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%

The main objective of this paper is to review the state of the art of residential PV systems in France. This is done analyzing the operational data of 6868 installations. Three main questions are posed. How much energy do they produce? What level of performance is associated to their production? Which are the key parameters that most influence their quality? During the year 2010, the PV systems in France have produced a mean annual energy of 1163 kWh/kWp. As a whole, the orientation of PV generators causes energy productions to be some 7% inferior to optimally oriented PV systems. The mean Performance Ratio is 76% and the mean Performance Index is 85%. That is to say, the energy produced by a typical PV system in France is 15% inferior to the energy produced by a very high quality PV system. On average, the real power of the PV modules falls 4.9% below its corresponding nominal power announced on the manufacturer's datasheet. A brief analysis by PV modules technology has led to relevant observations about two technologies in particular. On the one hand, the PV systems equipped with heterojunction with intrinsic thin layer (HIT) modules show performances higher than average. On the other hand, the systems equipped with the copper indium (di)selenide (CIS) modules show a real power that is 16% lower than their nominal value

This paper presents a review of back‐tracking geometry not only for single axis but also for two‐axis tracking and analyses the corresponding energy gains. It compares the different back‐tracking strategies with the ideal tracking in terms of energy yield concluding, on the one hand, that back‐tracking is more useful for single horizontal axis than for the single vertical one, and on the other hand, that back‐tracking is more efficient when applied in the primary axis of a two‐axis tracker. Copyright © 2011 John Wiley & Sons, Ltd.

Energy yield and occupation of land are two parameters that must be optimized when designing a large PV plant. This paper presents the results of simulating the energy yield of flat panels for some locations and different tracking strategies as a function of the ground cover ratio. Some interesting results for design purposes, such as the optimal solar trackers depending on the land availability or the energy gains of every tracking strategy, are shown. For example, the energy gains associated to one north–south axis tracking, referenced to static surfaces, ranges from 18 to 25%, and from 37 to 45% for the two‐axes tracker for reasonable ground cover ratios. To achieve these results, a simulation tool with appropriate models to calculate the energy yield for different solar trackers has been developed. Copyright © 2008 John Wiley & Sons, Ltd.