Comparative optimization of global solar radiation forecasting using machine learning and time series models

Environ Sci Pollut Res Int. 2022 Feb;29(10):14871-14888. doi: 10.1007/s11356-021-16760-8. Epub 2021 Oct 8.


The increasing use of solar energy as a source of renewable energy has led to increasing the interest in photovoltaic (PV) power outputs forecasting. In the meantime, forecasting global solar radiation (GSR) depends heavily on weather conditions, which fluctuate over time. In this paper, an algorithm method is proposed, to choose the optimum machine learning techniques and time series models which minimizing the forecasting errors. The forecasted GSR belongs to the Faculty of Sciences, Abdelmake Eassadi University, Tetouan, Morocco. The selected machine learning and times series are Autoregressive Integrated Moving Average (ARIMA), Feed Forward Neural Network with Back Propagation (FFNN-BP), k-Nearest Neighbour (k-NN), and Support Vector Machine (SVM) compared with persistence model as the reference model. To compare the results, several statistical metrics are calculated to evaluate the performance of each method. The obtained results indicated that the used machine learning and time series methods were more straightforward to implement. In particular, we find that the Feedforward neural network (FFNN) and ARIMA perform better and give good approximations with the corresponding GSR output.

Keywords: Forecasting; Global solar radiation outputs; Machine learning; Solar energy; Time series.

MeSH terms

  • Forecasting
  • Machine Learning*
  • Neural Networks, Computer
  • Solar Energy*
  • Time Factors