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

Abstract

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 how shading and soiling can be integrated into machine learning 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 predicted values is performed. This study is able to show that the forecast error could be reduced in the same way as the loss due to shading and soiling using various machine learning methods. The results were compared against a Physical Informed Neural Network (PINN), which outperformed classical machine learning methods both with and without shading and soiling.