Machine Learning and Weather Model Combination for PV Production Forecasting

Abstract

Accurate predictions of photovoltaic generation are essential for effectively managing power system resources, particularly in the face of high variability in solar radiation. This is especially crucial in microgrids and grids, where the proper operation of generation, load, and storage resources is necessary to avoid grid imbalance conditions. Therefore, the availability of reliable prediction models is of utmost importance. Authors address this issue investigating the potential benefits of a machine learning approach in combination with photovoltaic power forecasts generated using weather models. Several machine learning methods have been tested for the combined approach (linear model, Long Short-Term Memory, eXtreme Gradient Boosting, and the Light Gradient Boosting Machine). Among them, the linear models were demonstrated to be the most effective with at least an RMSE improvement of 3.7% in photovoltaic production forecasting, with respect to two numerical weather prediction based baseline methods. The conducted analysis shows how machine learning models can be used to refine the prediction of an already established PV generation forecast model and highlights the efficacy of linear models, even in a low-data regime as in the case of recently established plants.

A Survey on Anomalies and Faults that may Impact the Reliability of Renewable-based Power Systems

“…Since PV systems are located outdoors, they frequently encounter environmental stress as high temperatures, rain, snow, and the PV system does not operate under Standard Test Conditions (STCs), thus failing to achieve their nominal power. Since variations in solar irradiation directly impact the power generation of PV systems [20], with the consequent uncertainties that must be carefully considered [21], certain areas of PV arrays could yield higher power output compared to others (mismatch) due to nonuniform shading from physical obstructions like trees, buildings, and overhead power lines, etc. [22,23].…”