Inclusion of Shading and Soiling With Physical and Data-Driven Algorithms for Solar Power Forecasting

Shading and soiling are the biggest environmental factors that negatively affect the yield of PV systems. In order to integrate PV systems into the grid as easily and on large scale as possible, it is important that energy generation forecasts are as accurate as possible. The scope of this paper is to present a method to integrateshading and soiling into machine learn-ing based PV forecasts,even if they have already been pre-trained by a large dataset. This paper focuses on shading by buildings, trees, obstacles, while shading by clouds can only be considered to a limited extent by weather forecasts. This study uses a dataset of three years of training data to build a base model. Subsequently, the power loss due to shading and soiling is determined using a digital twin and used to correct the forecast values of the baseline model. Finally, an evaluation of the corrected and original predictedvalues is performed. This shows that the forecast error canbe reduced in the same way as the loss due to shading and soiling using various machine learning methods. The results arecompared against a Physics-In-formed Neural Network (PINN), which outperformed popularmachine learning methods both with and without shading and soilingby 6.6%.