An empirical approach to parameterizing photovoltaic plants for power forecasting and simulation

Abstract

The aim of this work is to develop an algorithm that can utilize historical PV power measurements to establish the parameters of a physical model for power production. The chosen approach consists in evaluating the parameters of a PV model that maximize the likelihood that simulations match with power measurements. The proposed method offers advantages beyond the standard approaches used for the simulation or prediction of PV power production, as it makes maxinnun use of the information typically available on a PV plant (plant description and measurement history). Furthermore, an interpretation and control of the algorithm output is made possible. The performance of the proposed approach has been evaluated and analyzed using measurements from two PV plants, It is shown that the proposed approach may identify the orientation angles of a PV module to within an accuracy of less than 2 degrees in optimal cases, Situations were also found with a difference between the estimated and actual angles of 5 degrees, for which the estimated parameters lead to better simulation/forecast accuracy than the actual ones as they balance the systematic error of the chosen PV-model

Elman-Based Forecaster Integrated by Adaboost Algorithm in 15 min and 24 h ahead Power Output Prediction Using PM 2.5 Values, PV Module Temperature, Hours of Sunshine, and Meteorological Data

“…The physical method is based on the geographical information of the photovoltaic power station, detailed meteorological data, and the operation equation of the PV module [13]. Saint-Drenan et al [14] developed a physical method that can employ historical PV power data to found the parameters equation of a physical model for power output. On the one hand, the modeling process is relatively complex.…”

“…calculate the error of base learner by Formula (set, output the final hypotheses by Formula (14)(14) …”

An approach for the estimation of the aggregated photovoltaic power generated in several European countries from meteorological data

“…Though more practical than the classical approaches, these statistical approaches are also time consuming because of the handling of the data and they cannot be applied in regions where no training data is available. Another common practice consists in estimating the total power generated in a region by upscaling the power generated by a subset of reference plants (Schierenbeck et al, 2010;Lorenz and Heinemann, 2012;Shaker et al, 2015Shaker et al, , 2016Saint-Drenan et al, 2016;Bright et al, 2017;Pierro et al, 2017;Killinger et al, 2017). The major obstacles to this practice is on the one hand the establishment of criteria on the selection of the plants that are statistically representative of the region and their number, and on the other hand, the access to measurements of the selected plants.…”

An unsupervised method for identifying local PV shading based on AC power and regional irradiance data

“…• Researchers commonly face the task of analyzing the performance of a large number of PV sites (Killinger et al, 2017a(Killinger et al, , 2017bSaint-Drenan et al, 2015) to gain insight into the performance of the systems in real life conditions. This requires irradiance data for the investigated period which can come from either satellite measurements, or nearby meteorological stations.…”