A Hybrid Numerical Method and Comparative Study for Solar Irradiance Forecasting

Jayesh Lakshmidas Thaker, Robert Höller

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review


Renewable energy sources like solar energy and wind power, which are not available on-demand must be stored or used instantly. The solar irradiance is strongly dependent on unpredictable atmospheric conditions. Thus, the performance and the accuracy of solar forecasting model is highly influenced by the complex and stochastic atmospheric conditions. The forecasting challenges of solar energy augment with increasing deployment of photovoltaic (PV) system into electrical grids. As the global horizontal irradiance (GHI) is the most important key input in PV power prediction systems, increasing effort is spent on research on forecasting of GHI as a basis for corresponding power forecasts.
The proposed approach in this paper is based on forecast of the global numerical weather prediction (NWP) model Global Forecast System (GFS). The proposed model uses day-type classification benchmark based on hourly
vectors generated using clear sky index and hybrid numerical formulation. A novel GHI prediction system using input data from an GFS and two levels ensemble is presented and evaluated by providing day ahead forecasts with an hourly time resolution. The methodology applies various machine learning (ML) and recurrent neural network (RNN) models along with a parametric model as the basis to build an ensemble model. The focus of this paper is the comparison of the capability and performance of developed models and build a hybrid ensemble model for producing reliable day-ahead forecast horizon and an hourly resolution of GHI. The detailed study and analysis of probabilistic time series forecast is presented. The reliability and the sharpness of the forecast
models is evaluated by using various skill scores of probabilistic forecasts.
Results show that the hourly irradiance classification approach reduces the forecasting error by a considerable margin and the optimized hybrid ensemble odel outperforms all other models. These findings also emphasize that all the models perform better than GFS.
Original languageEnglish
Title of host publication8th World Conference on Photovoltaic Energy Conversion (WCPEC-8)
Subtitle of host publicationProceedings of the International Conference
Publication statusPublished - 9 Dec 2022


  • Solar irradiance forecasting
  • Photovoltaics
  • Numerical weather prediction
  • Machine learning
  • Deep learning
  • ensemble models
  • weather classification
  • clear sky index
  • global horizontal irradiation
  • model output statistics


Dive into the research topics of 'A Hybrid Numerical Method and Comparative Study for Solar Irradiance Forecasting'. Together they form a unique fingerprint.

Cite this