Environmental Impacts on the Performance of Solar Photovoltaic Systems

Renewable energy and cryptocurrency: A dual approach to economic viability and environmental sustainability

One of the foremost challenges facing Bitcoin, as the most valuable cryptocurrency operating on a proof-of-work mechanism, is its substantial energy consumption and environmental impact. With the expansion of the Bitcoin market, mining has surged in popularity, particularly in countries where energy and monetary costs are comparatively low. This study aims to assess the impact of utilizing renewable energy from a photovoltaic system for Bitcoin mining, simulating a solar power plant with a 50.91-MW capacity alongside a corresponding Bitcoin mining operation in the United Arab Emirates. Economic evaluations were conducted using comprehensive, historically archived data to ensure results that closely mirror real-world scenarios. Additionally, for a more nuanced comparison, an economic assessment of selling the power plant's electricity to the grid was also performed, with the findings juxtaposed. The outcomes indicate that initiating such a system at the start of 2020 with an investment of approximately $42 million could recoup its costs in about 3.5 years. In contrast, selling electricity to the grid would extend the power plant's return on investment period to 8.1 years. Furthermore, the environmental evaluation revealed that adopting renewable solar energy for mining could avert the emission of around 50,000 tons of CO2 annually.

Effects of climatic conditions of Al Seeb in Oman on the performance of solar photovoltaic panels

Human activities and climatic elements, including temperature, humidity, and wind speed, have an impact on natural dust deposition. Therefore, this study aims to investigate the effects of wind speed, relative humidity, and ambient temperature on the performance of soiled photovoltaic panels in Al Seeb, Oman. The study was conducted by exposing the solar PV panels to outdoor sunlight for a duration of two months. Parameters such as solar radiation, voltage, current, solar panel temperature, wind speed, relative humidity, and ambient temperature were collected in a short time interval. It was observed that the dust densities of 20.7 g/m2, 27 g/m2, and 41.3 g/m2 resulted in electrical power reductions of 18 %, 33 %, and 40 % for the panels uncleaned for one week, two weeks, and three weeks, respectively. The effect of daily dust resulted in an energy reduction of 14 %. Moreover, dust deposition decreases when the wind speed increases, resulting in a higher power output and vice versa. The higher the humidity, the stronger the dust's adhesion to the surface, resulting in more deposition and reduced power output. The maximum power output of 82.3 W was achieved at the wind speed of 10 m/s, 34.9 % relative humidity, and ambient temperature of 38.5 °C.

Optimal placement and operation of soft open points, capacitors, and renewable distributed generators in distribution power networks to reduce total one-year energy loss

The paper optimizes the placement of soft open points (SOPs) devices, shunt capacitor banks (SCBs), and distributed generators (DGs) in the IEEE 69-node distribution power grid for reducing the power loss of a single hour and total energy losses of one year. EO is proven to be more effective than previous methods and three other applied algorithms, including the Coot optimization algorithm (COOT), Modified weight inertia factor and modified acceleration coefficients-based particle swarm optimization (CFPSO), and Tunicate swarm algorithm (TSA). So, EO is applied for the last case considering one SOPs, one wind turbine (WT), two solar photovoltaic systems (PVs), and two SCBs over one year with twelve months and 24 h each month. The study reaches the smallest power loss compared to previous studies in the first case with one SOPs device. The results from the second to the fourth cases indicate that the power grid needs the placement of SCBs and DGs first and SOPs devices to reach the lowest power loss. Case 5 indicates that the hybrid system with one WT and two PVs suffers higher power losses than the base system at hours with high generation from renewable sources; however, integrating the SOPs and SCBs into the hybrid system can reach smaller losses than the base system at these hours. Thus, using SOPs and SCBs in integrated distribution power grids with renewable energies can greatly benefit energy loss reduction.

Modeling of soiling losses on photovoltaic module based on transmittance loss effect

The photovoltaic modules are mostly installed outdoors, exposing them to different conditions. These conditions significantly affect their performance. One of the most influential factors on photovoltaic modules is the soiling phenomenon from dust deposition. Dust deposition on the surface of the modules causes transmittance loss. Some studies in different parts of the world have tried to find mathematical correlations between particulate deposition and transmittance. These correlations are a function of dust characteristics and environmental factors. This study proposes a new methodology to mathematically combine the photovoltaic model and transmittance loss correlations. The proposed model could examine and predict the effect of soiling on photovoltaic modules' performance. Three photovoltaic modules with different capacities are selected. Using the proposed model, they are modeled by assuming clean and dirty photovoltaic surfaces depending on the installation conditions. This study is based on actual data from the center of Tehran, located at 35° 41' north latitude and 51° 23' east longitude for 12 months in the year 2020. The module's performance was investigated in the presence of dust. The presented results are validated by comparing them with other studies. The results show that the soiling effect is not dependent on the modules' capacity.

Reliability and performance evaluation of a solar PV-powered underground water pumping system

The operation and effectiveness of a solar-powered underground water pumping system are affected by many environmental and technical factors. The impact of these factors must be investigated to be considered when developing these systems and to ensure their dependability. This study evaluated the dependability and performance of photovoltaic water pumping system (PVWPS) under real operating conditions by examining the effects of solar irradiance, panels' temperature, and components' efficiency. From December 2020 to June 2021, experiments were conducted on a 10 hp PVWPS located in Bani Salamah, Al-Qanater-Giza Governorate, Egypt, at latitude 30.3° N, longitude 30.8° E, and 19 m above sea level. The irradiance values reached 755.7, 792.7, and 805.7 W/m2 at 12:00 p.m. in December, March, and June, respectively. Furthermore, the irradiance has a significant impact on the pump flow rate, as the amount of pumped water during the day reached 129, 164.1, and 181.8 m3/day, respectively. The panels' temperatures rose to 35.7 °C, 39.9 °C, and 44 °C, respectively. It was observed that when the temperature rises by 1 degree Celsius, efficiency falls by 0.48%. The average efficiency of photovoltaic solar panels reached its highest value in March (13.8%) and its lowest value in December (13%).

Performance evaluation of solar photovoltaic panels under bird droppings accumulation using thermography.. [DOI: 10.21203/rs.3.rs-3179348/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Bird guano accumulated on solar photovoltaic (SPV) panels caused a reduction of its output power by blocking the sunlight received on it. Therefore, thermal imaging was used to understand and study the effect of bird droppings accumulated on SPV panels. Four number of bird droppings (1, 2, 3, and 4 drops) were studied and compared with clean SPV panel (control). Front and backside temperatures of the SPV module have been evaluated in addition to current, voltage, power, and efficiency of the SPV modules. The study found that high-density of droppings accumulation on the frontside of SPV modules significantly decreased their temperature due to increased solar radiation reflection. The bird guano accumulated on the SPV module with 4 drops led to a decrease in output currents of the SPV modules by 36–38% as compared with the clean SPV module. While the module voltage was found to be slightly affected by the bird guano accumulated on it. The bird guano accumulated on the SPV module with 4 drops led to a decrease in output power and efficiency of the SPV modules by 26% and 43% respectively as compared with the clean SPV module. Therefore, the importance of this work is to understand how the accumulation of feces affects the performance of the SPV performance, as it clearly demonstrates its negative impact.

Numerical investigation of the effect of dust shields on accumulation of dust over PV panels

Dust accumulation on photovoltaic panels represents a major challenge for the operation of solar panels especially in the regions known by their high rate of dust and low frequency of rain. The objective of this study is to minimize dust accumulation on PV panels operating street light posts using dust shields. A novel dust shield having the same width of the panel, and subtending an angle of 120° with the panel, is proposed for dust mitigation. Numerical simulations are carried out to evaluate the influence of the dust shield on dust accumulation over the panel's surface. It is found that using a dust shield decreases the dust deposition rate by more than 44%. Moreover, extending the panel's surface at the lower edge with an extension plate together with the dust shield decreases the dust deposition rate better than using a dust shield only. Also, the effect of adding an air gap between the shield and the added extension plate is investigated, and it is found that the air gap induces air drafts over the panel's surface, which acts as an air barrier that obstructs the approach of dust particles to the panel's surface. These drafts get stronger as the air gap thickness increases, accordingly, less particles deposit on the panel. Finally, it is found that using a dust shield with a length smaller than the panel's length in addition to an extension plate together and increasing the thickness of the air gap is an effective and efficient solution for dust mitigation, such that the percentage decrease in the dust deposition rate that might be more than 88%.

Availability of Biomass and Potential of Nanotechnologies for Bioenergy Production in Jordan

Jordan’s energy situation is in a critical state of dependency, with the country relying heavily on imports to satisfy its ever-increasing energy requirements. Renewable energy is a more competitive and consistent source of energy that can supply a large proportion of a country’s energy demand. It is environmentally friendly and minimizes atmospheric pollutant emissions. Thus, bioenergy has the potential to be a crucial alternative energy source in Jordan. Biomass is the principal source of bioenergy; it accounts for approximately 13% of the primary energy demand and is anticipated to supply half of the total primary energy demand by 2050. Nanotechnology has emerged as an important scientific research area with numerous applications, including biofuels. This review summarizes the application of nanoparticles to improve the properties and processes of biofuels. It presents the availability and viability of nanotechnology-supported bioenergy production in Jordan. Jordan generates up to 5.8 million tons of biomass each year and has access to abundant nonedible plant resources (such as Jojoba, Handal, and Jatropha). The theoretical energy potential of waste and residue available in Jordan was also assessed; it was discovered that the 1.28 million tons of dry crop residues (vegetables, fruits, and farming crops) could generate 6.8 PJ of energy per year and that biogas could be generated at a rate of 817 MCM/year

A Solar Irradiance Forecasting Framework Based on the CEE-WGAN-LSTM Model

With the rapid development of solar energy plants in recent years, the accurate prediction of solar power generation has become an important and challenging problem in modern intelligent grid systems. To improve the forecasting accuracy of solar energy generation, an effective and robust decomposition-integration method for two-channel solar irradiance forecasting is proposed in this study, which uses complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a Wasserstein generative adversarial network (WGAN), and a long short-term memory network (LSTM). The proposed method consists of three essential stages. First, the solar output signal is divided into several relatively simple subsequences using the CEEMDAN method, which has noticeable frequency differences. Second, high and low-frequency subsequences are predicted using the WGAN and LSTM models, respectively. Last, the predicted values of each component are integrated to obtain the final prediction results. The developed model uses data decomposition technology, together with advanced machine learning (ML) and deep learning (DL) models to identify the appropriate dependencies and network topology. The experiments show that compared with many traditional prediction methods and decomposition-integration models, the developed model can produce accurate solar output prediction results under different evaluation criteria. Compared to the suboptimal model, the MAEs, MAPEs, and RMSEs of the four seasons decreased by 3.51%, 6.11%, and 2.25%, respectively.

The effect of various industrial dust particles on the performance of photovoltaic panels in Turkey

The accumulation of dust is one of the main causes of power loss in photovoltaic (PV) farms, and the effect of dust particles' size and chemistry on system performance is often overlooked. This study has focused on a comprehensive analysis of the effect of different dust particles collected from common industrial production facilities in Turkey on the performance of PV panels in the indoor laboratory environment. The collected dust samples were analyzed to determine the dust sample particles' chemical properties, size, character, and topography. The data for dust samples at different weights with changes in maximum power point (MPP) of PV panel has been collected using the artificial solar irradiation source system. Thus, the mathematical correlations (R2≥0.965) between the PV panel Thevenin resistance (RTH), fill factor (FF), MPP, and pollution rate were obtained using these collected data and particle swarm optimization (PSO). According to the results of the obtained mathematical correlations, marble dust is 2.3, 3.4, and 4.2 times less polluting than cement, fly ash, and silica fume, respectively. Additionally, it was observed that smaller dust particles block more light than larger ones of the same weight and reduce MPP, FF while increasing the RTH.

Reliability modeling and performance evaluation of solar photovoltaic system using Gumbel–Hougaard family copula

Purpose

Solar photovoltaic (PV) is commonly used as a renewable energy source to provide electrical power to customers. This research establishes a method for testing the performance reliability of large grid-connected PV power systems. Solar PV can turn unrestricted amounts of sunlight into energy without releasing carbon dioxide or other contaminants into the atmosphere. Because of these advantages, large-scale solar PV generation has been increasingly incorporated into power grids to meet energy demand. The capability of the installation and the position of the PV are the most important considerations for a utility company when installing solar PV generation in their system. Because of the unpredictability of sunlight, the amount of solar penetration in a device is generally restricted by reliability constraints. PV power systems are made up of five PV modules, with three of them needing to be operational at the same time. In other words, three out of five. Then there is a charge controller and a battery bank with three batteries, two of which must be consecutively be in operation. i.e. two out of three. Inverter and two distributors, all of which were involved at the same time. i.e. two out of two. In order to evaluate real-world grid-connected PV networks, state enumeration is used. To measure the reliability of PV systems, a collection of reliability indices has been created. Furthermore, detailed sensitivity tests are carried out to examine the effect of various factors on the efficiency of PV power systems. Every module's test results on a realistic 10-kW PV system. To see how the model works in practice, many scenarios are considered. Tables and graphs are used to show the findings.

Design/methodology/approach

The system of first-order differential equations is formulated and solved using Laplace transforms using regenerative point techniques. Several scenarios were examined to determine the impact of the model under consideration. The calculations were done with Maple 13 software.

Findings

The authors get availability, reliability, mean time to failure (MTTF), MTTF sensitivity and gain feature in this research. To measure the reliability of PV systems, a collection of reliability indices has been created. Furthermore, detailed sensitivity tests are carried out to examine the effect of various factors on the efficiency of PV power systems.

Originality/value

This is the authors' original copy of the paper. Because of the importance of the study, the references are well-cited. Nothing from any previously published articles or textbooks has been withdrawn.

Effect of dust and methods of cleaning on the performance of solar PV module for different climate regions: Comprehensive review

Recent achievement and progress in solar PV play a significant role in controlling climate change. This study reviewed comprehensively electrical characteristics, life cycle of dust, optical characteristics, and different cleaning techniques related to the effect of dust on the performance of PV modules throughout different climate regions of the world. The power maximum power point (MPP) and curve of PV module under the effect of irradiance and temperature were presented. The effect of dust (shading) on the electrical efficiency of PV module was discussed based on soft, partial, and complete (soiling) shading. The physical properties of dust around the globe such as PM₁₀ concentration, dust loading (mgm^-2), and fine dust particles concentration were covered and discussed. Reasons behind the accumulation of dust based on, location and installation factors, dust type, and environmental factors. Environmental reasons causing dust and dust removal in accordance with the life cycle of dust was covered in detail. All the reasons that cause the generation, accumulation and removal of dust during its life cycle were explained. All forces responsible for the adhesion phase of the dust life cycle were presented. The effect of dust on PV module transmittance and electrical parameters module were discussed in detail based on physical properties of the dust at its location and installation conditions. Self-cleaning super hydrophobic surfaces based on methods such as solvents, vapor-assisted coating, powder coating, and polymerization were discussed. All cleaning technologies, including self-cleaning technologies, based on the material coating used, and the manufacturing of PV cells was compared. The future prospective for PV technologies and cleaning methods were also covered.

Thermal Investigation and Optimized Design of a Novel Solar Self-Driven Thermomechanical Actuator

As the world moves toward cleaner and greener sources of energy, the use of solar energy appeals the most for countries in the Middle East and North Africa (MENA) region, since they have an abundant amount of solar radiation throughout the year. This paper offers a novel design for a shape memory alloy (SMA) actuator that uses solar energy to trigger thermomechanical behavior. Additionally, the proposed design of the thermomechanical actuator aims to be a piston-based linear actuator covered by a solar heat collector (SHC). Furthermore, the thermal behavior of the actuator has been studied in detail using a simulation-based study under the real-time weather conditions of Dammam city, Kingdom of Saudi Arabia (KSA). The thermal study proves that the optimized design of the thermomechanical actuator has achieved a minimum daily temperature variation of 10 °C, which enables the SMA-based thermomechanical actuator to operate in a daily manner throughout the year. Moreover, the presented numerical results show that the proposed thermomechanical actuator requires a twice-maintenance routine yearly. Additionally, it has been observed that the SHC, which is the central part of the designed thermomechanical actuator, can increase the temperature inside the actuator by about 15 °C more than ambient temperature. The proposed study adds to the body of knowledge a design for a passive, solar-driven, and self-actuating smart thermomechanical SMA actuator that is capable of integration with various solar applications, such as the cleaning and tracking of photovoltaic systems.

Low-cost system for sunlight incidence angle measurement using optical fiber

The development and optimization of renewable energy systems are some of the most necessary topics to advance towards secure and sustainable energy models. Photovoltaic energy is one of those sustainable options that could contribute to the reduction of greenhouse gas emissions. The optimal angle of solar incidence producing the highest absorption in a day is an important parameter to install photovoltaic systems. This value is often estimated using simulation models based on geographic location; however, those models ignore the influence of nearby obstruction objects, albedo, and local weather conditions. Such a problem is addressed in this work by designing a system to estimate the optimum angle of solar incidence for the photovoltaic panels. The system is based on an arrangement of 33 measurement points spaced in arcs every 45 degrees in azimuth and every 22.5 degrees in elevation, which provides a wide range for analysis. The light captured by each optical fiber is transmitted to a flat array where the power is measured using a single RGB camera.

Designing and Energy Estimation of Photovoltaic Energy Generation System and Prediction of Plant Performance with the Variation of Tilt Angle and Interrow Spacing

The focus of this research is to design a ground-mounted photovoltaic system at optimal tilt angle and interrow space to meet high demand of electrical energy. The Department of Electrical Engineering and Technology, GC University Faisalabad has been considered to perform the simulation test. This study is conducted using Meteonorm software for solar resource assessment. Furthermore, HelioScope software is used for modeling of a ground-mounted photovoltaic system, study of PV system’s performance in terms of annual generation, system losses and performance ratio and analysis of photovoltaic module’s performance, current-voltage and power-voltage curves for different irradiance levels. From SLD, it is seen that 11 strings are connected to each inverter and inverters output power are combined by using 20.0 A circuit interconnects. The performance of photovoltaic systems is impacted by tilt angle and interrow spacing. From simulation results of all cases, it is concluded that the PV system installed at 15° tilt angle with 4 feet interrow spacing are more efficient than the other installed PV systems, because total collector irradiance is maximum (1725.0 kWh/m2) as compared to other tilt angles. At 15° tilt angle, the annual production of photovoltaic system is 2.265 GWh and performance ratio of PV system is 82.0%. It is envisioned that this work will provide the guidance to energy system designers, planners and investors to formulate strategies for the installation of photovoltaic energy systems in Pakistan and all over the world.

Performance enhancement of solar photovoltaic (PV) module using a novel flat plate (NFP) glass cover by reducing the effect of bird dropping (BD) settlement

A massive bird dropping (BD) deposition on the common rectangular flat plate (RFP) of photovoltaic (PV) module is a matter of great concern in Western Rajasthan (WR) that diminish the overall energy production capacity of the system remarkably. In this research article, a prototype novel flat plate (NFP) design of a front glass cover of PV module is proposed to prevent the impact of BD settlement by the restriction of bird's sitting/movement on the front glass cover. In this regard, the performance analysis of PV module with common RFP and newly designed NFP glass covers has been assessed at the different inclination β° (0-90). The BD accumulation onto the both glass covers was explored by the optical transmittance profiles at the different tilt angles, i.e., explained by bird movement on each flat glass surfaces. Consequently, a significant amount of output electric energy has been gained in NFP design rather than RFP corresponding to particular tilt regions TR I (0° ≤ β ≤ 25°), TR II (25° ≤ β ≤ 60°), and TR III (60° ≤ β ≤ 90°). According to the results achieved, an excellent level of improvement in average power loss, ~ 97.85%, corresponding to optimal TR (III) has been detected by employing NFP glass collector.

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.

Analytical Modeling and Optimization of a Heat Sink Design for Passive Cooling of Solar PV Panel

High temperature is the primary challenge in the development of solar photovoltaic (PV) systems in an arid climate. A rise in temperature diminishes the performance of the PV systems and shortens their lifespan. The goal of this manuscript is to develop an analytical model to predict the temperature of PV panels under a passive cooling system for an arid environment. Taking into consideration the link between solar panel temperature and its conversion efficiency, Kirchhoff’s and Ohm’s laws for a complex circuit were applied to calculate the heat flux in the solar panel system, and hence obtain the temperatures of each layer in the system. Closed-form analytical expressions for temperature, output power, and conversion efficiency of the solar panel were deduced and presented as functions of solar irradiance, ambient temperature, emissivity, wind velocity, tilt angle, and dimensions of fins. Comparison between the results presented in the literature and those predicted by the developed analytical model validated the presented model. Moreover, the length of the fins required for safe thermal operation of solar panels in harsh desert environment were also obtained from analysis. Furthermore, the effect of using such a cooling system on the temperature and efficiency of the solar panels was verified by using the developed model under real conditions in Dammam city during summer and winter seasons. The results showed that the optimized heat sink could raise the solar panel power by 8.7% during summer and by 6.5% during winter.

Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques

Fossil fuels and their use to generate energy have multiple disadvantages, with renewable energies being presented as an alternative to this situation. Among them is photovoltaic solar energy, which requires solar installations that are capable of producing energy in an optimal way. These installations will have specific characteristics according to their location and meteorological variables of the place, one of these factors being soiling. Soiling generates energy losses, diminishing the plant’s performance, making it difficult to estimate the losses due to deposited soiling and to measure the amount of soiling if it is not done using very economically expensive devices, such as high-performance particle counters. In this work, these losses have been estimated with artificial intelligence techniques, using meteorological variables, commonly measured in a plant of these characteristics. The study consists of two tests, depending on whether or not the short circuit current (Isc) has been included, obtaining a maximum normalized root mean square error (nRMSE) lower than 7%, a correlation coefficient (R) higher than 0.9, as well as a practically zero normalized mean bias error (nMBE).

Design, Modeling, and Experimental Investigation of Active Water Cooling Concentrating Photovoltaic System

This work presents performance study of a concentrating photovoltaic/thermal (CPV/T) collector and its efficiency to produce electric and thermal power under different operating conditions. The study covers a detailed description of flat photovoltaic/thermal (PV/T) and CPV/T systems using water as a cooling working fluid, numerical model analysis, and qualitative evaluation of thermal and electrical output. The aim of this study was to achieve higher efficiency of the photovoltaic (PV) system while reducing the cost of generating power. Concentrating photovoltaic (CPV) cells with low-cost reflectors were used to enhance the efficiency of the PV system and simultaneously reduce the cost of electricity generation. For this purpose, a linear Fresnel flat mirror (LFFM) integrated with a PV system was used for low-concentration PV cells (LCPV). To achieve the maximum benefit, water as a coolant fluid was used to study the ability of actively cooling PV cells, since the electrical power of the CPV system is significantly affected by the temperature of the PV cells. This system was characterized over the traditional PV systems via producing more electrical energy due to concentrating the solar radiation as well as cooling the PV modules and at the same time producing thermal energy that can be used in domestic applications. During the analysis of the results of the proposed system, it was found that the maximum electrical and thermal energy obtained were 170 W and 580 W, respectively, under solar concentration ratio 3 and the flow rate of the cooling water 1 kg/min. A good agreement between the theoretical and experimental results was confirmed.

Modelling of a Single Passage Air PV/T Solar Collector: Experimental and Simulation Design

The hybrid photovoltaic/thermal solar collector has attracted research attention for more than five decades. Its capability to produce thermal energy simultaneously with electrical energy is considered attractive since it provides higher total efficiency than stand-alone photovoltaic or thermal systems separately. This paper describes theoretical and experimental studies of a finned single pass air-type photovoltaic/thermal (PV/T) solar collector. The performance of the system is calculated based on one dimensional (1D) steady-state analysis using one dimensional energy balance equations, where simulation was carried out using MATLAB. Experiments were carried out to observe the performance of the solar collector under changes in air mass flow rate. Experimental values on photovoltaic panel temperature and air temperature on both air inlet and outlet, together with the ambient temperature and solar radiation were measured. The simulation results were validated against the results obtained from experiments using the error analysis method, Root Mean Square Error. At a solar irradiance level of 800 to 900 W/m2, the thermal efficiency increases to 20.32% while the electrical efficiency increases to 12.01% when the air mass flow rate increases from 0.00015 kg/s to 0.01 kg/s. The error analysis shows that both experimental and simulation results are in good agreement.