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Ítem Photovoltaic Self-Consumption in Industrial Cooling and Refrigeration(MDPI, 2020-12-21) Martínez-Calahorro, Antonio Javier; Jiménez-Castillo, Gabino; Rus-Casas, Catalina; Gómez-Vidal, Pedro; Muñoz-Rodríguez, Francisco JoséThe industrial sector has a great opportunity to reduce its energy costs through distributed generation. In this sense, the potential of photovoltaic self-consumption systems in the industrial cooling and refrigeration sector is shown. Two industries with photovoltaic self-consumption installations are shown and the electricity consumption profile of this type of industry which has a remarkable basal electricity consumption during daytime is analyzed. The matching between consumption and photovoltaic generation profiles is provided through the self-consumption and self-sufficiency curves considering different reporting periods (monthly and annual). Moreover, a new index is presented: self-sufficiency index for sunshine hours, φSS,SH. This index evaluates the performance of the photovoltaic self-consumption system when facing the consumption only during sunshine hours. This index may complement the self-sufficiency index and may improve the analysis of this type of systems in the industrial sector. Self-consumption indices of 90% may be provided. Moreover, self-sufficiency indices for total (24 h) and for sunshine hours of 25% and 50%, respectively, for industry A, and 26% and 45% for industry B have been obtained. During daytime, half the load consumption in this type of industry may be covered by photovoltaics while achieving high levels of use of the photovoltaic energy generated.Ítem Impacts of Array Orientation and Tilt Angles for Photovoltaic Self-Sufficiency and Self-Consumption Indices in Olive Mills in Spain(MDPI, 2020-02-18) Jiménez-Castillo, Gabino; Muñoz-Rodríguez, Francisco José; Martinez-Calahorro, Antonio Javier; Tina, Giuseppe Marco; Rus-Casas, CatalinaOlive mills are extensive in the Mediterranean Basin, and Spain constitutes approximately 45% of global production. The industrial sector faces a new energetic paradigm where distributed generation provided by small renewable energy sources may reduce the dependence from fossil energy sources as well as avoid energy distribution losses. Photovoltaic self-consumption systems can play an important role in confronting this challenge due to their modularity and their decreasing cost. Most of self-sufficiency energy studies are focused on building sector and discussions about the idiosyncrasy of industrial load profiles, and their matching capability with photovoltaic generation profiles can be scarcely found. This work analyzes the potential of photovoltaic self-consumption systems as a function of the array power, array tilt, and orientation angles to face the electric consumption in olive mills. Different recording intervals and reporting periods are considered. Results show that a self-sufficiency index of 40% may be achieved on olive harvest basis. Moreover, due to the load profile particularities, percentage error lower than 1.6% has been found when considering a recording interval of 60 min when matching the olive load consumption and photovoltaic generation profiles. Chosen array tilt and orientation angles may be key parameters to maximize the self-sufficiency index.Ítem Development of a Prototype for Monitoring Photovoltaic Self-Consumption Systems(MDPI, 2020-01-01) Rus-Casas, Catalina; Jiménez-Castillo, Gabino; Aguilar-Peña, Juan Domingo; Fernández-Carrasco, Juan Ignacio; Muñoz-Rodríguez, Francisco JoséCurrently, the increasing energy consumption around the world and the environmental impact resulting from the use of fossil fuel-based energy have promoted the use of renewable energy sources such as photovoltaic solar energy. The main characteristic of this type of energy is its unpredictability, as it depends on meteorological conditions. In this sense, monitoring the power generation of photovoltaic systems (PVS) in order to analyze their performance is becoming crucial. The purpose of this paper is to design a monitoring system for a residential photovoltaic self-consumption system which employs an Internet of Things (IoT) platform to estimate the photovoltaic power generation according to solar radiation and temperature. The architecture of the developed prototype will be described and the benefits of providing the use of IoT for monitoring will be highlighted, since all data collected by the data acquisition system (DAS) may be stored in the Cloud. The comparison of the results with those of other monitoring systems was very positive, with an uncertainty that complies with the IEC61724 standard.Ítem Improvements in Performance Analysis of Photovoltaic Systems: Array Power Monitoring in Pulse Width Modulation Charge Controllers(MDPI, 2019-05-09) Jiménez-Castillo, Gabino; Muñoz-Rodríguez, Francisco José; Rus-Casas, Catalina; Gómez-Vidal, PedroVarious challenges should be considered when measuring photovoltaic array power and energy in pulse width modulation (PWM) charge controllers. These controllers are frequently used not only in stand-alone photovoltaic (SAPV) systems, but may also be found in photovoltaic (PV) self-consumption systems with battery storage connected to the electricity grid. An acceptable solution may be reached using expensive data acquisition systems (DASs), although this could be generally disproportionate to the relatively low cost of SAPV systems. Therefore, the aim of this paper is to develop new and e ective monitoring techniques which will provide the PV array direct current (DC), output power (PA,dc), and PV array DC output energy (EA), thus avoiding the use of sophisticated DASs and providing high accuracy for the calculated parameters. Only transducers and electronic circuits that provide the average and true rms values of the PWM signals are needed. The estimation of these parameters through the aforementioned techniques showed high accuracy for both series and shunt PWM battery charge controllers. Normalized root mean square error (NRMSE) was lower than 2.4%, normalized mean bias error (NMBE) was between 1.5% and 1.1%, and mean absolute percentage error (MAPE) was within 1.6%.Ítem Development of a Utility Model for the Measurement of Global Radiation in Photovoltaic Applications in the Internet of Things (IoT)(MDPI, 2019-03-08) Rus-Casas, Catalina; Hontoria, Leocadio; Fernández-Carrasco, Juan Ignacio; Jiménez-Castillo, Gabino; Muñoz-Rodríguez, Francisco JoséIn order to develop future projects in the field of photovoltaic solar energy, it is essential to accurately know the potential solar resources. There are many methods to estimate the incident solar radiation in a certain place. However, most of them are very expensive or do not have the ideal characteristics for good monitoring of a particular photovoltaic installation. For these reasons, an electronic device connected to the internet of things (IoT) is presented in this paper which manages to measure global radiation in photovoltaic applications. The device developed has been patented in the Spanish Patent and Trademark Office. It presents some features that make it very suitable to measure photovoltaic installations with the advantage of being a low cost and very reliable device. The device has been tested to determine global horizontal irradiance obtaining a correlation coefficient R2 = 0.994.Ítem Comparative analysis of direct inclined irradiance data soruces for micro-tracking concentrator photovoltaics(MDPI, 2025-06-05) Pérez-Higueras, Pedro; Ceballos, María Ángeles; Mouhib, Elmehdi; Bessa, Joao Gabriel; Montes-Romero, Jesús; Mata-Campos, RaulIn recent years, the scientific community has intensified its efforts to develop a new type of concentrator photovoltaic module that is competitive with conventional modules. These modules are based on internal tracking systems, known as micro-tracking concentrator photovoltaic modules, which generate electrical energy proportional to the direct radiation on the inclined surface. There are several reviews, databases, and models for various components of solar radiation, particularly for global and direct normal radiation. However, readily available data on direct inclined irradiance remain scarce. This paper reviews several available sources of solar radiation data, finding that only the Photovoltaic Geographic Information System and Solar Radiation Database provide direct inclined irradiance data. A comparative statistical analysis was carried out, and a reasonable fit was obtained between both databases. In addition, direct inclined radiation data extracted from these databases were compared with the values calculated using a well-established mathematical model. In addition, worldwide maps were generated to determine areas of interest for this technology. Therefore, this paper presents an original comparative analysis of existing databases containing information on direct inclined irradiation. This information is of interest for the accurate design and performance analysis of micro-tracking concentrator modules.Ítem A new approach to analyse from monitored data the performance, matching capability and grid usage of large Rooftop Photovoltaic systems. Case of study: Photovoltaic system of 1.05 MW installed at the campus of University of Jaén(Elsevier, 2025) Muñoz-Rodríguez, Francisco José; Gómez-Vidal, Pedro; Fernández-Carrasco, Juan Ignacio; Tina, Giuseppe Marco; Jiménez-Castillo, GabinoRooftop photovoltaic installations highlight their potential to meet a significant portion of urban electricity demand. These systems range from a few kW in residential areas to hundreds of kW in large Rooftop PV systems in commercial and industrial settings. The latter, which may include several inverters or arrays with different orientations and inclinations, require a proper analysis to assess the potential of this technology and to ensure the design objectives. This paper presents a methodology for analysing from monitored data large Rooftop PV systems, focusing on performance, self-consumption and grid usage. The approach is scalable, applicable at the inverter, individual Rooftop PV and global system levels. New key parameters defined include weighted system irradiation (HI,weighted) and weighted system reference yield (Yr,weighted), which account for different array orientations and inclinations. The methodology is validated using a 1.05 MW system at the University of Jaén with monitored data over a year. Results indicate subsystem and system PR values above 0.83 and a system Capacity Factor of 0.19, confirming a proper performance. Annual self-consumption and self-sufficiency indices of 97.5 % and 17.7 %, respectively, and a solar hour self-sufficiency of 27.7 % reveal minimal energy export and substantial potential to meet the university’s electricity demand.Ítem Effect of electrical operating conditions on thermal behavior of PV modules: Numerical and experimental analysis(Elsevier, 2025) Osama, Amr; Tina, Giuseppe Marco; Gagliano, Antonio; Jiménez-Castillo, Gabino; Muñoz-Rodríguez, Francisco JoséThe rapid growth of photovoltaic (PV) energy has the potential to transform the global energy landscape. However, the intermittent nature of solar power presents significant challenges to grid integration, such as overgeneration and curtailment. Consequently, PV systems may operate at points other than the maximum power point (MPP). Monitoring the thermal behavior of photovoltaic systems is critical due to its impact on productivity and system health. Most studies focus on meteorological variables, often overlooking the influence of electrical operating states on thermal performance. Thus the objective is to evaluate the accuracy of existing thermal models from the literature and widely used specialized software tools—alongside their commonly cited coefficients against different electrical operating status (EOS). This study investigates the thermal behavior of PV modules under different EOS: short-circuited (PVset-1), open-circuited (PVset-2), and operating at MPP (PVset-3). The experiment was conducted over four months at Jaén University campus in Spain. Results showed the short-circuited module's temperature was 6.90 °C higher, and the open-circuited module's temperature was 3.67 °C higher than the MPP module. Thermographic investigations revealed multiple hotspots in the short-circuited set. These hotspots can severely impact the module's long-term reliability and efficiency. The analysis of thermal models considering these operating states indicated an overestimation of the MPP module's temperature. However, the Keddouda model demonstrated high accuracy potential, with an average deviation of less than 3.4 %, particularly at high irradiance levels. These findings highlight the necessity of considering EOS in thermal models to enhance the accuracy and reliability of PV system performance assessments.Ítem 3D object recognition for anthropomorphic robots performing tracking tasks(Springer, 2019-07-03) Satorres Martínez, Silvia; Sánchez García, Alejandro; Estévez Estévez, Elisabet; Gómez Ortega, Juan; Gámez García, JavierObject recognition is still a major research issue of particular relevance in robotics. The new trend in industrial and mainly in service robotics is to perform manipulation tasks in an unstructured environment working in synergy with humans. To perform tasks in an environment that is not perfectly controlled, robots need adequate perceptual capabilities. Among various types of sensors available for robotic systems, the time-of-flight (ToF) camera is one of the most utilized since it simultaneously provides intensity and depth data at a high frame rate. Our proposal makes use of this technology exploiting both, depth and grey-scale information. Therefore, intensity and geometric features are fused together to allow 3D object recognition in real scenes in presence of partial occlusions. As a case study, an object tracking task for an anthropomorphic robot is presented. Experimental results demonstrate the effectiveness of the proposed method, not only providing reliable visual information about the object to be tracked but also recognizing potential obstacles which should be avoided during the robot arm movement.Ítem The Advantage of Multispectral Images in Fruit Quality Control for Extra Virgin Olive Oil Production(Springer, 2021-08-17) Martínez Gila, Diego M.; Navarro Soto, Javiera P.; Satorres Martínez, Silvia; Gómez Ortega, Juan; Gámez García, JavierThe highest quality of extra virgin olive oil must be guaranteed to succeed in the competitive market of the industry. To fulfil this need, high standards of fruit quality are required. In recent years, this search has led to the development of computer vision systems for the automatic inspection of fruits. Skin damage, dirtiness, and external colour were the main features selected. However, no known report is related to the internal quality evaluation of olives. The present research explores the application of multispectral images (from 600 to 975 nm) to discriminate olive fruits by their firmness. To achieve this aim, 58 olives were classified as hard or soft by an expert and then measured by a penetrometer. Afterwards, 58 multispectral images were obtained, and 8352 pixels were randomly selected for feature extraction. The feature vector of each pixel was composed of 25 infrared absorption values. These data were input into three supervised classification algorithms: a naïve Bayes (NB) classifier, a multilayer perceptron (MLP), and a support vector machine variation (SMO). The highest performance was achieved by the MLP, with an accuracy of 95% for the firmness grading. Furthermore, the three algorithms were subjected to principal component analysis (PCA), by which the MLP was confirmed to be the best model and its results were improved. Moreover, according to the second principal component, the 675 nm and 855 nm band frequencies have the strongest weights. The overall results confirm the convenience of multispectral imaging in olive firmness inspection, with the advantage of being a non-invasive technology.Ítem Application of a Lab-Made Voltametric Electronic Tongue to Identify Musty and Vinegary Defects in Olive Oils(Springer, 2022-11-25) Martínez Gila, Diego Manuel; Estévez Estévez, Elisabet; Gómez Ortega, Juan; Gámez García, JavierThe olive oil sector is undergoing a technological transformation promoted by high international competitiveness. This transformation must be aligned with the concepts of industry 4.0 that are already well defined and implemented in other sectors. The integration of advanced sensorics in the process phase in which the quality of the manufactured product is inspected is key to responding in the shortest possible time to deviations in the desired quality. In this work, the results of the experimentation carried out with an e-tongue voltammetric sensor are presented to evaluate its potential in the detection of two of the organoleptic defects that appear more frequently in olive oil (musty and vinegary). This sensor has been built in the research group’s laboratory and is made up of three metals in the measurement probe (nickel, silver and copper). Three classification algorithms (Support Vector Machines, Naïve Bayes and Classification Trees) were used and musty-type defect was identified with a success rate of 72%, while the vinegary-type defect was detected with a success rate of 84%.Ítem Olive fly sting detection based on computer vision(ELSEVIER, 2019-11-14) Beyaz, Abdullah; Martínez-Gila, Diego Manuel; Gómez-Ortega, Juan; Gámez-García, JavierOlive fly (Bactrocera oleae Rossi) is a parasite that infects olive fruit by stinging the fruit to deposit its eggs. The infection damages the fruit during its growth and affects the quality of the olive oil as a final product. Detection of olive fly stings in the virgin olive oil production process is therefore critical. This research aimed to develop automatic methodologies based on computer vision to detect fly stings in infected olive fruit samples. Different methodologies to detect defective areas on the surface of the fruit and classify them between stings and other bruises are described. The methodology to detect defective areas reached a success ratio of 93% and best classification algorithms reached a success ratio close to 80%. The proposed methodology could be applied at reception of fruit for input of the virgin olive oil production process.Ítem Classification of olive fruits and oils based on their fatty acid ethyl esters content using electronic nose technology(SPRINGER, 2021-08-20) Martínez Gila, Diego Manuel; Sanmartin, Chiara; Navarro Soto, Javiera; Mencarelli, Fabio; Gómez Ortega, Juan; Gámez García, JavierAmong several parameters defined for the commercial classes of virgins olive oils (VOOs), there is one, the fatty acid ethyl ester (FAEE), that is only define for the best quality (EVOO). Fruit condition mainly determine these compounds, although, extraction process or deplorable storage condition could rise them up. The FAEE oxidation compound are originated by adding an alcohol chain into the oil molecule. Therefore, the hypothesis of this study is that the inherent constitution of FAEE entails a modification of the volatile profile of oils and olives and this is significant enough to be detected using an electronic nose. With this aim, different samples of olives and oils were analyzed in an accredited laboratory. On the other hand, volatiles from the same samples were captured by an electronic nose. The classification problem was analyzed from two points of view or models. The first was to classify fruits and oils based on whether they are within or outside the legal limits. And the second problem was oriented to classify fruits and oils based on their high or low FAEE content but being within the legal limits. To solve this problem, three classification algorithms were evaluated: Naïve Bayes (NB), Multilayer Perceptron (MLP) and Sequential Minimal Optimization (SMO). For the first model, a well-classified sample rate of 80.3% was obtained for NB and 100% for SMO and MLP, for measurements on oils. The same model evaluated with measurements on olives yielded a success rate of 87.5% with NB, 87.7% with MLP and 82.1% with SMO. For the second model, the suc- cess rates remained within the same orders of magnitude. For measurements on oils, the results were 89.7% for NB, 92.5% for MLP and 100% for SMO. And for measurements on olives the results were 77.9% for NB, 88.6% for MLP and 90.9% for SMO. In all cases, the characteristics that worked best were those obtained from the first derivative of the electronic nose response. Based on these results, the e-nose demonstrate to be a non-invasive technology suitable for the classification of olive fruits and oils based on their FAEE content.Ítem Fast tool based on electronic nose to predict olive fruit quality after harvest(ELSEVIER, 2019-11-14) Martínez-Gila, Diego Manuel; Gámez-García, Javier; Bellincontro, Andrea; Mencarelli, Fabio; Gómez-Ortega, Juanuality analyses of oil from olive fruit are performed according to regulated procedures and in accredited la- boratories that are usually separated from the oil mill. These analytics include organoleptic features involving smelling by human experts. Therefore, oil features depend on the physicochemical conditions of the harvested fruit. An automatic and non-invasive system for monitoring and controlling the process in postharvest stages could optimize the quality of the processed oil. To validate this hypothesis we proposed a methodology based on an electronic nose sensor and pattern recognition algorithms to predict the quality of the oil to be processed from measurements on freshly harvested olive fruit. The pattern recognition algorithms applied were the Naïve Bayes (NB) classifier, the partial least squares discriminant analysis (PLSDA) and a multilayer perceptron (MLP) ar- tificial neural network. Using the measurements performed on 82 samples of olives, the best result was obtained with the MLP network, with 90.2 % success obtained in the classification of the virgin and extra virgin olive oil quality by applying 10-fold cross-validation. Integration of this methodology to virgin olive oil production allows prediction of the quality of the final oil from the olive fruit received from the farmer.Ítem Classification of olive fruits and oils based on their fatty acid ethyl esters content using electronic nose technology(SPRINGER, 2021-08-20) Martínez Gila, Diego Manuel; Sanmartin, Chiara; Navarro Soto, Javiera; Mencarelli, Fabio; Gómez Ortega, Juan; Gámez García, JavierAmong several parameters defined for the commercial classes of virgins olive oils (VOOs), there is one, the fatty acid ethyl ester (FAEE), that is only define for the best quality (EVOO). Fruit condition mainly determine these compounds, although, extraction process or deplorable storage condition could rise them up. The FAEE oxidation compound are originated by adding an alcohol chain into the oil molecule. Therefore, the hypothesis of this study is that the inherent constitution of FAEE entails a modification of the volatile profile of oils and olives and this is significant enough to be detected using an electronic nose. With this aim, different samples of olives and oils were analyzed in an accredited laboratory. On the other hand, volatiles from the same samples were captured by an electronic nose. The classification problem was analyzed from two points of view or models. The first was to classify fruits and oils based on whether they are within or outside the legal limits. And the second problem was oriented to classify fruits and oils based on their high or low FAEE content but being within the legal limits. To solve this problem, three classification algorithms were evaluated: Naïve Bayes (NB), Multilayer Perceptron (MLP) and Sequential Minimal Optimization (SMO). For the first model, a well-classified sample rate of 80.3% was obtained for NB and 100% for SMO and MLP, for measurements on oils. The same model evaluated with measurements on olives yielded a success rate of 87.5% with NB, 87.7% with MLP and 82.1% with SMO. For the second model, the suc- cess rates remained within the same orders of magnitude. For measurements on oils, the results were 89.7% for NB, 92.5% for MLP and 100% for SMO. And for measurements on olives the results were 77.9% for NB, 88.6% for MLP and 90.9% for SMO. In all cases, the characteristics that worked best were those obtained from the first derivative of the electronic nose response. Based on these results, the e-nose demonstrate to be a non-invasive technology suitable for the classification of olive fruits and oils based on their FAEE content.Ítem Photovoltaic Device Performance Evaluation Using an Open-Hardware System and Standard Calibrated Laboratory Instruments(MDPI, 2017-11-15) Jesús Montes Romero, Michel Piliougine; José Vicente Muñoz, Eduardo F. Fernández; Juan De la CasaThis article describes a complete characterization system for photovoltaic devices designed to acquire the current-voltage curve and to process the obtained data. The proposed system can be replicated for educational or research purposes without having wide knowledge about electronic engineering. Using standard calibrated instrumentation, commonly available in any laboratory, the accuracy of measurements is ensured. A capacitive load is used to bias the device due to its versatility and simplicity. The system includes a common part and an interchangeable part that must be designed depending on the electrical characteristics of each PV device. Control software, developed in LabVIEW, controls the equipment, performs automatic campaigns of measurements, and performs additional calculations in real time. These include different procedures to extrapolate the measurements to standard test conditions and methods to obtain the intrinsic parameters of the single diode model. A deep analysis of the uncertainty of measurement is also provided. Finally, the proposed system is validated by comparing the results obtained from some commercial photovoltaic modules to the measurements given by an independently accredited laboratory.Ítem Optical Design of a 4-Off-Axis-Unit Cassegrain Ultra-High CPV Module with Central Receiver(OSA - The Optical Society, 2016-05-01) JUAN P. FERRER-RODRÍGUEZ, EDUARDO F. FERNÁNDEZ; FLORENCIA ALMONACID, PEDRO PÉREZ-HIGUERASUltra-High Concentrator Photovoltaics, UHCPV, with concentrations higher than 1000 suns, has been pointed by different authors to have a great potential for being a cost-effective PV technology. This Letter presents a UHCPV Cassegrain-based optical design in which the sunrays are concentrated and sent from four different and independent paraboloid-hyperboloid pairs optical units onto a single central receiver. The optical design proposed has as main advantage the achievement of ultra-high concentration ratios, using relative small mirrors, with similar performance values of efficiency, acceptance angle and irradiance uniformity than other designs.Ítem Comparative assessment of the spectral impact on the energy yield of high concentrator and conventional photovoltaic technology(Elsevier Ltd, 2016-04) Eduardo F. Fernández, Alberto Soria-Moya; Florencia Almonacid, Jorge AguileraPhotovoltaic materials are spectrally selected and their electrical output is affected by the spectral distribution of the incident irradiance. The performance of high concentrator photovoltaic (HCPV) systems is more influenced by the spectral changes than conventional single-junction photovoltaic (PV) systems due to the use of multi-junction (MJ) solar cells and optical devices. Despite this, the detailed comparison of the spectral impact on the electrical output of HCPV and PV technology under the same atmospheric conditions has not been addressed yet. Because of this, this paper aims to compare the spectral impact on the energy yield of both type of devices at a monthly and annual time scale at several locations with disparate climate conditions. The spectral dependence of both technologies is quantified by using the spectral factor (SF) index in conjunction with the Simple Model of Atmospheric Radiative Transfer of Sunshine (SMARTS) at five locations of the Aerosol Robotic Network (AERONET) database. The present paper shows that the current HCPV systems present annual spectral losses of around 5% with respect to PV systems at representative locations.Ítem A method for estimating the cell temperature at maximum power point of a HCPV module under actual operating conditions(Elsevier Ltd, 2014-05) Eduardo F. Fernández, P. Rodrigo; F. Almonacid, P. Pérez-HiguerasThe operating cell temperature of a HCPV module or system is a key factor, because it directly affects efficiency and reliability. Hence, the accurate estimation of cell temperature in a HCPV module is crucial. Under real operating conditions, HCPV modules work at the maximum power point, because they are connected to an inverter. At the maximum power point, the cell temperature is lower than the cell temperature of open circuit, because solar cells are generating power which is not transformed into heat. At present, none of the existing methods are valid to estimate the operating cell temperature of a HCPV module connected to an inverter. In this paper, a procedure for estimating this temperature is introduced. The results show that the proposed method performs effectively in the estimation of the cell temperature in a HCPV module connected to an inverter. In addition, an analysis of the difference between the cell temperature of open circuit and the cell temperature at the maximum power point in a HCPV is conducted and a difference of up to 21 °C has been found.Ítem Calculation of the cell temperature of a High Concentrator Photovoltaic (HCPV) module: a study and comparison of different methods(Elsevier Ltd, 2014-02) Eduardo F. Fernández, F. Almonacid; P. Rodrigo, P. Pérez-HiguerasAscertaining the operating cell temperature of a high concentrator photovoltaic (HCPV) module is critical because its electrical parameters are influenced by this factor. However, measuring the cell temperature of an HCPV module is a complex task due to the unique features of such a module. This paper calculates the cell temperature in an HCPV module by using different methods to address this important issue. We conducted a comparative study of four methods used to estimate the cell temperature of an HCPV module, including the IEC 60904-5 method, a method based on thermal resistance proposed by the Instituto de Sistemas Fotovoltaicos de Concentración, the Lineal method and an artificial neural network-based method introduced in this paper. The complete procedures, parameters and coefficients required to estimate the cell temperature with each method are provided. The results show that methods based on direct measurements of the HCPV module perform better than methods based on atmospheric parameters. However, all of the studied methods can be used to estimate cell temperatures with an acceptable margin of error.