Research Article: Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction

Date Published: May 31, 2019

Publisher: Public Library of Science

Author(s): Hamidreza Ghazvinian, Sayed-Farhad Mousavi, Hojat Karami, Saeed Farzin, Mohammad Ehteram, Md Shabbir Hossain, Chow Ming Fai, Huzaifa Bin Hashim, Vijay P. Singh, Faizah Che Ros, Ali Najah Ahmed, Haitham Abdulmohsin Afan, Sai Hin Lai, Ahmed El-Shafie, Yang Li.

http://doi.org/10.1371/journal.pone.0217634

Abstract

Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS.

Partial Text

Solar energy is one of the most important forms of energy. Although fossil fuels can produce a large amount of energy, they cause various kinds of pollution [1,2]. Undoubtedly the knowledge of solar radiation is important as it has direct or indirect impact on the current and future life [3]. This energy affects the agriculture, industry engineering, health and the tourism sector of any nation [2].

The present study deals with computing solar radiation in a Mediterranean region of Turkey. The Adana and Antakya stations are considered (Fig 3). The major climatic features of this region are inclination towards rainy winters and hot summers. The Adana station is located at latitude 37.22°N, longitude 35.40°E, and an altitude of 20 m; Antakya is located at latitude 36.22°N, longitude 35.40°E, and an altitude of 20 m. The climatic conditions of the region are affected by a winter season with high rainfall as well as hot summers. The SR distribution shows that the region has high solar energy potential and Turkey has high potential for solar energy because it is in the northern hemisphere. The most value for solar radiation for two stations are observed in July. The annual solar radiation for Antakya is 10.89 MJ/m2/day and it is 12.23 MJ/m2/day for Adana station.

Evolutionary algorithms, such as IPSO, have parameters whose best values can be reported based on a literature review or experimentation. We set an interval for the random parameters and evaluate the variations in the objective function for various values of the parameters. Then, the best values of the parameters (c1 and c2 = 2, w = 0.6 and population size for particle swarm = 40) are selected when the objective function converges to its minimum value [38,39,46]. A sensitivity analysis is considered for determining the most suitable parameters and the variation of objective function values is observed accordingly. In this regard, the least objective function value was preferred.

The present study introduced a new prediction model for SR. The model is essentially based on an improved SVR integrated withI PSO;I PSO determines the optimum values of the unknown SVR parameters. The proposed model was applied to two stations from Turkey for evaluation against the previously developed SVR-PSO, MARS, GP and M5T models, which have been applied to the same stations. Based on the proposed performance indicators, increasing the number of inputs improved the results of the SVR-IPSO model. In addition, the application of SVR-IPSO to the Antakya station showed the superiority of SVR-IPSO over the other models. The proposed SVR-IPSO models for the two stations achieved better performance than the MARS, GP, SVR-PSO and M5T models for different input scenarios. Furthermore, an additional input variable representing the month of the year resulted in improvements over previous input scenarios. In conclusion, the proposed SVR integrated with IPSO (SVR-IPSO) can be considered an effective tool for solar radiation prediction that could help decision-makers create efficient plans for renewable energy production. A few important variables were lacking in the selected stations and hence could not be examined in this study. Also, the SVR-IPSO, was validated for Konya station and the results were compared with the SVR-GA, SVR-PSO and SVR-FFA. The results showed that the SVR-IPSO model has best performance comparing with all the presented models.

 

Source:

http://doi.org/10.1371/journal.pone.0217634

 

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