Building Integrated Photovoltaic Research Articles

Influence of Ti Layers on the Efficiency of Solar Cells and the Reduction of Heat Transfer in Building-Integrated Photovoltaics

This study examined the potential application of metallic coatings to mitigate the adverse effects of ultraviolet (UV) and infrared (IR) light on photovoltaic modules. Titanium coatings were applied on low-iron glass surfaces using magnetron sputtering at powers of 1000, 1250, 1500, 1750, 2000, and 2500 W. The module with uncoated glass served as a reference. The Ti layer thickness varied from 7 nm to 20 nm. Transmittance and reflectance spectra were used to calculate visible light transmittance Lt, UV light transmittance Ltuv, solar transmittance g, and visible light reflectance Lr. The obtained parameters indicated that the thinnest Ti layer (1000 W) coating did not significantly affect light transmittance, but thicker layers did, altering the Lt, g, and Lr factors. However, every sample noticeably changed Ltuv, probably due to the natural formation of a UV-reflective thin TiO2 layer. The differences in fill factor (FF) were minimal, but thicker coatings resulted in lower open-circuit voltages (Uoc) and short-circuit currents (Isc), leading to a reduction in power conversion efficiency (PCE). Notably, a Ti coating deposited at 2500 W reduced the power of the photovoltaic module by 78% compared to the uncoated sample but may protect modules against the unwanted effects of overheating.

Red-color-modulated perovskite solar cells with copper(I) thiocyanate/cellulose acetate–based distributed Bragg reflectors

Abstract This study investigates the development of perovskite solar cells (PSCs) equipped with distributed Bragg reflectors (DBRs) through a coating-based fabrication technique. The incorporation of DBRs enhanced the esthetic appeal and improved the light absorption properties of PSCs, rendering them suitable for building-integrated photovoltaics (BIPV). The DBRs, composed of 3–11 layers of copper(I) thiocyanate and cellulose acetate, exhibited peak reflectance at 700 nm. However, as the number of DBR layers increased, there was a corresponding decrease in short-circuit current density (Jsc) and power conversion efficiency (PCE) due to greater light reflection. Specifically, compared with PSCs without DBRs, those with 11-layer DBRs showed 41.2% and 39.0% reduction in Jsc and PCE, respectively. Despite these reductions, this study highlights the potential of colored PSCs for practical BIPV applications, achieving a balance between esthetics and efficiency.

Optimization of coupling phase-change materials and thermal screens in façade-integrated hybrid photovoltaic collectors for optimal energy production and thermal comfort in buildings

The operation of building-integrated photovoltaic (BIPV) systems gives rise to a significant proportion of the solar radiation absorbed by the cells being unable to be converted into electricity. This phenomenon consequently increases the temperature. This temperature increase impacts the cells' electrical efficiency, leading to a reduction in their performance and accelerating their degradation. The combination of phase-change materials with insulating fluid blades, situated behind photovoltaic cells, represents a passive cooling solution that optimizes the performance of hybrid photovoltaic systems when incorporated into facades. The present study assesses a system that incorporates paraffin as a PCM and an argon layer in a PV-PCM-argon layer physical model (PVT/ArPCM), in comparison with a PV-PCM system (PVT/PCM), to enhance the thermoelectric performance of photovoltaic systems mounted on façades while ensuring optimal thermal comfort within buildings. The discrete heat transfer equations were solved using the Thomas algorithm and the iterative Gauss-Seidel method in conjunction with the implicit finite difference method. The findings illustrate that the electrical efficiency experienced only a slight increase, estimated at 0.01%, while there was a notable enhancement in the indoor thermal comfort experienced by occupants, with a 65% improvement observed due to the incorporation of an argon-filled thermal screen. The incorporation of an argon layer led to a minor reduction in temperature of 0.01°C in the photovoltaic cells, resulting in a minimal improvement of 0.014% in electrical power production. The phase-changing material incorporated into PVT/ArPCM demonstrated superior thermal management capabilities in comparison to the same material employed in PVT/PCM.

Effect of Chlorine Inclusion in Wide Band Gap FAPbBr3 Perovskites

AbstractWide bandgap perovskites have recently gained attention owing to their physical properties, versatility, and potential in various optoelectronic devices including LEDs, detectors, and building‐integrated photovoltaics (BIPV). However, BIPV materials must meet conflicting requirements, necessitating high performance, high transparency in the visible spectrum and color neutrality. This study investigates the controlled addition of chlorine in FAPb(Br1‐xClx)3 perovskites to achieve band gaps exceeding 2.4 eV. Increasing chlorine content from xCl = 0.00 to xCl = 0.25 widens the band gap from 2.37 to 2.52 eV, effectively improving the visible light transparency. Advanced characterization techniques including X‐ray diffraction, synchrotron radiation photoelectron spectroscopy, photoluminescence imaging, and fast transient absorption spectroscopy, complemented by density functional theory, reveal insights into absorption properties, electronic structure, and ultrafast recombination dynamics as a function of the thin film chemical composition. Furthermore, this study evaluates energetic disorder, carrier recombination rates, and non‐radiative losses for different compositions by extracting quantitative parameters such as Urbach energy and quasi‐Fermi level splitting, offering novel insights and guidelines for the design and optimization of emerging photovoltaic (PV) materials. Optimal PV performance metrics are achieved with a wide bandgap bromine perovskite containing 14% chlorine, striking a balance between morphology, transparency, and voltage losses.

Double-Shell Encapsulation of Lead-Free Tin Halide Perovskite for Self-Powered Smart Windows.

Luminescent solar concentrators (LSC) have the potential application in building integrated photovoltaic (BIPV). 0D tin-based perovskites are a promising embedding phosphor in LSC due to the large Stokes shift and high photoluminescence quantum yield. But the instability and uncontrollable crystal growth are severe limiting their successful utilization in device fabrication. To tackle these issues, double-shell encapsulated configurations are presented, soft ligands of hypophosphorous acid and hard-shell of hollow mesoporous silica are simultaneously suppressing the oxidation of Sn2+ and restricting crystal growth within nano-matrix. The stable phosphor is subsequently embedded into LSC for harvesting solar energy and the resulting output power efficiently drives the combined electrochromic glass under natural light. These fabricated devices also offer the self-adaptable switch on/off functionality to regulate light absorption with variable solar irradiation intensity in real time. This approach is anticipated to open new avenues for utilizing lead-free perovskite nanomaterials in self-powered smart windows for BIPV.

City-wide Solar Radiation Potential Analysis by Coupling Physical Modelling and Machine Learning

Abstract. Addressing the challenges posed by climate change and meeting urban energy demands is of utmost importance in today's world. Building Integrated Photovoltaics (BIPV) emerges as a crucial solution for energy conservation and carbon emissions reduction in urban environments. However, traditional methods of assessing solar radiation on buildings using physical models are often computationally intensive and time-consuming. This paper introduces a novel hybrid approach that integrates physical model-based solar radiation calculation with machine learning techniques to analyze Solar Radiation Potential (SRP) across city-wide building infrastructure. The proposed approach precisely evaluates the SRP of representative blocks by leveraging computing-intensive physical models integrated with 3D building data. Subsequently, two machine learning models are developed to effectively predict the SRP of building roofs and facades across the entire city. To validate the efficacy of this approach, an experiment was conducted in Shenzhen, China, yielding insightful results. The findings reveal that Shenzhen has a huge potential for BIPV solar power generation, with mean annual total building roof and facade solar radiation values of 9.22*107 kwh and 2.47*108 kwh, respectively. It can be further observed that relying solely on rooftop installations is insufficient to meet electricity demand. This study not only provides an innovative alternative for city-wide SRP estimation by combining physical modeling and machine learning but also offers valuable insights for fostering low-carbon urban environments and informing data-driven and model-driven urban planning and management strategies.

The Impact of Ambient Weather Conditions and Energy Usage Patterns on the Performance of a Domestic Off-Grid Photovoltaic System

Solar photovoltaic (PV) systems are growing rapidly as a renewable energy source. Evaluating the performance of a PV system based on local weather conditions is crucial for its adoption and deployment. However, the current IEC 61724 standard, used for assessing PV system performance, is limited to grid-connected systems. This standard may not accurately reflect the performance of off-grid PV systems. This study aims to evaluate how ambient weather conditions and energy usage patterns affect the performance of an off-grid PV system. This study uses a 3.8 kWp building-integrated photovoltaic (BIPV) system located at SolarWatt Park, University of Fort Hare, Alice, as a case study. Meteorological and electrical data from August and November are analyzed to assess the winter and summer performance of the BIPV system using the IEC 61724 standard. The BIPV system generated 376.29 kWh in winter and 366.38 kWh in summer, with a total energy consumption of 209.50 kWh in winter and 236.65 kWh in summer. Solar irradiation during winter was 130.18 kWh/m2, while it was 210.24 kWh/m2 during summer. The average daily performance ratio (PR) was 44.01% in winter and 28.94% in summer. The observed decrease in PR during the summer month was attributed to the higher levels of solar irradiance experienced during this time, which outweighs the increased AC energy output. The low-performance ratio does not indicate technical issues but rather a mismatch between the load demand and PV generation. The results of this study highlight the need for a separate method to assess the performance of off-grid PV systems.

A study on the microstructure and power generation performance of colored BIPV modules

The need for greenhouse gas reduction and carbon neutrality is increasing, and the Building Integrated Photovoltaic (BIPV) power generation system is emerging as a key element. However, colored BIPV modules that enhance the aesthetic value of buildings have challenges in maintaining power generation efficiency due to their varied optical properties. This work investigates the structural, elemental, and power generation of colored BIPV modules fabricated using Plasma Enhanced Chemical Vapor Deposition (PECVD), inorganic pigment dyeing (HAZE), and color dot printing (DOT) in comparison to a conventional BIPV module without color treatment. Comprehensive structural and elemental analyses were performed, and optical properties were assessed. Power generation performance was measured using an Outdoor-exposed Power Generation Evaluation (OPGE) test at various tilt angles and azimuths. The results indicated a 57.2 % decrease in power output for the PECVD module, while the DOT and HAZE modules showed a reduction of approximately 30 % compared to the reference module. The power output of the colored modules decreased compared to the conventional ones due to higher light reflectance. However, to enhance efficiency, an improved optical design was developed, reducing light reflectance by over 57 % on average. This provides the potential for optimizing both the aesthetic and functional aspects.

Biweight midcorrelation-based CRITIC method: a case study for electrification of BIPV in Iran

Criteria weights are essential in various multi-criteria decision-making (MCDM) approaches. The amount of weight indicates the criterion's importance, and the performance of MCDM techniques depends on the correct estimation of criteria weights and criteria amount in the decision matrix. The distance correlation-based CRiteria Importance Through Inter-criteria Correlation (CRITIC) technique is a modified version of the CRITIC method. Since Pearson's correlation is sensitive to noisy and outlier data, this paper proposes using the biweight midcorrelation as a robust alternative to Pearson's correlation. The weights obtained from the proposed biweight midcorrelation method make using the full range of MCDM techniques possible. This study examined the performance of the proposed method in comparison with eight criteria weighting techniques on the electric supply for a Building-Integrated PhotoVoltaic (BIPV) problem. Results from three different tests (i.e. distance correlation, Spearman rank-order correlation, and symmetric mean absolute percentage error (sMAPE)) indicated that the proposed method could produce more valid criteria weights and ranks than other methods. The value of the sMAPE criterion for the proposed method is 0.1096. Sensitivity analysis also revealed that the proposed method provides more robust criteria weights and ranks with a larger decision matrix.

Optimizing energy efficiency in mediterranean single-family homes: A parametric study of building typology, orientation, and BIPV integration

The energy efficiency of residential buildings is a critical concern, especially in regions with challenging climatic conditions like the Mediterranean. Despite numerous studies on energy-efficient design, there is a lack of specificity in how building typology, orientation, and Building-Integrated Photovoltaics (BIPVs) interact to influence energy performance. This study addresses this gap by conducting a parametric investigation of single-family houses, focusing on variations in building typology and orientation, both with and without BIPV integration. The parameters evaluated include energy consumption for heating, cooling, lighting, and domestic hot water, using simulations performed with iSBEM-Cy, the official tool for energy performance calculations in Cyprus. The results reveal that building orientation can reduce energy consumption, while BIPV integration contributes additional energy savings. Terraced houses with only Building-Applied Photovoltaics (BAPVs) showed the best energy balance (−38.20 kWh/m2/yr), while detached houses had the highest energy consumption (71.24 kWh/m2/yr). With BIPVs, energy balances improved significantly across all typologies, with terraced houses reaching −78.20 kWh/m2/yr, demonstrating the strong potential of BIPV integration. These findings highlight the importance of considering context-specific factors when integrating renewable energy systems, offering insights for architects and urban planners.

Machine learning driven building integrated photovoltaic (BIPV) envelope design optimization

By integrating renewable energy systems (RESs) into buildings, Building-Integrated Photovoltaics (BIPV) reshape the demand–supply relationship, offering substantial benefits such as reduced energy usage, lower greenhouse gas emissions, improved indoor comfort, and architectural enhancement. However, designing optimal BIPV systems involves balancing multiple parameters and conflicting objectives, making it a complex, computationally intensive process. This study proposes a data-driven optimization framework to enhance BIPV envelope design during the detailed design phase. The novelties of this research lie in 1) the use of Artificial Neural Networks (ANN) as a surrogate model for rapid prediction, and 2) a multi-objective optimization (MOO)framework tailored for BIPV design at the detailed design phase. The proposed framework integrates ANN-based predictive modelling with an NSGA-II optimization component to generate optimal BIPV configurations efficiently. Implemented in Python, this approach is validated through a benchmark case study in Melbourne, demonstrating its effectiveness in reducing computational demands while achieving balanced energy, economic, and indoor comfort performance. This research advances sustainable building design by addressing gaps in current optimization frameworks and providing practical tools for architects and designers, promoting the wider adoption of BIPV solutions with significant computational savings.

SolarSAM: Building-scale Photovoltaic Potential Assessment Based on Segment Anything Model (SAM) and Remote Sensing for Emerging City

Driven by advancements in photovoltaic (PV) technology, solar energy has emerged as a promising renewable energy source due to its ease of integration onto building rooftops, facades, and windows. For emerging cities, the lack of detailed street-level data presents a challenge for effectively assessing the potential of building-integrated photovoltaic (BIPV). To address this, this study introduces SolarSAM, a novel BIPV evaluation method that leverages satellite imagery and deep learning techniques, and an emerging city in northern China is utilized to validate the model performance. SolarSAM segmented various building rooftops using text prompt-guided semantic segmentation during the process. Separate PV models were then developed for Rooftop PV, Facade-integrated PV, and PV windows, using this segmented data and local climate information. The potential for BIPV installation, solar power generation, and city-wide power self-sufficiency were assessed, revealing that the annual BIPV power generation potential surpassed the city’s total electricity consumption by a factor of 2.5. Economic and environmental analysis were also conducted for the BIPVs on different buildings; the levelized cost of electricity is 0.18-0.41 CNY/kWh, and the annual total carbon reduction is 7.08×107 T CO2. These findings demonstrated the model’s performance and revealed the potential for BIPV power generation.

ANALIZA PRELIMINARĂ DE VALORIFICARE A SISTEMELOR FOTOVOLTAICE INTEGRATE ÎN CLĂDIRI LA ÎNTREPRINDERILE TERMOENERGETICE

The research carried out in this paper focuses on the study of the integration of photovoltaic panels into buildings, a solution known as BIPV (Building Integrated Photovoltaics), which represents a new form of solar energy use: these are building materials, but these also facilitate the generation of solar electricity on additional surfaces and allow an optimisation of the overall operation as a main source of electricity and can also be seamlessly integrated into the building envelope and its components. In the paper, the opportunities for development of building-integrated photovoltaic systems at thermal power enterprises were analysed, using concrete data of the JSC "CET-Nord" in the Republic of Moldova as an example for further research, as well as determining the potential photovoltaic capacity of administrative and production buildings of the enterprise. This will help to improve the understanding of the impact of BIPV systems on the energy transition of cities and on the notion of near-zero energy cities in Europe, by identifying possible future approaches to BIPV that can be used by energy specialists as well as architects and urban planners to assess how much of the energy used by buildings could be provided by BIPV systems when implemented as building envelope materials across the entire building envelope.

An overview on building-integrated photovoltaics: technological solutions, modeling, and control

The advancement of renewable and sustainable energy generation technologies has been driven by environment-related issues, energy independence, and high costs of fossil fuels. Building-integrated photovoltaic systems have been demonstrated to be a viable technology for the generation of renewable power, with the potential to assist buildings in meeting their energy demands. This work reviews the current status of novel PV technologies, including bifacial solar cells and semi-transparent solar cells. This review discusses the various constructions of PV technologies, recent advances in these products, the influence of key design factors on electrical and thermal performance, and their potential in the design of energy-efficient smart buildings. The attention is focused on bifacial and semi-transparent PV systems, given the high level of interest of the scientific community in their current and potential applications.Focus is also devoted to the analysis of the electrical, optical, and thermal modeling procedures developed for sizing, designing, and integrating photovoltaics into larger building simulations. The development of these models has a positive impact on the implementation of next-generation smart buildings. The latest innovative developments and key issues in the application of bifacial PV solutions in buildings are also summarized and analyzed. Special attention is paid to rear side electrical performance, which can be evaluated by means of illuminance/optical backside modeling. Finally, energy management and control of PV-equipped buildings via both model-based and data-driven approaches are discussed, as well as the integration of electric storage systems in a multi-building context.

Towards a high resolution simulation framework for building integrated photovoltaics under partial shading in urban environments

This paper presents a framework for modeling the performance of photovoltaic (PV) systems, which is intended to be used in the design, optimization, and deployment of PV systems in urban environments. Urban PV and building integrated PVs (BIPV) are a crucial component of the transition to a low-carbon society. However, existing frameworks for simulating PV systems accurately characterize PV under partial shading, leading to performance gaps with systems deployed in environments such as cities where shading is more common than in large open fields. In this paper, we extend a framework from literature for modeling parametric BIPV arrays using a power model that characterizes IV-curves at the PV cell. We describe the framework and perform a validation against a measured rooftop PV array dataset. We then evaluate the extended framework to other PV modeling and simulation frameworks found in the literature through the simulation of a simplified facade under various levels of shading. Results show that the extended framework is necessary to account for partial shading, but there may be use cases when lower resolution frameworks may be just as accurate. Amongst the frameworks a range of predicted power outputs under the shading conditions was observed; from 6% to 846% with a mean of 162% from the other frameworks relative to the proposed framework. The range is due to the extensive set of conditions tested, where it was observed that in conditions where shading is present but sparse, there is a larger range of predicted output from the various frameworks. Whereas in conditions where shading coverage is high this range is lower. Lastly, we demonstrate the use of the framework on three arrays. Again it was observed that homogeneous shading conditions may not require the level of resolution provided by the proposed framework, while heterogeneous could benefit the most from the approach. This indicates, and is discussed in the paper, that modelers should consider the shading conditions present when deciding on which framework to apply.

An experimental investigation into the use of biomimetic methods for thermal regulation and heat retention with PCMs in buildings

To meet the goal of net-zero emissions by 2050, integrating renewable energy into building energy supplies is a key solution, particularly through the application of solar energy. Due to the intermittent nature of solar energy, effective thermal regulation and heat transfer using thermal mass materials play critical roles in energy efficiency, safety, and performance in building applications. For solar electricity production in building integration, the high temperatures in Building Integrated Photovoltaics (BIPV) need to be regulated to avoid the reduction of solar power conversion efficiency and to maximize the utilization of excess thermal energy. This requires effective energy charging and discharging to meet energy demands.A biomimetic method has been experimentally investigated to enhance the thermal regulation and the thermal energy retention capacity of hybrid PVT-PCM/EG systems for building thermal management. By mimicking natural systems, the PVT-PCM-EG system promotes sustainability and energy efficiency, contributing to reduced energy consumption and lower carbon emissions. Based on the testing condition of around 480 Wm-2 and 15 °C, it is recommended that the combined two transient temperature PCM/EG A28 and A36 for the PVT-PCM/EG system can achieve the best performance for both heat retention and PV temperature regulation.

Comprehensive Review of the Advancements, Benefits, Challenges, and Design Integration of Energy-Efficient Materials for Sustainable Buildings

Energy-efficient materials are essential in buildings to reduce energy consumption, lower greenhouse gas emissions, and enhance indoor comfort. These materials help address the increasing energy demand and environmental impact of traditional construction methods. This paper presents a comprehensive literature review that explores advanced materials and technologies for improving building energy efficiency, sustainability, and occupant comfort. The study applies a comparative analysis of peer-reviewed research to examine key technologies analyzed include building-integrated photovoltaics, advanced insulating materials, reflective and thermal coatings, glazing systems, phase-change materials, and green roofs and walls. The study highlights the significant energy savings, thermal performance, and environmental benefits of these materials. By integrating these technologies, buildings can achieve enhanced energy efficiency, reduced carbon footprints, and improved indoor comfort. The findings underscore the potential of advanced building materials in fostering sustainable construction practices. The methodology of this review involves collecting, analyzing, summarizing, comparing and synthesizing existing research to draw conclusions on the performance and efficiency of these technologies.

Outdoor Performance Analysis of Semitransparent Photovoltaic Windows with Bifacial Cells and Integrated Blinds

The stricter requirements for the energy performance of buildings are creating a market for several building‐integrated photovoltaic (BIPV) technologies, including photovoltaic (PV) windows. Herein, an innovative multifunctional PV window concept designed to enhance energy generation while providing overheating protection for better indoor thermal and visual comfort is presented. This concept utilizes bifacial c‐Si solar cell strips combined with venetian blinds, all embedded in a unique insulating glazing unit. The bifacial technology increases the energy yield by using the blinds as reflectors, directing more irradiance to the cells’ rear side. The goal of this study is to analyze the outdoor performance of this concept under real operating conditions. Twelve demonstrators are installed and monitored. Various measurement campaigns are conducted, examining the impact of different blind types, tilt angles, sun positions and sky conditions. The highest energy boosts occur when the blinds are fully closed at a 75° angle with their convex side facing outward. Blinds with the highest specular reflectance achieve a maximum performance increase of 25% on sunny days and a daily average increase of 12% compared to the case of no blinds.

A study on the enhancement of energy efficiency and indoor environment through the integration of solar photovoltaic modules in daylighting louver systems

The global energy demand is predicted to increase significantly, with a strong link between energy consumption and CO2 emissions. Lighting energy accounts for a substantial proportion of total energy consumption in buildings, with the potential to be reduced using daylighting louvers. Despite some drawbacks, integrating solar photovoltaic modules with louvers could enhance energy efficiency while addressing the increasing demand for renewable energy systems in new buildings. Therefore, this study explores the potential benefits of integrating solar photovoltaic (PV) modules into a daylighting louver system to simultaneously reduce lighting, cooling, and heating loads and generate solar power. The performance of the proposed louver system was experimentally compared to a conventional building-integrated photovoltaic (BIPV) system installed on a window. The daylighting louver system demonstrated significant improvements in indoor illumination and occupants’ experience compared to the BIPV system. Furthermore, the indoor air temperature in the space with the louver system increased by about 5 degrees compared to the BIPV system, indicating its contribution to heating load reduction. The power generation performance of the louver system reached up to 65 % of the BIPV system, with a total cumulative electricity generation of 64 %. The results suggest that the daylighting louver system can be expanded to enhance its efficiency and address the increasing demand for renewable energy system installations in new buildings. Future studies should investigate the potential of 2-axis or 3-axis louvers to further improve the system’s efficiency.

Large-scale prediction of solar irradiation, shading impacts, and energy generation on building Façade through urban morphological indicators: A machine learning approach

Incorporating Building-integrated Photovoltaics (BIPVs) into urban environments offers a sustainable approach by transforming building exteriors into renewable energy sources, especially by utilizing the extensive vertical surfaces in cities. While individual building applications have been extensively studied, there is a lack of research on BIPV potential at the urban scale due to the complexity of urban fabrics. Therefore, this study presents a novel method for predicting large-scale solar irradiation and energy generation on building surfaces using urban morphological indicators through machine learning models. Using an Australian city as a case study, a hierarchical two-level machine learning model is developed to predict the solar potential for building façade PV systems. The model incorporates 13 urban morphological predictors to determine average shading height and unshaded solar irradiance. By combining these predictions with wall datasets and different façade BIPV efficiencies, the proposed method predicts unshaded electricity generation. The results showcase high efficiency and accuracy in the prediction of the shading height, solar irradiance, and electricity generation. This innovative approach aids urban planners in considering large-scale surface BIPVs into urban development.