Multi-horizon solar radiation forecasting for Mediterranean locations using time series models

TL;DR

This paper compares various time series models for short- and medium-term solar radiation forecasting in the Mediterranean, highlighting the effectiveness of MLPs with exogenous variables and hybrid approaches.

Contribution

It introduces a comprehensive comparison of predictors for different horizons and proposes innovative combinations of models and preprocessing techniques for improved accuracy.

Findings

MLP with exogenous variables performs well at h+24 and m+5 horizons.

Hybrid ARMA-MLP models enhance prediction accuracy.

Stationarity and weather data integration improve model performance.

Abstract

Considering the grid manager's point of view, needs in terms of prediction of intermittent energy like the photovoltaic resource can be distinguished according to the considered horizon: following days (d+1, d+2 and d+3), next day by hourly step (h+24), next hour (h+1) and next few minutes (m+5 e.g.). Through this work, we have identified methodologies using time series models for the prediction horizon of global radiation and photovoltaic power. What we present here is a comparison of different predictors developed and tested to propose a hierarchy. For horizons d+1 and h+1, without advanced ad hoc time series pre-processing (stationarity) we find it is not easy to differentiate between autoregressive moving average (ARMA) and multilayer perceptron (MLP). However we observed that using exogenous variables improves significantly the results for MLP . We have shown that the MLP were more adapted for horizons h+24 and m+5. In summary, our results are complementary and improve the existing prediction techniques with innovative tools: stationarity, numerical weather prediction combination, MLP and ARMA hybridization, multivariate analysis, time index, etc.