Hybrid methodology for hourly global radiation forecasting in Mediterranean area

TL;DR

This paper introduces hybrid models combining ANN and ARMA to improve hourly global radiation forecasting in the Mediterranean, achieving over 1% better accuracy than individual models.

Contribution

It presents novel hybrid modeling techniques that adapt to seasonal variations, enhancing the accuracy of solar radiation predictions in Mediterranean regions.

Findings

Hybrid models outperform individual ANN or ARMA models by over 1% in accuracy.

Seasonal and error-based hybrid models improve forecasting during different times of the year.

Maximum improvement observed in autumn, minimum in winter.

Abstract

The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams. This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving Average (ARMA) model. While ANN by its non-linear nature is effective to predict cloudy days, ARMA techniques are more dedicated to sunny days without cloud occurrences. Thus, three hybrids models are suggested: the first proposes simply to use ARMA for 6 months in spring and summer and to use an optimized ANN for the other part of the year; the second model is equivalent to the first but with a seasonal learning; the last model depends on the error occurred the previous hour. These models were used to forecast the hourly global radiation for five places in Mediterranean area. The forecasting performance was compared among several models: the 3 above mentioned models, the best ANN and ARMA for each location. In the best configuration, the coupling of ANN and ARMA allows an improvement of more than 1%, with a maximum in autumn (3.4%) and a minimum in winter (0.9%) where ANN alone is the best.