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8th World Conference on Photovoltaic Energy Conversion; 748-749, This study aims at finding the impact of randomness in manufacturing on the uncertainty of power production by a photovoltaic (PV) module. For this purpose, the module efficiency at the reference condition (ref) is chosen as the parameter that describes the manufacturing randomness, and using the experimental data, a statistical distribution for that is found. Then, Monte Carlo approach, as a systematic way, is utilized, and the statistical distribution for produced power of the PV module is found. A module from poly crystalline type, with the rated capacity of 120 W, is selected, and the analysis is carried out for that as the case study. The fitting based on the experimental data for 114 PV modules is demonstrated that a normal distribution with the average of 15.8% and standard deviation of 0.498% is the best statistical distribution for ref. Moreover, according to the obtained results, due to ref randomness, power production varies in the range of 97 W to 113 W. It means 16 W uncertainty in the power production, which is 13.3% of the nominal capacity.

The research, carried out in collaboration between the ESTER lab of the University of Tor Vergata (network 9 of the DTC) and the BHiLab of the CNR (network 10 of the DTC) is the development of a methodology for environmental monitoring and thermographic and thermofluximetric analyses aimed at to the development of intervention scenarios to improve the energy and environmental performance of prestigious historic buildings, through an approach based on simulation. The studies were performed on Palazzo Maffei Borghese, the Italian case study of the European project ENICBCMed BEEP, coordinated by BHiLAB.

In G. Pernigotto, F. Patuzzi, A. Prada, V. Corrado, & A. Gasparella (Eds.), Building Simulation Applications BSA 2019 (pp. 149���156).

In G. Pernigotto, F. Patuzzi, A. Prada, V. Corrado, & A. Gasparella (Eds.), Building Simulation Applications BSA 2017 (pp. 159–166).

33rd European Photovoltaic Solar Energy Conference and Exhibition; 1576-1583, The aim of this work is to improve knowledge about the observed seasonal behavior of CdTe modules under real operating conditions. The study consists of a characterization of the CdTe photovoltaic (PV) technology based on the application of the Sandia Array Performance Model (SAPM) to two PV fields in Italy (Viterbo and Bolzano) and two PV test modules in Spain (Madrid and Jaén). SAPM is a semi-empirical power estimation model that takes into account the four main phenomena that affect the Performance Ratio (PR) of PV plants or modules: temperature, angle of incidence, irradiance and spectral effects. New outdoor experimental procedures were set up at the ESTER station of University of Rome Tor Vergata to validate the temperature coefficients, the angle of incidence coefficients and the spectral mismatch coefficients of CdTe modules to be used in the SAPM model implementation. A detailed analysis of the deviation between the experimental PR data and the modeled PR estimated by SAPM reveals that it is possible to characterize the residuals and attribute them to phenomena not considered by the model.

33rd European Photovoltaic Solar Energy Conference and Exhibition; 2485-2491, On regional scale, the estimation of the solar power generation from the environmental conditions and the solar power forecast is essential for Distribution System Operators, Transmission System Operator, energy traders, and aggregators. Indeed, high photovoltaic penetration results in a stochastic variability of the electric demand that could compromise the stability of the grid, increase the amount of energy reserve and the energy imbalance cost. In this context, two upscaling methods and several models were developed and used for estimation and forecast of the photovoltaic distributed generation in a small area of Italy with high photovoltaic penetration. The upscaling methods are based on chains of data-driven models (k-mean clustering, principal component analysis and ensemble of neural networks) that use as inputs satellite derived irradiance and numerical weather prediction. These models allow a complete assessment of the accuracy of the regional power forecast at different horizons, from 0 hour ahead (power estimation) to 48 hours ahead. The power estimation model provided a RMSE of 3% of installed capacity. Intra-day forecast (from 1 to 4 hours) obtained a RMSE of 5% - 7% and a skill score with respect to the clear sky persistence from -8% to 33.6%. The one and two days ahead forecast achieved a RMSE of 7% and 7.5% and a skill score of 39.2% and 45.7%. The ensemble smoothing effect on cluster scale was also studied. It was proved that the RMSE reduction achieved on cluster scale due to the ensemble smoothing follows the same exponential law found for single systems. In this case the smoothing effect reduces the RMSE of day-ahead forecast of 12% with respect to the mean single cluster value. It was also shown that the skill score increases linearly with the size of the region with a rate of 0.1% for km. Furthermore, a method to estimate the forecast error was also developed. It was based on an ensemble neural network model coupled with a probabilistic correction. It can provide a highly reliable computation of the prediction intervals.

33rd European Photovoltaic Solar Energy Conference and Exhibition; 1904-1908, Information concerning the performance of photovoltaic (PV) plants in a region is of fundamental importance for many stakeholders of the sector. Nevertheless, often the lack of meteorological measurements – irradiance over all – prevent from correctly assessing the current health status of PV plants, i.e. the presence of ongoing mechanisms that gradually cause a performance degradation higher than expected and warranted. This study presents a methodology to infer statistical information on the performance loss rate (PLR) of a PV fleet in a region, when only few characteristics of PV plants (e.g. final yield, location, nominal power) are known. It makes use of satellite-retrieved values of global horizontal and diffuse horizontal irradiance, downloaded freely and only for an optimized number of representative locations. A fleet of 1899 PV plants located in South Tyrol (North-East of Italy) is used as a case study to validate the methodology, and results and possible applications are presented. In general, the presented methodology looks promising in extracting statistical information from a fleet of PV plants distributed in a region, and in inferring the probabilistic performance of future plants installed in the same area. Issues concerning the quality of the input data, related especially to the irradiance and the local skyline, are identified and discussed for a further improvement of the methodology.

In G. Pernigotto, F. Patuzzi, A. Prada, V. Corrado, & A. Gasparella (Eds.), Building Simulation Applications BSA 2017 (pp. 185–192).

32nd European Photovoltaic Solar Energy Conference and Exhibition; 1475-1481, This work aims to compare several data-driven models using different Numerical Weather Prediction (NWP) input data and then to build up an outperforming Multi-Model Ensemble (MME) and its prediction intervals. Statistic, stochastic and hybrid machine-learning algorithms were developed and the NWP data from IFS and WRF models were used as input. It was found that the same machine learning algorithm differs in performance even if it make use of NWP data with comparable accuracy. This apparent inconsistency depends on the capability of the machine learning model to correct the bias error of the input data. The stochastic and the hybrid model using the same WRF input, as well as the stochastic and the non-linear statistic models using the same IFS input, produce very similar results. The MME resulting from the averaging of the best data-driven forecasts, improves the accuracy of the outperforming member of the ensemble, bringing the skill score from 42% to 46%. To reach this performance, the ensemble should include forecasts with similar accuracy but generated with the higher variety of different data-driven technology and NWP input.

24th European Photovoltaic Solar Energy Conference, 21-25 September 2009, Hamburg, Germany; 97-100, The Centre for Hybrid and Organic Solar Energy (CHOSE) at the University of Rome Tor Vergata is focusing its efforts in developing reliable and stable dye solar cells and modules. The CHOSE development program foresees that new cells and modules will be tested both indoor and outdoor in order to have an overview of the real performances of the devices. This is generally a key point for a correct evaluation of the actual performances of any photovoltaic technology. In October 2008 an outdoor measurement campaign on Dye Solar Cells has been started. Two different types of cells have been built in the laboratory, varying the characteristics of the TiO2 film, in order to evaluate their behaviour in real outdoor environment. Data collected during the two first month of outdoor cell exposure are presented in this paper. Daily trends of cell efficiency as a function of the environmental parameters and of the cell temperature have been investigated, focusing on the cell behaviour at low irradiance levels.

24th European Photovoltaic Solar Energy Conference, 21-25 September 2009, Hamburg, Germany; 3586-3589, In the paper the uncertainty analysis of the FTA Lab ESTER facility instrumentation is presented together with the procedure used to validate data outputs through measurements on a reference polycrystalline module provided by ISAAC-SUPSI. The reference module has been tested for several months and a procedure has been identified to sort the IV curves for translation at STC conditions. Blaesser method has been implemented and comparison between STC data from indoor measurements by ISAAC and translated outdoor curves produced quite satisfactory results.