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

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

doi: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.

Highlights

Solar energy is one of the most important forms of energy
The results indicated that the support vector regression (SVR)-improved particle swarm optimization (IPSO) has the less Root Mean Square Error (RMSE) and Mean absolute error (MAE) value and the fourth combination for SVR-IPSO is better compared to the SVR-genetic algorithm (GA), SVR-PSO and SVR-firefly algorithm (FFA)
The proposed model was applied to two stations from Turkey for evaluation against the previously developed SVR-PSO, multivariate adaptive regression (MARS), genetic programming (GP) and M5T models, which have been applied to the same stations

Summary

Introduction

Solar energy is one of the most important forms of energy. fossil fuels can produce a large amount of energy, they cause various kinds of pollution [1,2]. 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 ANN model was compared with SVR, and it was found that multi-layer perceptron neural networks could achieve better RMSE than could SVR and other kinds of ANNs. Latitude, longitude, monthly minimum and maximum temperatures, and relative humidity were used as inputs. Khatib et al [14] compared different methods of computing SR and showed that regression methods had drawbacks in terms of identifying the unknown parameters and that artificial intelligence methods generally outperformed them. SR has been estimated by a hidden Markov model and a generalized fuzzy model [16]

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Results

Conclusion