Forecasting Hourly Solar Radiation using a Novel Hybrid Technique based on Machine Learning Models

Hamza Ali Ou Salah, Benyounes Oukarfi, Khalid Bahani, Mohammed Ramdani, Mohammed Moujabbir

2021

Abstract

Photovoltaic production is highly dependent on solar radiation time series, which is sporadic. Grid operators have a significant problem integrating photovoltaic energy sources into the electrical grid due to the unpredictability of solar radiation. To overcome this, forecasting global solar radiation can solve the intermittency due to the variability of weather conditions. It allows the grid operators to predict photovoltaic power production to facilitate the planning and dispatching tasks of the electric grid. In this work, we have proposed a new hybrid method to predict one-hour solar radiation in Évora city (Portugal). The hybrid model is based on the daily classification of global solar radiation and machine learning algorithms such as support vector machines (SVM) and artificial neural network (ANN). We have collected five years of global horizontal solar radiation data from the meteorological station of Évora city. We have evaluated the performance of the proposed model using normalized root mean square error (nRMSE) and normalized mean absolute error (nMAE). The results show that, for sunny days, the SVM model performs better than the ANN model with nRMSE = 9.15 % and nMAE = 4.65%, while for cloudy days, the ANN model gives better results than the SVM model with nRMSE= 42.09 % and nMAE = 25.1%. Moreover, we have carried out a performance comparison with the recent literature. The results show the superiority of the proposed hybrid model compared to literature’s models.

Paper Citation

in Harvard Style

Ali Ou Salah H., Oukarfi B., Bahani K., Ramdani M. and Moujabbir M. (2021). Forecasting Hourly Solar Radiation using a Novel Hybrid Technique based on Machine Learning Models. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 135-142. DOI: 10.5220/0010729800003101

in Bibtex Style

@conference{bml21,
author={Hamza Ali Ou Salah and Benyounes Oukarfi and Khalid Bahani and Mohammed Ramdani and Mohammed Moujabbir},
title={Forecasting Hourly Solar Radiation using a Novel Hybrid Technique based on Machine Learning Models},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={135-142},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010729800003101},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - Forecasting Hourly Solar Radiation using a Novel Hybrid Technique based on Machine Learning Models
SN - 978-989-758-559-3
AU - Ali Ou Salah H.
AU - Oukarfi B.
AU - Bahani K.
AU - Ramdani M.
AU - Moujabbir M.
PY - 2021
SP - 135
EP - 142
DO - 10.5220/0010729800003101