Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quantile Regression Forests as machine learning tool to forecast AC power with a confidence interval. Real data from five PV plants was used to validate the methodology, and results show that daily production is predicted with an absolute cvMBE lower than 1.3%

In recent decades, trends in photovoltaic (PV) technology deployment have shown an overall increase across the world. Comprehensive knowledge of the solar resource and its future evolution is demanded by the energy sector. Solar resource and PV potential have been estimated in several studies using both the global climate model (GCM) and regional climate model (RCM), revealing a GCM–RCM discrepancy in the projected change over Europe. An increase in surface solar radiation (SSR) (and therefore in PV potential production) is projected by GCMs, whereas most RCM simulations project a decrease in SSR over Europe. In this work, we investigate the role of aerosol forcing in RCMs as a key explaining factor of this inconsistency. The results show that RCM simulations including evolving aerosols agree with GCMs in the sign and amplitude of the SSR change over Europe for mid-21st century projections (2021–2050 compared to 1971–2000 for representative concentration pathway climate change scenario RCP8.5). The opposite signal is projected by the rest of the RCMs. The amplitude of the changes likely depends on the RCM and on its aerosol forcing choice. In terms of PV potential, RCMs including evolving aerosols simulate an increase, especially in summer for Central and Eastern Europe, with maximum values reaching +10% in some cases. This study illustrates the key role of the often-neglected aerosol forcing evolution in RCMs. It also suggests that it is important to be very careful when using the multi-model Coordinated Regional Climate Downscaling Experiment (CORDEX) projections for solar radiation and related variables, and argues for the inclusion of aerosol forcing evolution in the next generation of CORDEX simulations.

This study develops a proposal of method of calculation useful to estimate the energy produced by a PV grid‐connected system making use of irradiance‐domain integrals and definition of statistical moment. Validation against database of real PV plants performance data shows that acceptable energy estimation can be obtained with first to fourth statistical moments and some basic system parameters. This way, only simple calculations at the reach of pocket calculators, are enough to estimate AC energy. Copyright © 2006 John Wiley & Sons, Ltd.

A review of existing studies about life cycle assessment (LCA) of PV systems has been carried out. The data from this review have been completed with our own figures in order to calculate the energy payback time (EPBT) of double and horizontal axis tracking and fixed systems. The results of this metric span from 2 to 5 years for the latitude and global irradiation ranges of the geographical area comprised between –10° to 10° of longitude, and 30° to 45° of latitude. With the caution due to the uncertainty of the sources of information, these results mean that a grid connected PV system (GCPVS) is able to produce back the energy required for its existence from 6 to 15 times during a life cycle of 30 years. When comparing tracking and fixed systems, the great importance of the PV generator makes advisable to dedicate more energy to some components of the system in order to increase the productivity and to obtain a higher performance of the component with the highest energy requirement. Both double axis and horizontal axis trackers follow this way, requiring more energy in metallic structure, foundations and wiring, but this higher contribution is widely compensated by the improved productivity of the system. Copyright © 2008 John Wiley & Sons, Ltd.

This paper presents a new methodology to build parametric models to estimate global solar\ud irradiation adjusted to specific on-site characteristics based on the evaluation of variable im-\ud portance. Thus, those variables higly correlated to solar irradiation on a site are implemented\ud in the model and therefore, different models might be proposed under different climates. This\ud methodology is applied in a study case in La Rioja region (northern Spain). A new model is\ud proposed and evaluated on stability and accuracy against a review of twenty-two already exist-\ud ing parametric models based on temperatures and rainfall in seventeen meteorological stations\ud in La Rioja. The methodology of model evaluation is based on bootstrapping, which leads to\ud achieve a high level of confidence in model calibration and validation from short time series (in\ud this case five years, from 2007 to 2011).\ud The model proposed improves the estimates of the other twenty-two models with average\ud mean absolute error (MAE) of 2.195 MJ/m2 day and average confidence interval width (95%\ud C.I., n=100) of 0.261 MJ/m2 day. 41.65% of the daily residuals in the case of SIAR and 20.12% in\ud that of SOS Rioja fall within the uncertainty tolerance of the pyranometers of the two networks\ud (10% and 5%, respectively). Relative differences between measured and estimated irradiation\ud on an annual cumulative basis are below 4.82%. Thus, the proposed model might be useful\ud to estimate annual sums of global solar irradiation, reaching insignificant differences between\ud measurements from pyranometers