Short-term solar radiation forecasting using a new seasonal clustering technique and artificial neural network

ABSTRACT

Solar radiation represents the most important parameter for sizing and planning solar power systems. However, solar radiation depends significantly on meteorological conditions which are variable and uncontrollable. Therefore, forecasting global solar radiation can play a key role to integrate solar energy resources into the electric grid. This paper presents a new hybrid approach based on seasonal clustering technique and artificial neural network (ANN) for forecasting 1 h-ahead of global solar radiation. For this purpose, the fuzzy c-means algorithm (FCM) was used to cluster 3 years of monthly average experimental data into different seasons according to solar and meteorological parameters of Évora city. Subsequently, based on the seasonal clustering results, the meteorological dataset was divided into dfferent training subsets. Furthermore, for each subset, an ANN model has been designed to forecast hourly global solar radiation. In this study, hourly meteorological data from January 2012 to December 2016 have been used for forecasting. The hourly data were collected from Évora-city’s meteorological station in Portugal (38°34 N, 07°54 W). The results show the superiority of the hybrid approach compared to the individual ANN model according to statistical indicators.

KEYWORDS:

• Forecasting
• solar radiation
• machine learning
• artificial neural network
• clustering
• fuzzy c-means

Biographical note

Hamza Ali-Ou-Salah, received the B.Sc. degree in Electronics and Computer Science and the M.Sc. degree in information processing from Hassan II University of Casablanca, Morocco, in 2013 and 2015 respectively. He started his Ph.D. in renewable energy management at Hassan II University of Casablanca in 2016. His research interests are renewable energy systems, artificial intelligence and their application in renewable energy management.

Benyounes Oukarfi, received the B.Sc. and M.Sc. degrees in Electrical and Electronics Engineering from the University of Nantes, France, in 1985 and 1986, respectively. He received the D.E.A and Ph.D. degrees in Electronics from the Blaise Pascal university, France, in 1987 and 1992, respectively. He received the Engineer’s degree in Computer Science from the University of Claude Bernard, France, in 1995. He is currently a Full Professor at Hassan II University of Casablanca, Morocco. His research interests include photovoltaic system optimization, smart grid management, artificial intelligence in their application in renewable energy.

Mouhaydine Tlemçani, received the M.Sc. degree in electrical engineering from Slovak Technical University, Bratislava, Slovak Republic, in 1992 and the Ph.D. degree from the Universidade de Évora, Évora, Portugal, in 2007. He is currently an Assistant Professor in the Department of Mechatronics Engineering, Universidade de Évora. He is also a Full Member of the Assembly of School of Science and Technology, Évora, Portugal. His current research interests include photovoltaic systems, electrical measurements, signal processing, and nonlinear dynamics.

Acknowledgments

Monthly data used for clustering were obtained from the NASA Langley Research Center Atmospheric Science Data Center, Surface Meteorological and Solar Energy (SSE) web portal supported by the NASA LaRC POWER Project.

Disclosure Statement

No potential competing interest was reported by the authors.

Data availability statement

The solar radiation data that support the findings of this study are available from the Institute of Earth Sciences. Restrictions apply to the availability of these data, which were used under license for this study. Data is available from the authors with the permission of the Institute of Earth Sciences.

Monthly data that support the findings of this study are openly available at https://power.larc.nasa.gov

Additional information

Funding

This work was supported by the [National Centre for Scientific and Technical Research, Morocco] under Grant [number 4UH2C2017].