced efficiency deterioration. Thus, the methodology offered a robust approximation of temperature effects on energy yield in real operating conditions. Also, the height at which a solar panel is mounted influences both its thermal behavior and radiative exposure. Increasing the panel height enhances air circulation beneath the module, which improves convective heat dissipation. This cooling effect reduces the operating temperature of the solar cells, thereby minimizing thermal losses and improving conversion efficiency [36]. Elevated panels can operate closer to their optimal temperature ranges, which is particularly beneficial in hot climates where temperature-induced efficiency loss is significant. Additionally, in the case of bifacial PV systems, a greater height allows more ground-reflected irradiance (albedo) to reach the rear side of the panel, increasing the bifacial gain. However, excessive elevation may increase material costs and structural complexity. Therefore, an optimal height balances thermal performance improvement, energy gain, and system cost-efficiency [37].

Regarding the anisotropy of sunlight, which refers to its non-uniform angular distribution in the sky, it can be said that it plays an essential role in accurately estimating solar irradiance on tilted PV surfaces. Unlike isotropic models that assume diffuse radiation is uniformly distributed, anisotropic models consider directional components such as circumsolar radiation and horizon brightening, which are more representative of real atmospheric conditions. This distinction becomes especially important during winter months or under partially cloudy skies when diffuse radiation dominates. Studies have shown that anisotropic models like Hay and Davies (HD) and Hay Davies Klucher Reindle (HDKR) yield significantly higher and more accurate irradiance values compared to isotropic models. For instance, in January, the HDKR model can estimate up to 14.82% more incident radiation than traditional isotropic approaches [38].

Lastly, although this study primarily focused on the evaluation of solar energy generation through simulation-based analysis and real-time meteorological integration, it is important to note that the practical implementation of such systems also requires secure and reliable data management infrastructure. In particular, challenges associated with key management and secure data distribution emerge when integrating photovoltaic systems with smart grids and IoT-enabled energy management platforms. Addressing these concerns involves establishing robust cryptographic protocols, efficient key distribution mechanisms, and secure authentication frameworks to safeguard system integrity and user data. Future research should explore the integration of energy optimization with cybersecurity solutions to ensure resilience and trust in large-scale solar energy deployment.

5. Conclusions

This study presented a simulation-based optimization of photovoltaic (PV) energy systems for green buildings in Truro City, utilizing PVSyst software in conjunction with real-time meteorological data. The results indicated that a fixed PV panel configuration with a tilt angle between 35° and 39° and an azimuth of 0° (true south orientation) yielded the highest energy output under local climatic conditions. The system achieved an average monthly energy generation of 646.83 kWh/m², with a shading-induced energy loss of approximately 10% on a 5 m pitch layout, requiring 1680 m² of installation area. The integration of dynamic meteorological datasets significantly enhanced the precision of the simulation model, improving short- and medium-term energy yield forecasts. This has direct implications for operational energy scheduling, peak load management, and energy storage planning. Furthermore, the sensitivity analysis demonstrated that solar tracking systems, while technically feasible, offered marginal gains (an increase of less than 8% in output) that did not justify their increased capital and maintenance costs in this geographic and economic context. Future work should focus on multi-objective optimization approaches that simultaneously account for economic, environmental, and spatial parameters. Incorporating building-integrated PV (BIPV) technologies and energy storage systems (such as lithium-ion and vanadium redox flow batteries) could further enhance energy autonomy. Additionally, the use of AI-based forecasting models in tandem with real-time IoT sensor networks may offer improved decision support for dynamic load management and grid interaction. A broader validation across different climate zones and urban typologies would also help generalize the methodology and its recommendations.